U.S. patent application number 13/599333 was filed with the patent office on 2012-12-20 for coder optimization using independent bitstream partitions and mixed mode entropy coding.
This patent application is currently assigned to GOOGLE INC.. Invention is credited to Eric Ameres, James Bankoski, Adrian Grange, Scott Lavarnway, Dan Miller, Paul Wilkins, Yaowu Xu.
Application Number | 20120320978 13/599333 |
Document ID | / |
Family ID | 33423828 |
Filed Date | 2012-12-20 |
United States Patent
Application |
20120320978 |
Kind Code |
A1 |
Ameres; Eric ; et
al. |
December 20, 2012 |
CODER OPTIMIZATION USING INDEPENDENT BITSTREAM PARTITIONS AND MIXED
MODE ENTROPY CODING
Abstract
A system, apparatus, and method for encoding a video signal
having a plurality of frames including a current frame. The method
includes identifying a first block within the current frame;
determining at least one predictor token related to the first
block; identifying a reference block within the plurality of
frames; determining at least one prediction error token derived
from a difference between the first block and the reference block;
assigning the at least one predictor token to a first bitstream
partition; assigning the at least one prediction error token to a
second bitstream partition; and selecting a first encoding scheme
for the first bitstream partition and a second encoding scheme for
the second bitstream partition.
Inventors: |
Ameres; Eric; (Cohoes,
NY) ; Bankoski; James; (Los Gatos, CA) ;
Lavarnway; Scott; (Warrensburg, NY) ; Xu; Yaowu;
(San Diego, CA) ; Miller; Dan; (New York, NY)
; Grange; Adrian; (Cambridge, GB) ; Wilkins;
Paul; (Cambridge, GB) |
Assignee: |
GOOGLE INC.
Mountain View
CA
|
Family ID: |
33423828 |
Appl. No.: |
13/599333 |
Filed: |
August 30, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10713807 |
Nov 14, 2003 |
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13599333 |
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60469187 |
May 12, 2003 |
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Current U.S.
Class: |
375/240.12 ;
375/E7.243 |
Current CPC
Class: |
H04N 19/114 20141101;
H04N 19/523 20141101; H04N 19/156 20141101; H04N 19/154 20141101;
H04N 19/117 20141101; H04N 19/52 20141101; H04N 19/177 20141101;
H04N 19/139 20141101; H04N 19/172 20141101; H04N 19/14 20141101;
H04N 19/129 20141101; H04N 19/152 20141101; H04N 19/13
20141101 |
Class at
Publication: |
375/240.12 ;
375/E07.243 |
International
Class: |
H04N 7/32 20060101
H04N007/32 |
Claims
1. A method of encoding a video signal having a plurality of frames
including a current frame, comprising: identifying a first block
within the current frame; determining at least one predictor token
related to the first block; identifying a reference block within
the plurality of frames; determining at least one prediction error
token derived from a difference between the first block and the
reference block; assigning the at least one predictor token to a
first bitstream partition; assigning the at least one prediction
error token to a second bitstream partition; and selecting a first
encoding scheme for the first bitstream partition and a second
encoding scheme for the second bitstream partition.
2. The method of claim 1, wherein the first and second encoding
schemes are arithmetic encoding.
3. The method of claim 1, wherein the first encoding scheme is
arithmetic encoding and the second encoding scheme is Huffman
encoding.
4. The method of claim 1, wherein the first block is associated
with a prediction mode and wherein the at least one predictor token
is derived from the prediction mode.
5. The method of claim 1, wherein the at least one predictor token
is derived from a motion vector for the first block and wherein the
motion vector is indicative of a spatial relationship between the
first block and the reference block.
6. The method of claim 1, wherein the reference block is within a
frame other than the current frame.
7. The method of claim 1, further comprising: encoding the first
bitstream partition based on the first encoding scheme and the
second bitstream partition based on the second encoding scheme.
8. The method of claim 7, wherein encoding the first and second
bitstream partitions comprises: using a first processor to encode
the first partition based on the first encoding scheme; and using a
second processor to encode the second partition on based on the
second encoding scheme.
9. The method of claim 8, wherein the first and second bitstream
partitions are encoded in parallel.
10. The method of claim 8, wherein the first processor is a general
purpose subprocessor and the second processor is an entropy
optimized subprocessor.
11. The method of claim 1, wherein selecting the first encoding
scheme and the second encoding scheme comprises: calculating a cost
to encode the first and second bitstream partitions; and selecting
Huffman encoding as the second encoding scheme if the cost to
encode the first and second bitstream partitions exceeds a
predetermined threshold; and selecting arithmetic encoding as the
second encoding scheme if the cost to encode the first and second
bitstream partitions falls below the predetermined threshold.
12. The method of claim 11, wherein the cost for encoding the first
and second bitstream partitions is calculated partially using
Shannon entropy.
13. The method of claim 1, wherein the current frame is associated
with header information, and wherein the header information
includes a signal indicative of the selected first and second
encoding schemes.
14. An apparatus for encoding a video signal having at least one
frame with a plurality of blocks, comprising: a memory; and a
processor configured to execute instructions stored in the memory
to: identify a first block within the current frame; determine at
least one predictor token related to the first block; identify a
reference block within the plurality of frames; determine at least
one prediction error token derived from a difference between the
first block and the reference block; assign the at least one
predictor token to a first bitstream partition; assign the at least
one prediction error token to a second bitstream partition; and
select a first encoding scheme for the first bitstream partition
and a second encoding scheme for the second bitstream
partition.
15. The apparatus of claim 14, wherein the instructions to select
the first and second encoding schemes comprise instructions to:
calculate a cost to encode the first and second bitstream
partitions; select Huffman encoding as the second encoding scheme
if the cost to encode the first and second bitstream partitions
exceeds a predetermined threshold; and select arithmetic encoding
as the second encoding scheme if the cost to encode the first and
second bitstream partitions falls below the predetermined
threshold.
16. The apparatus of claim 14, wherein the instructions further
comprise instructions to: encode the first bitstream partition
based on the first encoding scheme and the second bitstream
partition based on the second encoding scheme.
17. The apparatus of claim 16, wherein the instructions to encode
the first and second bitstream partitions comprise instructions to:
use a first processor to encode the first partition based on the
first encoding scheme; and use a second processor to encode the
second partition based on the second encoding scheme.
18. An apparatus for encoding a video signal having at least one
frame with a plurality of blocks, comprising: means for identifying
a first block within the current frame; means for determining at
least one predictor token related to the first block; means for
identifying a reference block within the plurality of frames; means
for determining at least one prediction error token derived from a
difference between the first block and the reference block; means
for assigning the at least one predictor token to a first bitstream
partition; means for assigning the at least one prediction error
token to a second bitstream partition; and means for selecting a
first encoding scheme for the first bitstream partition and a
second encoding scheme for the second bitstream partition.
19. The apparatus of claim 18, further comprising: means for
encoding the first bitstream partition based on the first encoding
scheme and the second bitstream partition based on the second
encoding scheme.
20. The apparatus of claim 18, wherein the means for selecting a
first encoding scheme and the second encoding scheme includes:
means for calculating a cost to encode the first and second
bitstream partitions; means for selecting Huffman encoding as the
second encoding scheme if the cost to encode the first and second
bitstream partitions exceeds a predetermined threshold; and means
for selecting arithmetic encoding as the second encoding scheme if
the cost to encode the first and second bitstream partitions falls
below the predetermined threshold.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 10/713,807, filed on Nov. 14, 2003, which
claims priority from U.S. Provisional Application No. 60/469,187,
filed on May 12, 2003. U.S. patent application Ser. No. 10/713,807
and U.S. Provisional Application No. 60/469,187 are both
incorporated herein by reference in their entirety.
TECHNICAL FIELD
[0002] The invention relates to video data and more specifically to
methods and systems of coding, decoding, compressing, and
transmitting video data in as efficient a manner as possible.
BACKGROUND
[0003] The transmission of data is usually constrained by bandwidth
and throughput limitations. One cannot send or receive an infinite
amount of information in an infinitesimal amount of time. In order
to maximize the amount and quality of information being
transmitted, in some cases the information is compressed or coded
for transmission and uncompressed or decoded upon reception.
[0004] One area in which data compression is essential is in the
transmission of video data. Ordinary text, unless voluminous, is
easily and quickly transmitted. However, video data can include
aspects of color, brightness, and often stereo audio information. A
large amount of data is required to define even short video clips.
The transmission and coding of such data must be as efficient as
possible, i.e., it must require as little information as possible
to be transmitted.
[0005] Video compression is a subset of the general technique of
data compression, whereby a signal is squeezed or compressed into a
smaller set of numbers. These numbers will then take up less space
on a hard drive, or take less time to transmit over a network.
Before the numbers are used again, a decompression algorithm is
applied to expand the series of numbers to its original (or at
least a similar) form.
[0006] Video compression utilizes the fact that the signal is known
to originate as digitized video, in order to increase the
compression ratio, or the amount of squeezing that can be applied
to the series of numbers to be stored or transmitted. Significant
compression of video and audio are considered lossy algorithms
because they discard or lose some portion of the original
information; the reconstructed number series does not exactly match
the original. This is acceptable because the precision with which
we view video and audio, compared to the resolution of the
digitization process, is not perfect. While the video signal may
become slightly distorted, it is still recognizable. The degree to
which a compression algorithm faithfully reproduces the original
signal with minimum distortion or loss is a measure of the success
of the algorithm.
[0007] There are a number of good reasons to compress video and
audio signals, including technical issues and cost of equipment.
one overriding issue is the cost of transmitting data. As the
Internet matures into the de facto data transport platform for the
21st century, analog media such as videotape, film, and broadcast
will be supplanted by a digital media infrastructure built on the
Internet and Internet-related technologies. This digital
infrastructure will allow data to be transferred between any two
computing machines on the planet, if so desired. However, the speed
at which this data can be sent will depend on a number of factors.
In the limiting case, copper wires laid down over a century ago and
intended for analog voice communications are used with modem
technology (modem stands for Modulation/DEModulation) to transmit
data at speeds as low as 9600 bits per second. Similar speeds are
used to carry voice over wireless networks such as cellular.
Recently, cable modem, DSL, and satellite technologies have brought
six-figure data rates (100,000 to 1 million bits/second) to home
users. For high-end applications, optical fiber enables data rates
into the gigabit range (billions of bits per second) and
beyond.
[0008] Whatever the data rate available for a given application,
transmitting data costs money. At the present time, the cost of
sending one megabyte (8 million bits) over the Internet usually
costs anywhere from 5 cents at low volume, down to as low as one
cent at extremely high volume (this figure does not include the
cost at the receiving end). Therefore, the cost of transporting a
megabyte of data from one place to another is always more than a
penny.
[0009] Much work has been done in the field of video data
compression. Some of the features of video codecs in existence
include Discrete Cosine Transform compression, entropy coding, and
differential coding of motion vectors. Prior codecs also utilize
reference frames so that if a data packet is lost or corrupted, the
data can be retrieved by referring to a reference frame. All of
these features and difficulties therewith will be discussed in
greater detail below.
[0010] In DCT (Discrete Cosine Transform) based video compression
systems, an 8 by 8 block of pixel or prediction error signal data
is transformed into a set of 64 frequency coefficients (a DC value
and 63 AC values), which are then quantized and converted into a
set of tokens.
[0011] Typically the higher frequency AC coefficients are smaller
in magnitude and hence less likely to be non zero (i.e., more
likely to be zero) following quantization. Consequently, prior to
tokenization, the coefficients are often arranged in ascending
order starting with the lowest frequency coefficient (the DC value)
and finishing with the highest frequency AC coefficient. This scan
order, sometimes referred to as "zig-zag order", tends to group
together the non-zero values at the start and the zero values into
runs at the end and by so doing facilitates more efficient
compression.
[0012] However, this fixed scan order is seldom optimal. For
example, when encoding interlaced video material, certain high
frequency coefficients are much more prominent. This fact is
reflected in the prior art where there are examples of codecs (for
example MPEG-2), that mandate an alternative scan order for use
when coding interlaced video.
[0013] When optimizing a codec for a specific hardware device, it
is important to make sure that full use is made of any facilities
that the device may offer for performing multiple tasks in parallel
and to limit the extent to which individual parts of the decode
process become bottlenecks.
[0014] The instant invention's bitstream, in common with most other
video codecs, can broadly speaking be described as comprising
entropy coded tokens that can be divided into two main categories:
predictor or P tokens and prediction error or E tokens. P tokens
are tokens describing the method or mode used to code a block or
region of an image and tokens describing motion between one frame
and another. E tokens are used to code any residual error that
results from an imperfect prediction.
[0015] Entropy coding is a process whereby the representation of a
specific P or E token in the bitstream is optimized according to
the frequency of that token in the bitstream or the likelihood that
it will occur at a particular position. For example, a token that
occurs very frequently will be represented using a smaller number
of bits than a token that occurs infrequently.
[0016] Two of the most common entropy coding techniques are Huffman
Coding and arithmetic coding. In Huffman coding each token is
represented by a variable length pattern of bits (or a code).
Arithmetic coding is a more computationally complex technique but
it removes the restriction of using a whole number of bits for each
token. Using an arithmetic coder, it is perfectly possible to code
a very common token at an average cost of 2% of a bit.
[0017] Many multimedia devices have a co-processor unit that is
well suited to the task of entropy coding and a more versatile main
processor. Consequently, for the purpose of parallelization, the
process of encoding or decoding a bitstream is often divided into
entropy related tasks and non entropy related tasks. However, for a
given video clip, as the data rate increases, the number of tokens
to encode/decode rises sharply and entropy coding may become a
bottleneck.
[0018] With a conventional bitstream it is very difficult to
re-distribute the computational load of entropy coding to eliminate
this bottleneck. In particular, on the decode side, the tokens must
normally be decoded one at a time and in the order in which they
were encoded. It is also extremely difficult to mix methods or
entropy encoding (for example Huffman and arithmetic coding) other
than at the frame level.
[0019] By convention, most modern video codecs code the (x, y)
components of a motion vector, using a differential coding scheme.
That is, each vector is coded relative to the previous vector. For
example, consider two vectors (7,3) and (8,4). In this case the
second vector would be encoded as (1,1), that is (7+1, 3+1).
[0020] This scheme works well if most blocks or regions for which a
motion vector is coded exhibit motion that is similar to that of
their neighbors. This can often be shown to be the case, for
example when panning. However, it works less well if the motion
field is irregular or where there are frequent transitions between
background and foreground regions which have different motion
characteristics.
[0021] For most modern video codecs, motion prediction is an
important part of the compression process. Motion prediction is a
process whereby the motion of objects or regions of the image is
modelled over one or more frames and one or more `motion vectors`
is transmitted in the bitstream to represent this motion. In most
cases it is not possible to perfectly model the motion within an
image, so it is necessary to code a residual error signal in
addition to the motion information.
[0022] In essence, each motion vector points to a region in a
previously encoded frame that is similar to the region in the
current frame that is to be encoded. The residual error signal is
obtained by subtracting the predicted value of each pixel from the
actual value in the current frame.
[0023] Many modern video codecs extend the process by providing
support for prediction of motion to sub pixel accuracy, e.g,
half-pixel or quarter-pixel motion estimation. To create fractional
pixel data points, it is necessary to use some form of
interpolation function or filter applied to real (i.e. full pixel
aligned) data points.
[0024] Early codecs generally used simple bilinear interpolation as
shown in FIG. 1 attached hereto. In this example, A, B, C, and D
are full-pixel aligned data points and x, y, and z are half-pixel
aligned points. Point x is half-pixel aligned in the X direction
and can be calculated using the equation:
x=(A+B)/2. (1)
[0025] Point y is half-pixel aligned in the Y direction and can be
calculated using the equation:
y=(A+C)/2. (2)
[0026] Point z is half-pixel aligned in both X and Y can be
calculated using the equation:
z=(A+B+C+D)/2. (3)
[0027] Later codecs have tended to move towards the use of more
complex interpolation filters, such as bicubic filters, that are
less inclined to blur the image. In the example shown in FIG. 2, x
is a half-pixel point that lies half way between two full pixel
aligned pointes B and C. Using an integer approximation to a
bicubic filter it can be calculated using the equation:
x=(-A+9B+9C-D)/16. (4)
[0028] Though filters such as the one illustrated above tend to
produce sharper looking results, their repeated application over
several frames can in some situations result in unpleasant
artefacts such as false textures or false contouring.
[0029] When transmitting compressed video data over an unreliable
or questionable data link, it is important that a mechanism exists
for recovering when data is lost or corrupted, as video codecs are
often extremely sensitive to errors in the bitstream.
[0030] Various techniques and protocols exist for the reliable
transmission of data of such links, and these typically rely upon
detection of the errors and either re-transmission or the use of
additional data bits that allow certain types of error to be
corrected. In many situations the existing techniques are adequate,
but in the case of video conferencing over restricted bandwidth
links neither of the above mentioned approaches is ideal.
Re-transmission of lost data packets may not be practical because
it is likely to cause an increased end to end lag, while the use of
error correction bits or packets may not be acceptable in
situations where bandwidth is already severely restricted.
[0031] An alternative approach is simply to detect the error at the
decoder and report it to the encoder. The encoder can then transmit
a recovery frame to the decoder. Note that this approach may not be
appropriate if the error rate on the link is very high, e.g., more
than one error in every 10-20 frames.
[0032] The simplest form of recovery frame is a key frame (or intra
only frame). This is a frame that does not have any dependencies on
previous frames or the data therein. The problem with key frames is
that they are usually relatively large.
SUMMARY
[0033] Disclosed herein are aspects of systems, methods, and
apparatuses for encoding and decoding video signals.
[0034] One aspect of the disclosed implementations is a method for
encoding a video signal having at least one frame with a plurality
of blocks. The method includes identifying a first block within the
current frame; determining at least one predictor token related to
the first block; identifying a reference block within the plurality
of frames; determining at least one prediction error token derived
from a difference between the first block and the reference block;
assigning the at least one predictor token to a first bitstream
partition; assigning the at least one prediction error token to a
second bitstream partition; and selecting a first encoding scheme
for the first bitstream partition and a second encoding scheme for
the second bitstream partition.
[0035] Another aspect of the disclosed implementations is an
apparatus for encoding a video signal having at least one frame
with a plurality of blocks. The apparatus comprises a memory and a
processor configured to execute instructions stored in the memory
to: identify a first block within the current frame; determine at
least one predictor token related to the first block; identify a
reference block within the plurality of frames; determine at least
one prediction error token derived from a difference between the
first block and the reference block; assign the at least one
predictor token to a first bitstream partition; assign the at least
one prediction error token to a second bitstream partition; and
select a first encoding scheme for the first bitstream partition
and a second encoding scheme for the second bitstream
partition.
[0036] Another aspect of the disclosed implementations is an
apparatus for encoding a video signal having at least one frame
with a plurality of blocks. The apparatus includes means for
identifying a first block within the current frame; means for
determining at least one predictor token related to the first
block; means for identifying a reference block within the plurality
of frames; means for determining at least one prediction error
token derived from a difference between the first block and the
reference block; means for assigning the at least one predictor
token to a first bitstream partition; means for assigning the at
least one prediction error token to a second bitstream partition;
and means for selecting a first encoding scheme for the first
bitstream partition and a second encoding scheme for the second
bitstream partition.
[0037] It is an object of the invention to provide a video
compression method and codec that is efficient and reliable.
[0038] It is another object of the invention to provide a video
compression method and codec that can perform discrete cosine
transforms in an adaptive manner.
[0039] It is another object of the invention to provide a video
compression method and codec that performs entropy coding that
optimizes the resources of the hardware devices being employed.
[0040] It is another object of the invention to provide a video
compression method and codec that enhances motion vector
coding.
[0041] It is another object of the invention to provide a video
compression method and codec that accurately and efficiently
performs fractional pixel motion prediction.
[0042] It is another object of the invention to provide a video
compression method and codec that performs error recovery
efficiently, even in the environment of a video conference.
[0043] The above and other objects are fulfilled by the invention,
which is a method of compressing video data having at least one
frame having at least one block and each block having an array of
pixels. The disclosure includes at least one of the following
steps: I) transforming the pixels of each block into coefficients
and creating an optimal transmission order of the coefficients; II)
optimizing the speed of processing compressed video data by
partitioning the data bitstream and coding each partition
independently; III) predicting fractional pixel motion by selecting
an interpolation method for each given plurality of pixels
depending upon at least one metric related to each given block; and
IV) enhancing error recovery for a current frame using a frame
prior to the frame immediately before the current frame as the only
reference frame for lessening quality loss during data
transmission.
[0044] As for the coefficient reordering aspect of the invention,
the method transforms the pixels of each block into coefficients,
each coefficient having a coefficient position and a value and
determines a position value related to each coefficient position.
An optimal transmission order of coefficients is then created based
on the position values of each coefficient position, and the
coefficients are transmitted in the order so determined.
Preferably, the transmission order of coefficients is dynamically
re-ordered for each frame of video data. The transforming step
preferably transforms the pixels into discrete cosine transform
coefficients. The transmission order of coefficients may be
transmitted along with the coefficients. Preferably, each block has
the same number of coefficients and coefficient positions, and each
corresponding respective coefficient position conveys the same
respective information from block to block.
[0045] In an effort to reduce the amount of data being transmitted,
the transmission of coefficient order data may be limited to
changes in the coefficient order from one frame to the next frame.
Alternatively or in addition, the transmission order may be
consolidated into bands of coefficients, each band having a
plurality of coefficients organized by rank in numbers determined
above. In this case, only band information may be transmitted along
with the coefficients. Preferably, only band information will be
transmitted where a coefficient changes bands from one frame to the
next. As another alternative, all band information may always be
transmitted.
[0046] Reordering the coefficients can also include the provision
of a key frame. The inventive method may provide such a key frame
which is always completely self-encoded and requires no information
from or about a previous frame. In such a case, the encoder
determines if a given frame is a key frame. If it is determined
that the given frame is a key frame, the entire transmission order
of coefficients for the key frame is transmitted. If it is
determined that the given frame is not a key frame, only changes in
the transmission order of coefficients from the previous frame to
the given frame are transmitted.
[0047] As mentioned above, the invention contemplates optimizing
the speed of processing compressed video data by partitioning the
data bitstream and coding each partition independently.
Specifically, the invention divides the video data into at least
two data partitions and selects an optimal entropy coding method
for each data partition. The entropy coding methods thus selected
are applied respectively to each data partition. In one embodiment,
the video data is divided into a predictor token data partition and
an error token data partition; preferably, each data partition
undergoes a different entropy coding method, such as Huffman coding
and arithmetic coding. The various decoding processes of the
different data partitions may be performed asynchronously and/or
independently. This may be accomplished by providing at least two
subprocessors in the hardware, wherein one data partition is
decoded by one subprocessor and another data partition is decoded
by another subprocessor. Determining which entropy coding method is
to be used for a given data partition may be based on the size of
the given data partition.
[0048] In one embodiment of the method and codec, the predictor
token data partition is read and converted into a predictor block.
The error token data partition is also read and is converted into
coefficients and thence an error block. The predictor block and the
error block are summed to form an image block. As mentioned above,
it is preferable to provide at least two subprocessors, wherein
some of these steps are performed on one subprocessor and the rest
of the steps are performed on another subprocessor. Specifically,
the steps of reading the error token data partition and converting
the error token data partition into coefficients are preferably
performed by a fast entropy optimized subprocessor, and the other
steps are preferably performed by a general purpose
subprocessor.
[0049] The method optimizes decoder performance of the bitstream in
a way that avoids data and code cache misses. As many distinct
functions of the decoder's code as can fit into the code cache are
stored there. The code from this step is run for as many blocks as
can fit into the data cache. The next set of distinct functions of
the decoder's code and then collected, and the process is repeated
until all of the bitstream has been read and each of the blocks of
data have been produced.
[0050] Another aspect of optimizing decoder performance of the
bitstream optimizes the utilization of the subprocessors by
assigning each subtask to a separate processor. Preferably, the
portion of the decoder that reads error tokens from the bitstream
and translates them into coefficients is run on a fast entropy
optimized subprocessor. The portion of the decoder that reads the
predictor tokens from the bitstream and builds a filtered predictor
block from these tokens is run on a subprocessor with fast access
to memory. The portion of the decoder that translates the transform
coefficients from the above step into an error signal is run on a
subprocessor that has an optimized implementation of the transform
coder, and the portion of the decoder that adds the predictor block
to the error signal is run on a subprocessor optimized for motion
compensation.
[0051] The video data may be divided into two data partitions, a
first data partition representing a first area of the frame and a
second data partition representing a second area of the frame
(e.g,. upper and lower halves or left and right halves).
Alternatively, the video data may be divided into three data
partitions, each respectively representing level, saturation, and
hue information of the frame. In another version, the three data
partitions could respectively represent cyan, magenta, and yellow
information of the frame.
[0052] As mentioned before, the invention includes the aspect of
predicting fractional pixel motion by selecting an interpolation
method for each given plurality of pixels depending upon at least
one metric related to each given block. Specifically, the value of
the at least one metric associated with a given plurality of pixels
to encode is determined, and an interpolation method of encoding
the given plurality of pixels is selected depending upon the value
of the at least one metric determined. The interpolation method
thus selected is applied to the given plurality of pixels to
encode, and the process is repeated steps for each successive
plurality of pixels. The at least one metric may be at least one of
motion vector length and a complexity factor. The interpolation
methods may include bilinear, bicubic, quadratic, and B-spline
interpolation. The given plurality of pixels may be an entire frame
or a sub-portion thereof. If the motion vector length associated
with the given plurality of pixels is determined to be less than
the predetermined length value and the complexity factor associated
with the given plurality of pixels is determined to be greater than
the predetermined complexity value, then bicubic interpolation is
selected. A predetermined length value and the predetermined
complexity value is preferably set one time for a given number of
pluralities of pixels, and possibly once per frame.
[0053] The complexity factor is preferably a variance of the given
plurality of pixels, calculated as
C=(n Ex.sub.i.sup.2-(Ex.sub.i).sup.2)/n.sup.2 (4)
[0054] As mentioned above, the invention includes enhancing error
recovery for a current frame using a frame prior to the frame
immediately before the current frame as the only reference frame
for lessening quality loss during data transmission. Specifically,
the invention includes using a frame coded prior to the last frame
as the only reference frame for a given frame in order to lessen
the quality loss associated with transmission over lines which
produce lost or corrupt packets. This step is limited to at least
one of periodically (every F frames) and arbitrarily (based on some
other criteria).
[0055] This aspect of the invention is particularly well-suited for
a video conference. Specifically, each party to a video conference
compresses frames of video data and transmits the compressed video
data to the other parties with packets that are marked such that
the loss or corruption of a packet is detectable. If any party
detects that a packet is lost or corrupted, the detecting party
signals the sending party to send an update frame that has been
encoded using a reference frame that has already been successfully
received and decoded by all of the remaining parties.
[0056] The invention may preferably use reference frames in the
following manner. A fixed interval F of video frames may be
selected by the encoder and transmitted to the decoder. Every F'th
frame is encoded using only the previous encoded F'th frame for
reference. Every non F'th frame is encoded using the prior frame as
reference. Each frame of video is transmitted to the decoder so
that loss and corruption are detectable. All of these steps
preferably occur at the encoder. On the decoder side, the coded
video data is received from the encoder and decoded by the decoder.
If a packet is lost and the lost packet is associated with a non
F'th frame, the decoder waits for the next F'th frame to recover
the lost packet.
[0057] As another alternative, the invention encodes a current
frame at least one of periodically and arbitrarily at a higher than
ambient quality determined by a metric of statistics taken from
this and prior coded frames and stores the encoded current frame
for usage by subsequent frames as a secondary reference frame.
[0058] Variations in these and other aspects will be described in
additional detail hereafter.
DETAILED DESCRIPTION
[0059] Several different aspects of the invention will be described
hereinafter.
[0060] Dynamic Coefficient Reordering
[0061] In DCT (Discrete Cosine Transform) based video compression
systems an 8 by 8 block of pixel or prediction error signal data is
transformed into a set of 64 frequency coefficients (a DC value and
63 AC values), which are then quantized and converted into a set of
tokens.
[0062] Typically the higher frequency AC coefficients are smaller
in magnitude and hence less likely to be non zero following
quantization. Consequently, prior to tokenization the coefficients
are often arranged into ascending order starting with the lowest
frequency coefficient (the DC value) and finishing with the highest
frequency AC coefficient. This scan order, sometimes referred to as
"zig-zag order", tends to group together the non-zero values at the
start and the zero values into runs at the end and by so doing
facilitates more efficient compression.
[0063] However, this fixed scan order is seldom optimal. For
example, when encoding interlaced video material, certain high
frequency coefficients are much more prominent. This fact is
reflected in the prior art where there are examples of cbdecs (for
example MPEG-2), that mandate an alternative scan order for use
when coding interlaced video.
[0064] One aspect of the invention is a method whereby a codec can
optionally customize the scan order in which coefficients are
encoded to more optimally reflect the characteristics of a
particular data set.
[0065] According to this invention the codec maintains a record of
the distribution of zero vs. non-zero values for each of the DCT
coefficients, in one or more frames of video. This record is used
to create a custom scan order where coefficients that are more
likely to be non-zero appear earlier in the list.
[0066] The codec may optionally collate additional information such
as the average magnitude of the non-zero values for each
coefficient and use this to further optimize the scan order.
[0067] The overhead of transmitting a new custom scan order, or
updating a previously transmitted scan order, may in some cases
negate the benefit gained from improved coefficient coding
efficiency. Hence, a cost benefit analysis may be necessary to
determine if the update provides a net benefit.
[0068] The main factors affecting the outcome of this analysis are
the cost of update, the number of blocks (and hence coefficients)
to be encoded and the extent to which the new scan order deviates
from either a standard scan order or a previously encoded scan
order.
[0069] For an 8.times.8 element DCT, coding a "complete" custom
scan order (i.e., a new position for every one of the 64
coefficients), would require 384 bits (64 coefficients.times.6 bits
each). This cost is likely to be prohibitive unless the number of
blocks (and hence coefficients) to be coded is very large or the
optimum scan order differs very significantly from the default scan
order (this being either a standard scan order or one previously
encoded). The rationale behind this statement is that if the
default scan order is similar to the custom scan order, then the
average number of bits saved coding each block is likely to be
small, hence a large number of blocks must be coded to justify the
overhead of updating the scan order. Conversely if the default scan
order is dissimilar to the custom scan order, then the average
saving per block is likely to be high.
[0070] A simple way to improve this situation would be to only code
changes to the scan order. For example, for each coefficient, code
a bit to indicate whether it has changed its position in the scan
order and then if appropriate its new position. Though this will
typically result in a lower update cost, the worst case scenario
here is where the new scan order is different for all coefficients,
in which case the cost of update would be 448 bits
(64.times.7).
[0071] An attractive aspect of such an approach is that the cost of
update is lowest where the custom and default scan order are most
similar (and hence the likely cost saving per block is at its
lowest), and highest when they are most dissimilar.
[0072] The situation can be improved still further by considering
cost benefit at the level of individual coefficients or pairs of
coefficients. Consider, for example, a case where two coefficients
are adjacent to one another in the scan order and where the
likelihood of a non-zero value is almost identical for both. A
small change in the number of non-zero values for one or other of
the two coefficients could cause them to swap places in the custom
scan order. To encode this change would mean updating the scan
position for both coefficients at a cost of 14 bits (assuming the
update model above). However, the saving achieved might be
negligible. This problem is particularly relevant in respect of the
high order AC coefficients. Here, the frequency of non-zero values
is typically very low and even a tiny change could cause a
coefficients' position in the scan order to change
significantly.
[0073] While it is certainly feasible to base the calculation of a
custom scan order purely upon the distribution of zeros vs.
non-zeros for each coefficient, there are other factors that are
relevant. As mentioned previously, one of these is the average
magnitude of the non-zero values. Another is the fact that in some
cases a positive correlation may exist between the values of one or
more coefficients. For example, between a low order "pure
horizontal" AC coefficient and higher order `pure horizontal`
coefficients. In such cases, unless there is a substantial
difference in the prevalence of non-zero values, it may be
preferable to keep them in their original order (lowest frequency
to highest frequency).
[0074] The preferred implementation of this aspect of the invention
goes some way to addressing such issues whilst further reducing the
cost of updating the scan order. The procedure for creating a
custom scan order is broadly as follows:
[0075] (a) The DC coefficient is always coded first (position
0)
[0076] (b) Order the AC coefficients into descending order based
upon the proportion of the values that are non-zero for each
coefficient.
[0077] (c) Split the ordered list into 16 variable sized bands (see
table 1)
[0078] (d) Within each band re-order into zig-zag scan order.
[0079] Note that the subdivision into 16 bands as shown in Table 1
is based upon empirical observations with a range of different test
clips and is not necessarily optimal.
TABLE-US-00001 TABLE 1 Preferred scan order coefficient bands Band
First coefficient Last coefficient 0 1 1 1 2 4 2 5 10 3 11 12 4 13
15 5 16 19 6 20 21 7 22 26 8 27 28 9 29 34 10 35 36 11 37 42 12 43
48 13 49 53 14 54 57 15 58 63
[0080] Empirical experiments show that this banding strategy gives
results that are usually as good as and often better than those
obtained using a scan order based purely upon the proportion of the
values that are non zero; even before the cost of update is taken
into account.
[0081] The second advantage is that the cost of updating the scan
order is greatly reduced because it is only necessary to update a
value when it moves from one band to another. Further, only 4 bits
are needed to code a change in band.
[0082] A final optimization used in the preferred implementation is
based upon the observation that some coefficients change bands much
more frequently than others. For example, the high order AC
coefficients tend to change bands less often than the low order
coefficients.
[0083] If a particular coefficient is only updated 2% of the time,
for example, then it is wasteful to use 1 bit to indicate whether
or not it is to be updated on a given frame. By using arithmetic
coding techniques and assigning empirically determined update
probabilities to each coefficient, it is possible to get the
average update cost substantially below 1 bit per coefficient.
[0084] The following "C" code segments give supporting detail of
the preferred implementation of this aspect of the invention.
TABLE-US-00002 // Work out a new "preferred" scan order using the
zero/non-zero // frequency data that has been collected. void
CalculateScanOrder ( CP_INSTANCE *cpi ) { UINT32 i, j, k; UINT32
Sum; UINT32 tmp[2]; UINT32 NzValue [BLOCK_SIZE][2]; UINT32
GroupStartPoint, GroupEndPoint; // For each coefficient, calculate
the proportion of the values that 11 were non-zero as a scaled
number from 0-255. for ( i=1; i<BLOCK_SIZE; i++ ) { Sum =
cpi->FrameNzCount[i][0J + cpi->FrameNzCount[i][1]; if( Sum )
NzValue [i][0] = (cpi->FrameNzCount[i][I]*255)/Sum; else NzValue
[i] [0] = 0; NzValue [i][1] = i; } // Sort into decending order for
( i=1; i<BLOCK SIZE-1; i++ ) { for ( j =i+1; j>1; j-- ) { if
( NzValue [j][0] > NzValue [j-l][0] ) { // Swap them over tmp[O]
= NzValue [j- MO]; tmp[1] = NzValue [j-l][1]; NzValue [j-I][0]
=NzValue [j][0]; NzValue [j-l][1] = NzValue [j][1]; NzValue [j][0]
= tmp[O]; NzValue [j][1] = tmp[1]; } } } //Split into bands and
then re-sort within each band // into ascending order based upon
zig-zag scan position GroupEndPoint = 0; for ( k=0; k<SCAN_ORDER
BANDS; k++ ) { GroupStartPoint = GroupEndPoint + 1; GroupEndPoint
EndpointLookup[k]; for ( i=GroupStartPoint; i<GroupEndPoint; i++
) { for ( j =i+1; j>GroupStartPoint; j-- ) { if( NzValue [j][1]
< NzValue [j-1][l] ) { // Swap them over tmp[O] = NzValue
U-1][0]; tmp[1] =NzValue [j-l][1]; NzValue [j-I][0] = NzValue
[j][0]; NzValue [1-1][1] = NzValue NzValue [j][0] = tmp[0]; } } //
For each coef index mark its band number for ( i=GroupStartPoint;
i<<GroupEndPoint; i++ ) { // Note the new scan band number
for each coef. // NzValue [i][1] is the position of the coef in the
traditional // zig-zag scan order, i is the position in the new
scan order and /I k is the band number, cpi->NewScanOrderBands[
NzValue [i][1J ] = k; } } } // This structure gives scan order
update probabilities (scaled to the // range of 1-255) for each of
the dct coefficients (in traditional zig- // zag order). The values
are passed to the function "nDecodeBoolO" // and indicate the
probability that the result will be 0 (FALSE). // const UINT8
ScanBandUpdateProbs[BLOCK SIZE] .sub.-- { 255, 132, 132, 159, 153,
151, 161, 170, 164, 162, 136, 110, 103, 114, 129, 118, 124, 125,
132, 136, 114, 110, 142, 135, 134, 123, 143, 126, 153, 183, 166,
161, 171, 180, 179, 164, 203, 218, 225, 217, 215, 206, 203, 2I7,
229, 241, 248, 243, 253, 255, 253, 255, 255, 255, 255, 255, 255,
255, 255, 255, 255, 255, 255, 255 }; // Reads updates to the scan
order if they are available for this frame. void UpdateScanOrder(
PB INSTANCE*pbi ) { // Is the scan order being updated this frame?
If (nDecodeBool( 128 ) ) { // Read in the those scan bands that
have been updated for (i = l; i < BLOCK SIZE; i++ ) for (i = l;
i < BLOCK SIZE; i++ ) { U Has the band for this coefficient been
updated? if( nDecodeBool( ScanBandUpdateProbs[i] ) ) {
pbi->ScanBands[i] = VP6_bitread( SCAN_BAND UPDATE BITS ); }
//Build the new scan order from the scan bands data BuildScanOrder(
pbi, pbi->ScanBands ); } } // Builds a custom scan order from a
set of scan band data, void BuildScanOrder( P8 _INSTANCE *pbi,
UINT8 *ScanBands ) { UINT32 i, j; UINT32 ScanOrderIndex =1; // DC
is fixed pbi->ModifedScanOrder[O] = 0; // Create a scan order
where within each band the coefs are in ascending // order (in
terms of their original "zig-zag" scan order positions). for ( i =
0; i < SCAN_ORDER BANDS; i++ ) { for (j = 1; j < BLOCK SIZE;
j++ ) { if( ScanBands[j] == i ) {
pbi->ModifiedScanOrder[ScanOrderindex] = j; ScanOrderindex++; }
} } }
[0085] Using Independent Bitstream Partitions to Facilitate Encoder
and Decoder Optimization, and Using of Mixed Mode Entropy
Coding
[0086] When optimizing a codec for a specific hardware device, it
is important to make sure that full use is made of any facilities
that the device may offer for performing multiple tasks in parallel
and to limit the extent to which individual parts of the decode
process become bottlenecks.
[0087] The inventive bitstream, in common with most other video
codecs, can broadly speaking be described as comprising entropy
coded tokens that can be divided into two main categories.
[0088] (a) Predictor tokens (hereinafter referred to as P tokens).
For example, tokens describing the method or mode used to code a
block or region of an image and tokens describing motion between
one frame and another.
[0089] (b) Prediction Error signal tokens (hereinafter referred to
as E tokens). These are used to code any residual error that
results from an imperfect prediction.
[0090] Entropy coding is a process whereby the representation of a
specific P or E token in the bitstream is optimized according to
the frequency of that token in the bitstream or the likelihood that
it will occur at a particular position. For example, a token that
occurs very frequently will be; represented using a smaller number
of bits than a token that occurs infrequently.
[0091] Two of the most common entropy coding techniques are Huffman
Coding and arithmetic coding. In Huffman coding each token is
represented by a variable length pattern of bits (or a code).
Arithmetic coding is a more computationally complex technique but
it removes the restriction of using a whole number of bits for each
token. Using an arithmetic coder it is perfectly possible, for
example, to code a very common token at an average cost of 1/2 of a
bit.
[0092] Many multimedia devices have a co-processor unit that is
well suited to the task of entropy coding and a more versatile main
processor. Consequently, for the purpose of parallelization, the
process of encoding or decoding a bitstream is often divided into
entropy related tasks and non entropy related tasks.
[0093] However, for a given video clip, as the data rate increases
the number of tokens to encode/decode rises sharply and entropy
coding may become a bottleneck.
[0094] With a conventional bitstream it is very difficult to
re-distribute the computational load of entropy coding to eliminate
this bottleneck. In particular, on the decode side, the tokens must
normally be decoded one at a time and in the order in which they
were encoded. It is also extremely difficult to mix methods or
entropy encoding (for example Huffman and arithmetic coding) other
than at the frame level.
[0095] This aspect of the invention is a method designed to make it
easier to redistribute the computational load of entropy coding,
and to facilitate the use of mixed mode entropy coding through
structural changes to the bitstream.
[0096] According to this method each frame in the bitstream is
divided into two or more wholly independent data partitions. The
partitions may be written to or read from in parallel and are not
constrained to use the same entropy encoding mechanism. This makes
it easier to optimize the process of encoding or decoding to avoid
entropy related bottlenecks at high bit-rates.
[0097] The ability to use both Huffman and arithmetic techniques,
or a mixture of the two, within a single frame, gives the encoder
the ability to better optimize the tradeoff between the amount of
compression achieved and computational complexity. For example, an
encoder could be configured to use the less complex Huffman method
in one or more of its partitions if the projected size of a frame
exceeded a given threshold.
[0098] The specific implementation of this aspect of the invention
supports the use of either one or two main data partitions. In
addition there is a small header partition.
[0099] When using a single data partition the codec behaves in a
conventional manner. Both P and E tokens are coded using a
proprietary arithmetic coder in a single data partition. This
method has slightly lower overheads (a few bits per frame) but is
less flexible. For example:
TABLE-US-00003 Partition 1 (block 1) P, P, E, E, E (block 2) P, E,
E, (block 3) P, P, E, E, E,
[0100] In the second case, however, the P and E tokens are written
to separate partitions. For example:
TABLE-US-00004 Partition 1 Partition 2 (block 1) PP EEE (block 2) P
EE (block 3) P EEEE
[0101] The size of the first partition does not tend to vary as
much with data rate, and is comparatively small, so this partition
is always coded using the arithmetic coder. The second partition
may be coded using either the arithmetic coder or the Huffman
coder.
[0102] The choice of Huffman or arithmetic coding for the second
partition can be signalled at the frame level. In the preferred
implementation the choice depends upon the performance of the
target decoder platform and the projected size in bits of the
frame. Specifically, if the frame size rises above a threshold
number, where there is a danger that the decoder will have problems
decoding the frame in real time, then the Huffman method is
used.
[0103] Encoder performance can also be an issue where real time
encoding is a requirement, but with the possible exception of key
frames (which tend to be larger and have no dependencies on other
frames), the cost of the entropy coding is usually a smaller
fraction of the total computational cost in the encoder.
[0104] The following "C" code segments give supporting detail of
the preferred implementation of this aspect of the invention.
TABLE-US-00005 //This function packs the encoded video data for a
frame using either // one arithmetically coded data partition, two
arithmetically coded // data partitions, or one arithmetically
coded data partition and one Huffman data partition. // //The
argument "cpi" is a pointer to the main encoder instance data
structure. void PackCodedVideo ( CP_1NSTANCE *cpi ) { UINT32
PartitionTwoOffset; BOOL CODER *bc &cpi->bc; // Arithmetic
coder instance data structure B O O L CODER *bc2 &cpi->bc2;
// 2nd Arithmetic coder instance structure P8 ...INSTANCE *pbi =
&cpi->pb; // Decoder instance data structure // Initialize
the raw buffer i/o used for the header partition.
InitAddRawBitsToBuffer ( &cpi->RawBuffer, pbi-
>DataOutputPtr ); // Start the arithmetic and or Huffman coders
// If we are using two data partitions... if( pbi->MultiStream I
I (pbi->VpProfile = SIMPLE PROFILE) ) { //Start the first
arithmetic coder: Allow for the raw header bytes. VP6_StartEncode (
bc, (pbi->DataoutputPtr + ((KeyFrame) ? 4 : 3)) ); // Create
either a second arithmetic or Huffman partition // This is
initially written to a holding buffer "cpi->OutputBuffer2" if (
pbi->UseHuffman ) InitAddRawBitsToBuffer (
&pbi->HufBuffer, cpi->OutputBuffer2 ); else
VP6_StartEncode ( bc2, cpi->OutputBuffer2 ); // We are only
using a single data partition coded using the arithmetic coder.
else { // Start the arithmetic coder: Allow for the raw header
bytes. VP6_StartEncode( bc, (pbi->DataOutputInPtr + ((KeyFrame)
? 2 : 1)) ); // Write out the frame header information including
size. WriteFrameHeader (... ); if( pbi->UseHuffman )
PackHufmmanCoeffs (... ); else PackArithmeticCoeffs (... ); // Stop
the arithmetic coder instance used for the first data partition
VP6_StopEncode ( be ); //Work out the offsets to the data
partitions and write them into // the space reserved for this
information in the raw header partition. // // If we are using two
data partitions.... if( pbi->MultiStream I I (pbi->VpProfile
= SIMPLE PROFILE) ) { // Offset to first data partition from start
of buffer PartitionTwoOffset = 4 + be->pos; //Write offset to
second data partition partition. AddRawBitsToBuffer (
&cpi->RawBuffer, PartitionTwoOffset ,16 ); // If Huffman was
used for the second data partition ... if( pbi->UseHuffman ) {
// Flush the buffer for the Huffman coded output partition
EndAddRawBitsToBuffer ( &pbi->HuffBuffer ); // Copy the
Huffman coded data from the holding buffer into the output buffer.
memcpy ( &cpi->RawBuffer.Buffer[ PartitionTwoOffset ], pbi-
>HuffBuffer.Buffer, pbi->HuffBuffer.pos ); // Stop the
arithmetic coder instance used by the second data partition.
VP6_StopEncode ( bc2 ); //Copy over the contents of the holding
buffer used by //the second partition into the output buffer.
>DataOutputlnPtr[ PartitionTwoOffset ], bc2.buffer, bc2.pos ); }
) // Stop and flush the raw bits encoder used for the header
EndAddRawBitsToBuffer ( &cpi->RawBuffer ); } //This function
is called to select the coding strategy when using two data
partitions. void SelectMultiStreamMethod ( CP _INSTANCE *pbi ) { //
Calculate an estimated cost (Shannon entropy) for the frame using
// the information gathered re, the distribution of tokens in the
frame. // Add in the previously calculated cost estimate for coding
any mode and 11 motion vector information. EstimatedFrameCost =
VP6_ShannonCost( cpi ) + ModeMvCost; // Decide whether to drop
using Huffman coding for the second data partition. ) if (
EstimatedFrameCost > HuffmanCodingThreshold ) pbi-
>UseHuffman = TRUE; else pbi->UseHuffman = FALSE; }
[0105] Using a Plurality of Filters to Enhance Fractional Pixel
Motion Prediction in Video Codecs
[0106] For most modem video codecs motion prediction is an
important part of the compression process. Motion prediction is a
process whereby the motion of objects or regions of the image is
modelled over one or more frames and one or more motion vectors is
transmitted in the bitstream to represent this motion. In most
cases it is not possible to perfectly model the motion within an
image, so it is necessary to code a residual error signal in
addition to the motion information,
[0107] In essence, each motion vector points to a region in a
previously encoded frame that is similar to the region in the
current frame that is to be encoded. The residual error signal is
obtained by subtracting the predicted value of each pixel from the
actual value in the current frame.
[0108] Many modem video codecs extend the process by providing
support for prediction of motion to sub pixel accuracy. For example
half pixel or quarter pixel motion estimation. To create fractional
pixel data points it is necessary to use some form of interpolation
function or filter applied to real (i.e. full pixel aligned) data
points.
[0109] Early codecs generally used simple bilinear
interpolation
TABLE-US-00006 A x B y z C D
[0110] In this example, A, B, C, and D are full pixel aligned data
points and x, y, and z are half pixel aligned points. Point x is
half pixel aligned in the X direction and can be calculated using
the formula: x=(A+B)/2. Point y is half pixel aligned in the Y
direction and can be calculated using the formula: y=(A+C)/2. Point
z is half pixel aligned in both X and Y can be calculated using the
formula: z=(A+B+C+D)/2.
[0111] Later codecs have tended to move towards the use of more
complex interpolation filters, such as bicubic filters, that are
less inclined to blur the image. In the following example x is a
half pixel point that lies half way between two full pixel aligned
pointes B and C. Using an integer approximation to a bicubic filter
it can be calculated using the formula: x=(-A+9B+9C-D)/16.
A B x C D
[0112] Though filters such as the one illustrated above tend to
produce sharper looking results, their repeated application over
several frames can in some situations result in unpleasant
artefacts such as false textures or false contouring.
[0113] This aspect of the invention is a method where by a codec
can use a mixture of filtering techniques to create more optimal
fractional pixel predictors and select between these methods at the
clip level, the frame level, the block level or even at the level
of individual pixels.
[0114] In the preferred implementation a selection can be made on a
per frame basis as to whether to use bilinear filtering only,
bicubic filtering only or to allow a choice to be made at the block
level.
[0115] Selection at the block or region level could be achieved by
means of explicit signalling bits within the bitstream, but in the
preferred implementation selection is made using contextual
information already available in the bitstream and by means of a
complexity metric applied to the full pixel aligned data values
that are going to be filtered.
[0116] In situations where the quality of the motion predictor is
poor (for example if it was not possible to find a good prediction
for a block in the previous frame reconstruction), bilinear
filtering is often the best option. Specifically where the
prediction is poor the sharpening characteristics of the bicubic
filter may lead to an increase in the high frequency content of the
residual error signal and make it more difficult to encode.
[0117] In the absence of explicit signalling bits in the bitstream
various contextually available values that can be shown to be
correlated to a greater or lesser extent with poor prediction
quality. One of the simplest of these is motion vector length.
Specifically the quality of the prediction tends to degrade with
increasing motion vector length. The smoothness of the motion field
in is another possible indicator (i.e. how similar are the motion
vectors of neighbouring blocks).
[0118] Bilinear filtering also tends to be the better option in
situations where the choice of vector is unreliable (for example,
where there is not very much detail in the image and there are many
candidate vectors with similar error scores). In particular,
repeated application of a bicubic filter over many frames, to a
region that is relatively flat and featureless, may give rise to
unwanted artefacts.
[0119] In the preferred implementation two factors are taken into
account when choosing the filtering method. The first is the length
of the motion vector. The second is a complexity metric C
calculated by analyzing the set of full pixel aligned data points
that are going to be filtered.
[0120] Bicubic filtering is used only if both the following test
conditions are satisfied:
[0121] 1. The motion vector is shorter than a threshold value L in
both X and Y.
[0122] 2. The complexity C is greater than a threshold value T.
[0123] In the preferred implementation C is a variance of a set of
n data points xi calculated according to the formula:
C=(nExi.sup.2-(Exi).sup.2)/n.sup.2 (4)
[0124] In the preferred implementation the complexity threshold T
and the motion vector length threshold L may be set by the encoder
on a once per frame basis.
[0125] The following "C" code segments give supporting detail the
preferred implementation of this aspect of the invention.
TABLE-US-00007 PredictBlockFunction(... ) { if (
pbi->PredictionFilterMode = AUTO_SBLECT PM) { // Use bilinear if
vectors are above a threshold length in X or Y if( ((
abs(pbi->mbi.Mv[bp].x ) > BicMvSizeLimit) II ((
abs(pbi->mbi.Mv[bp].y ) > BicMvSizeLimit) ) {
FilterBlockBilinear(.., ); } else { //Calculate a complexity metric
(variance). //Note: for performance reasons the variance function
only // examines 16 data points (every other point in X and Y //
for an 8.times.8 block). Var = Varl6Point( DataPtr, Stride ); //If
the complexity is above the given threshold use bicubic else use
bilinear if( Var >= pbi->PredictionFilterVarThresh )
FilterBlockBilcubic(... ); else FilterBlockBilinear( ); } }} UINT32
Varl6Point ( UINT8 *DataPtr,1NT32 Stride ) { UINT32 i, j; UINT32
XSum=O, XXSum=O; UINT8 *Dif f tr = DataPtr; //Use every other point
in X and Y for ( i = 0; i < BLOCK HEIGHT WIDTH; I += 2 ) for (j
= 0; j < BLOCK_HEIGHT WIDTH; j += 2 ) { XSum += DiffPtr[j];
XXSum += DiffPtr[j] * DiffPtr[j]; } //Step to next row of block.
DiffPtr += (SourceStride << 1) //Compute population variance
as mis-match metric, return (( (XXSum* 16) - (XSum*XSum) ) 1256 );
}
[0126] Enhanced Motion Vector Coding
[0127] By convention, most modern video codecs code the (x,y)
components of a motion vector, using a differential coding scheme.
That is, each vector is coded relative to the previous vector. For
example, consider two vectors (7,3) and (8,4). In this case the
second vector would be encoded as (1,1), that is (7+1, 3+1).
[0128] This scheme works well if most blocks or regions for which a
motion vector is coded exhibit motion that is similar to that of
their neighbours. This can often be shown to be the case, for
example when panning. However, it works less well if the motion
field is irregular or where there are frequent transitions between
background and foreground regions which have different motion
characteristics.
[0129] This aspect of the invention is an alternative strategy for
encoding motion vectors which retains the advantages of
differential coding whilst being more tolerant of irregular fields
and background foreground transitions.
[0130] According to this invention, the codec maintains two or more
reference vectors relative to which motion vectors may be encoded.
The codec could switch between these reference vectors via explicit
signalling bits within the bitstream, but in the preferred
implementation the decision is based upon the coding methods and
motion vectors used by the blocks' immediate neighbours.
[0131] In the preferred implementation, a block may be coded as and
intra block (with no dependency on any previous frames), or an
inter block which is dependent upon either the previous frame
reconstruction, or an alternative reference frame that is updated
only periodically.
[0132] When coding with respect to the previous frame
reconstruction or the alternative reference frame, the invention
supports the following coding mode choices.
[0133] (a) Code with no motion vector (that is to say an implicit
(0,0) vector)
[0134] (b) Code using the same vector as the `nearest`
neighbouring.
[0135] (c) Code using the same vector as the `next nearest`
neighbour.
[0136] (d) Code using a new motion vector.
[0137] When defining the nearest or next nearest neighbour, only
blocks that are coded with respect to the same reference frame as
the current block and those that are coded with a non-zero motion
vector are considered. All other blocks are ignored.
[0138] When defining the next nearest neighbour, blocks that are
coded with the same vector as the nearest neighbour are also
ignored.
[0139] When coding a new motion vector the codec may use either
(0,0) or the nearest vector as the reference vector. In the
preferred implementation the nearest vector is used if the block
from which it is derived is either the block immediately to the
left or immediately above the current block (assuming that blocks
are being coded from left to right and from top to bottom). In all
other cases new vectors are coded with respect to (0,0).
[0140] Several extensions to the basic method are possible. If the
nearest and next nearest neighbours are the blocks immediately to
the left and immediately above the current block respectively, then
some sort of compound vector derived from the two could be used as
a reference for coding the new vector. Alternatively `nearest`
could be used to predict the x component and `next nearest` the y
component.
[0141] Another possible extension, still assuming that nearest and
next nearest are the blocks immediately to the left and above the
current block, would be to take special account of the case where
the nearest and next nearest vectors are not similar, and in such a
case revert to 0 as the reference value for x, y or both x and
y.
[0142] This method retains the benefits of simple differential
coding in cases where there is a regular or slowly changing motion
field. However, the use of special `no vector`, `nearest` and `next
nearest` modes makes for more efficient coding of transitions
between foreground and background and the ability to switch
automatically between multiple coding origins makes the method more
tolerant of irregular motion fields.
[0143] The following "C" code segments give supporting detail of
the preferred implementation of this aspect of the invention.
TABLE-US-00008 // This function determines whether or not there is
a // qualifying nearest and next nearest neighbour for the //
current block, what the motion vectors are for those // and how
close the nearest neighbour is. // xtNearest( PB_INSTANCE *pbi,
UINT32 MBrow, UINT32 MBcoI, UINT8 ReferenceFrame INT32 * Type ) {
int i; UINT32 OffsetMB; UINT32 BaseMB = MBOffset(MBrow,MBcol);
MOTION VECTOR ThisMv; //Set default outcome *Type =
NONEAREST_MACROBLOCK; // Search for a qualifying "nearest" block
for ( i=0; i<12 ; i++ ) { OffsetMB = pbi->mvNearOffset[i] +
BaseMB; // Was the block coded with respect to the same reference
frame? if ( VP6_Mode2Frame[pbi->predictionMode[OffsetMB]] 1=
ReferenceFrame) continue; // What if any motion vector did it use
ThisMv.x = pbi->MBMotionVector[OffsetMB].x; ThisMv.y =
pbi->MBMotionVector[OffsetMB].y; //If it was non-zero then we
have a qualifying neighbour if ( ThisMv.x 11 ThisMv.y ) Nearest.x =
ThisMv.x; Nearest.y = ThisMv.y; *Type = NONEAR_MACROBLOCK; break; }
pbi->mbi.NearestMvIndex = i; // Search for a qualifying "next
nearest" block for ( i=i+1; i<12; i++ ) { OffsetMB =
pbi->mvNearOffset[i] + BaseMB; //Was the block coded with
respect to the same reference frame? if ( VP6_Mode2Frame[pbi-
>predictionMode[OffsetMB]] != ReferenceFrame) continue; // What
if any motion vector did it use ThisMv.x =
pbi->MBMotionVector[OffsetMB].x; ThisMv.y =
pbi->MBMotionVector[OffsetMB).y; // If this vector is the same
as the "nearest" vector then ignore it. if( (ThisMv.x == Nearest.x)
&& (ThisMv.y Nearest,y) ) continue; // If it was non-zero
then we have a qualifying neighbour if( ThisMv.x 1I ThisMv.y ) {
NextNearest.x ThisMv.x; NextNearest.y ThisMv.y; *Type = MACROBLOCK;
break; } indicates data missing or illegible when filed
[0144] Using an Alternate Reference Frame in Error Recover
[0145] When transmitting compressed video data over an unreliable
data link it is important that a mechanism exists for recovering
when data is lost or corrupted, as video codecs are often extremely
sensitive to errors in the bitstream.
[0146] Various techniques and protocols exist for the reliable
transmission of data of such links and these typically rely upon
detection of the errors and either re-transmission or the use of
additional data bits that allow certain types of error to be
corrected.
[0147] In many situations the existing techniques are adequate but
in the case of video conferencing over restricted bandwidth links
neither of the above mentioned approaches is ideal. Re-transmission
of lost data packets may not be practical because it is likely to
cause an increased end to end lag, whilst the use of error
correction bits or packets may not be acceptable in situations
where bandwidth is already severely restricted.
[0148] An alternative approach is simply to detect the error at the
decoder and report it to the encoder. The encoder can then transmit
a recovery frame to the decoder. Note that this approach may not be
appropriate if the error rate on the link is very high. For
example, more than one error in every 10-20 frames.
[0149] The simplest form of recovery frame is a key frame (or intra
only frame). This is a frame that does not have any dependencies on
previous frames or the data therein. The problem with key frames is
that they are usually relatively large.
[0150] Disclosed herein is a mechanism whereby a codec maintains a
one or more additional references frames (other than the
reconstruction of the previously coded frame) that can be used as a
starting point for more efficiently coding of recovery frames.
[0151] In the preferred implementation of the invention the codec
maintains a second reference frame which is updated whenever there
is a key frame and optionally at other times, via a flag bit in the
frame header. For example the encoder could choose to update the
second reference frame once every `X` seconds or whenever an error
recovery frame is encoded.
[0152] Provided that the content of the second reference frame is
at least in some respects similar to the content of the current
frame, differential coding with respect to the second reference
frame is likely to be much cheaper than coding a key frame.
[0153] There are several ways in which one or more alternate
reference frames may be used to enhance compression quality or
efficiency. One obvious usage that is covered in the prior art is
in video sequences that oscillate back and forth between two or
more different scenes. For example, consider an interview where the
video switches back and forth between interviewer and interviewee.
By storing separate reference frames as a baseline for each camera
angle the cost of switching back and forth between these can be
greatly reduced, particularly when the scenes are substantially
different.
[0154] Whilst the invention has the option of using an alternate
reference frame in this way, the subject of this invention is the
use of a periodically updated alternate reference frame to enhance
the quality of compressed video is situations where there is a slow
progressive change in the video. Good examples of this are slow
pans, zooms, or tracking shots.
[0155] According this aspect of the invention, during slow pans or
other such slow progressive changes the encoder periodically
inserts frames which are encoded at a significantly higher quality
than the surrounding frames and which cause the second or
alternative reference frame to be updated.
[0156] The purpose of these higher quality "second reference
update" frames is to re-instate detail that has incrementally been
lost since the last key frame, or the last second reference update,
and to provide a better basis for inter frame prediction in
subsequent frames. This strategy of periodically raising the
quality (and hence the data rate) and at the same time updating the
second reference frame can be shown to provide a much better
cost/quality trade off in some situations than simply coding all
the frames at a similar quality.
[0157] Central to an effective implementation is the method for
determining an appropriate interval for the second reference
updates and the amount by which the quality or data rate should be
boosted.
[0158] In the preferred implementation of this aspect of the
invention, several factors are taken into account. These
include:
[0159] (a) The average amplitude of motion vectors in the preceding
few frames as an indicator of the speed of motion.
[0160] (b) The extent to which the motion field is correlated. For
example are the motion vectors all fairly similar.
[0161] (c) The extent to which the second reference frame has been
used as a predictor in preference to the previous frame
reconstruction in the previous few frames.
[0162] (d) The ambient quality or quantizer setting.
[0163] In cases where the average amplitude of the motion vectors
used is high (indicating faster motion), the interval between
second reference updates and the quality boost are both decreased.
Conversely, where the motion is slow a larger quality boost and
longer interval are used.
[0164] In cases where the motion field is highly correlated, that
is to say that there are a lot of similar motion vectors, the
quality boost for second reference frame updates is increased.
Conversely, when the motion field is poorly correlated the extent
of the boost is decreased.
[0165] In cases where the second reference frame is frequently
being used as a predictor in preference to the previous frame
reconstruction, the quality boost is increased. Conversely in cases
where the second reference frame is not used frequently it is
decreased.
[0166] The extent of the quality boost also depends to some extent
on the ambient quality with a larger boost being used when the
ambient quality is low and a smaller boost when the ambient quality
is high.
[0167] The following pseudo code gives more detail of the preferred
implementation of this aspect of the invention.
TABLE-US-00009 For each frame Calculate of the average amplitude of
the X and Y motion vector components (AvX and AvY) specified in
pixel units. MotionSpeed = the larger of AvX and AvY Calculate a
variance number for the X and Y motion vector components (VarianceX
and VarianceY). MaxVariance = the larger of VarianceX and VarianceY
MotionComplexity = MotionSpeed + (VarianceX 14) + (VarianceY l 4)
If a second reference frame update is due this frame Calculate a
data rate % boost number (Boost) based upon the predicted quality
index (actually a quantizer setting) for the frame, This can range
between +0% at highest quality to +1250% when the quality level is
very low. Multiply Boost by a MotionSpeed correction factor where
the factor can vary between 1 for very small values of MotionSpeed
to 0 for large values of MotionSpeed. Apply a further correction
factor to Boost based upon the extent to which the second reference
frame has been used in the previous few frames. This can vary from
1/16 in cases where the second reference frame was not used at all
in the previous few frames up to 1 in cases where it was used for
15% or more of the coded blocks. A series of tests are then applied
to determine whether or not to go ahead and update the second
reference frame with the calculated % boost.
[0168] The principal tests are:
[0169] (Boost>MinBoostTreshold) and
[0170] (MotionSpeed<MaxMotionSpeedThreshold) and
[0171] (MaxVariance<MaxVarianceThreshold) where
MinBoostTreshold, MaxMotionSpeedThreshold and MaxVarianceThreshold
are configurable parameters.
[0172] The invention has a number of special "motion re-use" modes
that allow the motion vector for a block to be coded more cheaply
if it is the same as the motion vector used by one of its near
neighbours. Further tests are applied to discount cases where the
usage of these modes falls below a threshold level.
[0173] If the decision is made to apply the boost and update the
second reference frame then set the frame data rate target to the
baseline value+Boost % and calculate and the interval until the
next update based upon MotionSpeed.
[0174] If the decision is made not to apply the boost and not to
update the second reference frame, then update the frame as normal
with a 0% data rate boost.
[0175] Else if a second reference frame update is not due,
calculate a reduced frame data rate target (negative boost) that
takes into account the level of boost applied when the second
reference frame was last updated and the current update
interval.
[0176] Using a Reconstruction Error Metric to Select Between
Alternative Methods for Creating Fractional Pixel Predictions
[0177] Many modern video codecs support prediction of motion to sub
pixel accuracy. For example half pixel or quarter pixel motion
estimation. To create fractional pixel data points it is necessary
to use some form of interpolation function or filter applied to
real (i.e., full pixel aligned) data points.
[0178] Early codecs generally used simple bilinear
interpolation.
TABLE-US-00010 A x y z C
[0179] In this example A, B, C, and D are full pixel aligned data
points and x,y and z are half pixel aligned points.
[0180] Point x is half pixel aligned in the X direction and would
be calculated using the formula (A+B/2).
[0181] Point y is half pixel aligned in the Y direction and would
be calculated using the formula (A+C/2).
[0182] Point z is half pixel aligned in both X and Y would be
calculated using the formula (A+B+C+D /2).
[0183] Later codecs have tended to move towards the use of more
complex interpolation filters such as bicubic filters, that are
less inclined to blur the image. In the following example `x` is a
half pixel point that lies half way between two full pixel aligned
pointes B and C. It can be calculated using the formula
(-A+9B+9C-D)/16.
[0184] A B x C D
[0185] Though filters such as the one illustrated above tend to
produce sharper results, repeated application over several frames
can sometimes result in unpleasant artefacts such as exaggeration
of textures or false contouring.
[0186] This aspect of the invention is a method where by a codec
can use a mixture of bilinear and bicubic filtering to calculate
more optimal fractional pixel predictors and select between these
methods either at a frame level or at the level of the individual
blocks or regions to which motion vectors are applied.
[0187] Selection at the block or region level could be achieved by
means of signalling bits within the bitstream, but in the preferred
implementation selection is made by means of a complexity metric
applied to the set of pixels in the previous reconstructed image
that are going to be filtered.
[0188] According to this method, blocks or regions with a
complexity score above a threshold value "T" are filtered using the
bicubic method whilst those with a lower complexity score are
filtered using the bilinear method.
[0189] In the preferred implementation the complexity metric is the
variance of the set of "n" full pixel aligned data points to be
filtered, where variance is defined as:
(nEx.sup.2-(Ex).sup.2)/n.sup.2. (5)
[0190] In the preferred implementation the threshold value T' may
be updated on a once per frame basis.
* * * * *