U.S. patent application number 16/993638 was filed with the patent office on 2020-11-26 for sub-block dmvr.
The applicant listed for this patent is Beijing Bytedance Network Technology Co., Ltd., Bytedance Inc.. Invention is credited to Hongbin LIU, Yue WANG, Kai ZHANG, Li ZHANG.
Application Number | 20200374543 16/993638 |
Document ID | / |
Family ID | 1000005032568 |
Filed Date | 2020-11-26 |
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United States Patent
Application |
20200374543 |
Kind Code |
A1 |
LIU; Hongbin ; et
al. |
November 26, 2020 |
SUB-BLOCK DMVR
Abstract
A method of decoding a bitstream comprising a digital
representation of a video includes decoding motion information for
a current video block from the bitstream, estimating matching costs
of the current video block using one or more templates based on a
partial set of pixel locations in the current block, where in each
of the one or more templates includes a video block with multiple
samples and refining the motion information of the current block
using a template having a minimum matching cost. The method further
includes estimating matching costs of the current video block to be
performed by dividing the current video block into sub-blocks and
estimating matching cost for each sub-block using a corresponding
partial set of pixel locations for that sub-block.
Inventors: |
LIU; Hongbin; (Beijing,
CN) ; ZHANG; Li; (San Diego, CA) ; ZHANG;
Kai; (San Diego, CA) ; WANG; Yue; (Beijing,
CN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Beijing Bytedance Network Technology Co., Ltd.
Bytedance Inc. |
Beijing
Los Angeles |
CA |
CN
US |
|
|
Family ID: |
1000005032568 |
Appl. No.: |
16/993638 |
Filed: |
August 14, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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PCT/IB2019/054706 |
Jun 6, 2019 |
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16993638 |
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62682150 |
Jun 7, 2018 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04N 19/44 20141101;
H04N 19/184 20141101; H04N 19/119 20141101; H04N 19/513
20141101 |
International
Class: |
H04N 19/513 20060101
H04N019/513; H04N 19/44 20060101 H04N019/44; H04N 19/119 20060101
H04N019/119; H04N 19/184 20060101 H04N019/184 |
Claims
1. A method of coding video data, comprising: deriving motion
information for a current video block; dividing, based on a size of
the current video block, the current video block into one or
multiple sub-blocks; deriving, based on the motion information
derived for the current video block, a refined motion information
for each sub-block of the one or multiple sub-block using a decoder
motion vector refinement (DMVR) tool; and coding the current video
block using the refined motion information.
2. The method of claim 1, wherein dividing comprises: dividing a
current video block into multiple subblocks in height to make a
height of the subblock being equal to a predefined height, if a
height of the current video block is an integral multiple of the
predefined height; and dividing a current video block into multiple
subblocks in width to make a width of the subblock being equal to a
predefined width, if a width of the current video block is an
integral multiple of the predefined width.
3. The method of claim 2, wherein at least one of the predefined
height and the predefined width is 16.
4. The method of claim 1, wherein a width of the subblock is less
than or equal to 16 and a height of the sub-block is less than or
equal to 16.
5. The method of claim 1, a luma component is used to derive the
refined motion information using the DMVR tool.
6. The method of claim 1, wherein the multiple blocks have a same
size.
7. The method of claim 1, wherein the deriving a refined motion
information for each sub-block further comprises: determining,
based on the motion information of the current video block, at
least one region in a reference picture for the each sub-block;
calculating at least one matching cost based on the at least one
region; determining the refined motion information for the each
sub-block based on the matching cost.
8. The method of claim 7, the calculating comprises: using one row
of every N rows in each of the at least one region to calculate the
at least one matching cost.
9. The method of claim 8, wherein the one row is the first row of
the every N rows.
10. The method of claim 8, wherein N is equal to 2.
11. The method of claim 1, wherein the coding comprises decoding
the current video block from bitstream.
12. The method of claim 1, wherein the coding comprises coding the
current video block into bitstream.
13. An apparatus for coding video data comprising a processor and a
non-transitory memory with instructions thereon, wherein the
instructions upon execution by the processor, cause the processor
to: derive motion information for a current video block; divide,
based on a size of the current video block, the current video block
into one or multiple sub-blocks; derive, based on the motion
information derived for the current video block, a refined motion
information for each sub-block of the one or multiple sub-block
using a decoder motion vector refinement (DMVR) tool; and code the
current video block using the refined motion information.
14. The apparatus of claim 13, wherein the instructions upon
execution by the processor, cause the processor to: divide a
current video block into multiple subblocks in height to make a
height of the subblock being equal to a predefined height, if a
height of the current video block is an integral multiple of the
predefined height; and divide a current video block into multiple
subblocks in width to make a width of the subblock being equal to a
predefined width, if a width of the current video block is an
integral multiple of the predefined width.
15. The apparatus of claim 14, wherein at least one of the
predefined height and the predefined width is 16.
16. The apparatus of claim 13, wherein a width of the subblock is
less than or equal to 16 and a height of the sub-block is less than
or equal to 16.
17. The apparatus of claim 13, wherein the instructions upon
execution by the processor further cause the processor to:
determine, based on the motion information of the current video
block, at least one region in a reference picture for the each
sub-block; calculate at least one matching cost based on the at
least one region; determine the refined motion information for the
each sub-block based on the matching cost.
18. The apparatus of claim 17, wherein the instructions upon
execution by the processor further cause the processor to: use one
row of every N rows in each of the at least one region to calculate
the at least one matching cost, wherein N is an integer and is
equal to or larger than 2.
19. The apparatus of claim 18, wherein the one row is the first row
of the every N rows.
20. A non-transitory computer-readable storage medium storing
instructions that cause a processor to: derive motion information
for a current video block; divide, based on a size of the current
video block, the current video block into one or multiple
sub-blocks; derive, based on the motion information derived for the
current video block, a refined motion information for each
sub-block of the one or multiple sub-block using a decoder motion
vector refinement (DMVR) tool; and code the current video block
using the refined motion information.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of International
Application No. PCT/IB2019/054706 file on Jun. 6, 2019, which
claims the priority to and benefits of U.S. Provisional Patent
Application No. 62/682,150, filed on Jun. 7, 2018. All of the
aforementioned patent applications are hereby incorporated by
reference in their entireties.
TECHNICAL FIELD
[0002] This document is related to video coding technologies.
BACKGROUND
[0003] In spite of the advances in video compression, digital video
still accounts for the largest bandwidth use on the internet and
other digital communication networks. As the number of connected
user devices capable of receiving and displaying video increases,
it is expected that the bandwidth demand for digital video usage
will continue to grow.
SUMMARY
[0004] Techniques related to decoder side motion vector derivation
(DMVD) in video coding are disclosed. It may be applied to the
existing video coding standard like HEVC, or the standard
(Versatile Video Coding) to be finalized. It may be also applicable
to future video coding standards or video codec.
[0005] In one example aspect, a method of decoding a bitstream
comprising a digital representation of a video is disclosed. The
method includes decoding motion information for a current video
block from the bitstream, estimating matching costs of the current
video block using one or more templates based on a partial set of
pixel locations in the current block, where in each of the one or
more templates includes a video block with multiple samples and
refining the motion information of the current block using a
template having a minimum matching cost. The method further
includes estimating matching costs of the current video block to be
performed by dividing the current video block into sub-blocks and
estimating matching cost for each sub-block using a corresponding
partial set of pixel locations for that sub-block.
[0006] In another example aspect, an apparatus comprising a
processor configured to implement each of the above-described
methods is disclosed.
[0007] In yet another example aspect, these methods may be embodied
in the form of computer-executable instructions and stored on a
computer readable program medium.
[0008] These, and other, aspects are further described in the
present document.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] FIG. 1 shows an example of a derivation process for merge
candidates list construction.
[0010] FIG. 2 shows example positions of spatial merge
candidates.
[0011] FIG. 3 shows examples of candidate pairs considered for
redundancy check of spatial merge candidates.
[0012] FIG. 4A and FIG. 4B show example positions for the second PU
of N.times.2N and 2N.times.N partitions.
[0013] FIG. 5 is an example illustration of motion vector scaling
for temporal merge candidate.
[0014] FIG. 6 shows examples of candidate positions for temporal
merge candidate, C0 and C1.
[0015] FIG. 7 shows an example of combined bi-predictive merge
candidate.
[0016] FIG. 8 shows an example derivation process for motion vector
prediction candidates.
[0017] FIG. 9 shows an example illustration of motion vector
scaling for spatial motion vector candidate.
[0018] FIG. 10 shows an example of Bilateral matching.
[0019] FIG. 11 shows an example of Template matching.
[0020] FIG. 12 shows an example of Unilateral ME in FRUC.
[0021] FIG. 13 shows an example of DMVR based on bilateral template
matching.
[0022] FIG. 14 shows an example of a simplified template in
template matching.
[0023] FIG. 15 shows an example of ATMVP motion prediction for a
CU.
[0024] FIG. 16 shows an example of one CU with four sub-blocks
(A-D) and its neighboring blocks (a-d).
[0025] FIG. 17 is an illustration of sub-blocks where OBMC
applies.
[0026] FIG. 18 shows an example of neighboring samples used for
deriving IC parameters.
[0027] FIG. 19 is a flowchart for an example method of video
decoding.
[0028] FIG. 20 is a block diagram of a video decoding
apparatus.
[0029] FIG. 21 shows an example implementation of a video
encoder.
DETAILED DESCRIPTION
[0030] The present document provides various techniques that can be
used by a decoder of video bitstreams to improve the quality of
decompressed or decoded digital video. Furthermore, a video encoder
may also implement these techniques during the process of encoding
in order to reconstruct decoded frames used for further
encoding.
[0031] Section headings are used in the present document for ease
of understanding and do not limit the embodiments and techniques to
the corresponding sections. As such, embodiments from one section
can be combined with embodiments from other sections. Furthermore,
while some embodiments describe video coding steps in detail, it
will be understood that corresponding steps decoding that undo the
coding will be implemented by a decoder. Furthermore, the term
video processing encompasses video coding or compression, video
decoding or decompression and video transcoding in which video
pixels are represented from one compressed format into another
compressed format or at a different compressed bitrate.
1. Technical Framework
[0032] Video coding standards have evolved primarily through the
development of the well-known ITU-T and ISO/IEC standards. The
ITU-T produced H.261 and H.263, ISO/IEC produced MPEG-1 and MPEG-4
Visual, and the two organizations jointly produced the H.262/MPEG-2
Video and H.264/MPEG-4 Advanced Video Coding (AVC) and H.265/HEVC
standards. Since H.262, the video coding standards are based on the
hybrid video coding structure wherein temporal prediction plus
transform coding are utilized. To explore the future video coding
technologies beyond HEVC, Joint Video Exploration Team (JVET) was
founded by VCEG and MPEG jointly in 2015. Since then, many new
methods have been adopted by JVET and put into the reference
software named Joint Exploration Model (JEM). In April 2018, the
Joint Video Expert Team (JVET) between VCEG (Q6/16) and ISO/IEC
JTC1 SC29/WG11 (MPEG) was created to work on the VVC standard
targeting at 50% bitrate reduction compared to HEVC.
2. Inter Prediction in HEVC/H.265
[0033] Each inter-predicted Prediction Unit (PU) has motion
parameters for one or two reference picture lists. Motion
parameters include a motion vector and a reference picture index.
Usage of one of the two reference picture lists may also be
signaled using inter_pred_idc. Motion vectors may be explicitly
coded as deltas relative to predictors.
[0034] When a Coding Unit (CU) is coded with skip mode, one PU is
associated with the CU, and there are no significant residual
coefficients, no coded motion vector delta or reference picture
index. A merge mode is specified whereby the motion parameters for
the current PU are obtained from neighboring PUs, including spatial
and temporal candidates. The merge mode can be applied to any
inter-predicted PU, not only for skip mode. The alternative to
merge mode is the explicit transmission of motion parameters, where
motion vector (to be more precise, motion vector difference
compared to a motion vector predictor), corresponding reference
picture index for each reference picture list and reference picture
list usage are signaled explicitly per each PU. Such a mode is
named Advanced Motion Vector Prediction (AMVP) in this
document.
[0035] When signaling indicates that one of the two reference
picture lists is to be used, the PU is produced from one block of
samples. This is referred to as `uni-prediction`. Uni-prediction is
available both for P-slices and B-slices.
[0036] When signaling indicates that both of the reference picture
lists are to be used, the PU is produced from two blocks of
samples. This is referred to as `bi-prediction`. B.sub.1-prediction
is available for B-slices only.
[0037] The following text provides the details on the inter
prediction modes specified in HEVC. The description will start with
the merge mode.
2.1.1. Merge Mode
2.1.1.1. Derivation of Candidates for Merge Mode
[0038] When a PU is predicted using merge mode, an index pointing
to an entry in the merge candidates list is parsed from the
bitstream and used to retrieve the motion information. The
construction of this list is specified in the HEVC standard and can
be summarized according to the following sequence of steps: [0039]
Step 1: Initial candidates derivation [0040] Step 1.1: Spatial
candidates derivation [0041] Step 1.2: Redundancy check for spatial
candidates [0042] Step 1.3: Temporal candidates derivation [0043]
Step 2: Additional candidates insertion [0044] Step 2.1: Creation
of bi-predictive candidates [0045] Step 2.2: Insertion of zero
motion candidates
[0046] These steps are also schematically depicted in FIG. 1. For
spatial merge candidate derivation, a maximum of four merge
candidates are selected among candidates that are located in five
different positions. For temporal merge candidate derivation, a
maximum of one merge candidate is selected among two candidates.
Since constant number of candidates for each PU is assumed at
decoder, additional candidates are generated when the number of
candidates obtained from step 1 does not reach the maximum number
of merge candidate (MaxNumMergeCand) which is signalled in slice
header. Since the number of candidates is constant, index of best
merge candidate is encoded using truncated unary binarization (TU).
If the size of CU is equal to 8, all the PUs of the current CU
share a single merge candidate list, which is identical to the
merge candidate list of the 2N.times.2N prediction unit.
[0047] In the following, the operations associated with the
aforementioned steps are detailed.
2.1.1.2. Spatial Candidate Derivation
[0048] In the derivation of spatial merge candidates, a maximum of
four merge candidates are selected among candidates located in the
positions depicted in FIG. 2. The order of derivation is A.sub.1,
B.sub.1, B.sub.0, A.sub.0 and B.sub.2. Position B.sub.2 is
considered only when any PU of position A.sub.1, B.sub.1, B.sub.0,
A.sub.0 is not available (e.g. because it belongs to another slice
or tile) or is intra coded. After candidate at position A.sub.1 is
added, the addition of the remaining candidates is subject to a
redundancy check which ensures that candidates with same motion
information are excluded from the list so that coding efficiency is
improved. To reduce computational complexity, not all possible
candidate pairs are considered in the mentioned redundancy check.
Instead only the pairs linked with an arrow in FIG. 3 are
considered and a candidate is only added to the list if the
corresponding candidate used for redundancy check has not the same
motion information. Another source of duplicate motion information
is the "second PU" associated with partitions different from
2N.times.2N. As an example, FIG. 4 depicts the second PU for the
case of N.times.2N and 2N.times.N, respectively. When the current
PU is partitioned as N.times.2N, candidate at position A.sub.1 is
not considered for list construction. In fact, by adding this
candidate will lead to two prediction units having the same motion
information, which is redundant to just have one PU in a coding
unit. Similarly, position B.sub.1 is not considered when the
current PU is partitioned as 2N.times.N.
2.1.1.3. Temporal Candidate Derivation
[0049] In this step, only one candidate is added to the list.
Particularly, in the derivation of this temporal merge candidate, a
scaled motion vector is derived based on co-located PU belonging to
the picture which has the smallest POC difference with current
picture within the given reference picture list. The reference
picture list to be used for derivation of the co-located PU is
explicitly signalled in the slice header. The scaled motion vector
for temporal merge candidate is obtained as illustrated by the
dotted line in FIG. 5, which is scaled from the motion vector of
the co-located PU using the POC distances, tb and td, where tb is
defined to be the POC difference between the reference picture of
the current picture and the current picture and td is defined to be
the POC difference between the reference picture of the co-located
picture and the co-located picture. The reference picture index of
temporal merge candidate is set equal to zero. A practical
realization of the scaling process is described in the HEVC
specification. For a B-slice, two motion vectors, one is for
reference picture list 0 and the other is for reference picture
list 1, are obtained and combined to make the bi-predictive merge
candidate.
[0050] In the co-located PU (Y) belonging to the reference frame,
the position for the temporal candidate is selected between
candidates C.sub.0 and C.sub.1, as depicted in FIG. 6. If PU at
position C.sub.0 is not available, is intra coded, or is outside of
the current CTU row, position C.sub.1 is used. Otherwise, position
C.sub.0 is used in the derivation of the temporal merge
candidate.
2.1.1.4. Additional Candidate Insertion
[0051] Besides spatial and temporal merge candidates, there are two
additional types of merge candidates: combined bi-predictive merge
candidate and zero merge candidate. Combined bi-predictive merge
candidates are generated by utilizing spatial and temporal merge
candidates. Combined bi-predictive merge candidate is used for
B-Slice only. The combined bi-predictive candidates are generated
by combining the first reference picture list motion parameters of
an initial candidate with the second reference picture list motion
parameters of another. If these two tuples provide different motion
hypotheses, they will form a new bi-predictive candidate. As an
example, FIG. 7 depicts the case when two candidates in the
original list (on the left), which have mvL0 and refIdxL0 or mvL1
and refIdxL1, are used to create a combined bi-predictive merge
candidate added to the final list (on the right). There are
numerous rules regarding the combinations which are considered to
generate these additional merge candidates, defined in the HEVC
standard.
[0052] Zero motion candidates are inserted to fill the remaining
entries in the merge candidates list and therefore hit the
MaxNumMergeCand capacity. These candidates have zero spatial
displacement and a reference picture index which starts from zero
and increases every time a new zero motion candidate is added to
the list. The number of reference frames used by these candidates
is one and two for uni and bi-directional prediction, respectively.
Finally, no redundancy check is performed on these candidates.
2.1.1.5. Motion Estimation Regions for Parallel Processing
[0053] To speed up the encoding process, motion estimation can be
performed in parallel whereby the motion vectors for all prediction
units inside a given region are derived simultaneously. The
derivation of merge candidates from spatial neighbourhood may
interfere with parallel processing as one prediction unit cannot
derive the motion parameters from an adjacent PU until its
associated motion estimation is completed. To mitigate the
trade-off between coding efficiency and processing latency, HEVC
defines the motion estimation region (MER) whose size is signalled
in the picture parameter set using the "log
2_parallel_merge_level_minus2" syntax element of HEVC. When a MER
is defined, merge candidates falling in the same region are marked
as unavailable and therefore not considered in the list
construction.
2.1.2. AMVP
[0054] AMVP exploits spatio-temporal correlation of motion vector
with neighbouring PUs, which is used for explicit transmission of
motion parameters. For each reference picture list, a motion vector
candidate list is constructed by firstly checking availability of
left, above temporally neighbouring PU positions, removing
redundant candidates and adding zero vector to make the candidate
list to be constant length. Then, the encoder can select the best
predictor from the candidate list and transmit the corresponding
index indicating the chosen candidate. Similarly with merge index
signalling, the index of the best motion vector candidate is
encoded using truncated unary. The maximum value to be encoded in
this case is 2 (see FIG. 8). In the following sections, details
about derivation process of motion vector prediction candidate are
provided.
2.1.2.1. Derivation of AMVP Candidates
[0055] FIG. 8 summarizes derivation process for motion vector
prediction candidate.
[0056] In motion vector prediction, two types of motion vector
candidates are considered: spatial motion vector candidate and
temporal motion vector candidate. For spatial motion vector
candidate derivation, two motion vector candidates are eventually
derived based on motion vectors of each PU located in five
different positions as depicted in FIG. 2.
[0057] For temporal motion vector candidate derivation, one motion
vector candidate is selected from two candidates, which are derived
based on two different co-located positions. After the first list
of spatio-temporal candidates is made, duplicated motion vector
candidates in the list are removed. If the number of potential
candidates is larger than two, motion vector candidates whose
reference picture index within the associated reference picture
list is larger than 1 are removed from the list. If the number of
spatio-temporal motion vector candidates is smaller than two,
additional zero motion vector candidates is added to the list.
2.1.2.2. Spatial Motion Vector Candidates
[0058] In the derivation of spatial motion vector candidates, a
maximum of two candidates are considered among five potential
candidates, which are derived from PUs located in positions as
depicted in FIG. 2, those positions being the same as those of
motion merge. The order of derivation for the left side of the
current PU is defined as A.sub.0, A.sub.1, and scaled A.sub.0,
scaled A.sub.1. The order of derivation for the above side of the
current PU is defined as B.sub.0, B.sub.1, B.sub.2, scaled B.sub.0,
scaled B.sub.1, scaled B.sub.2. For each side there are therefore
four cases that can be used as motion vector candidate, with two
cases not required to use spatial scaling, and two cases where
spatial scaling is used. The four different cases are summarized as
follows. [0059] No spatial scaling [0060] (1) Same reference
picture list, and same reference picture index (same POC) [0061]
(2) Different reference picture list, but same reference picture
(same POC) [0062] Spatial scaling [0063] (3) Same reference picture
list, but different reference picture (different POC) [0064] (4)
Different reference picture list, and different reference picture
(different POC)
[0065] The no-spatial-scaling cases are checked first followed by
the spatial scaling. Spatial scaling is considered when the POC is
different between the reference picture of the neighbouring PU and
that of the current PU regardless of reference picture list. If all
PUs of left candidates are not available or are intra coded,
scaling for the above motion vector is allowed to help parallel
derivation of left and above MV candidates. Otherwise, spatial
scaling is not allowed for the above motion vector.
[0066] In a spatial scaling process, the motion vector of the
neighbouring PU is scaled in a similar manner as for temporal
scaling, as depicted as FIG. 9. The main difference is that the
reference picture list and index of current PU is given as input;
the actual scaling process is the same as that of temporal
scaling.
2.1.2.3. Temporal Motion Vector Candidates
[0067] Apart for the reference picture index derivation, all
processes for the derivation of temporal merge candidates are the
same as for the derivation of spatial motion vector candidates (see
FIG. 6). The reference picture index is signalled to the
decoder.
2.2. New Inter Prediction Methods in JEM
2.2.1. Pattern Matched Motion Vector Derivation
[0068] Pattern matched motion vector derivation (PMMVD) mode is a
special merge mode based on Frame-Rate Up Conversion (FRUC)
techniques. With this mode, motion information of a block is not
signalled but derived at decoder side.
[0069] A FRUC flag is signalled for a CU when its merge flag is
true. When the FRUC flag is false, a merge index is signalled and
the regular merge mode is used. When the FRUC flag is true, an
additional FRUC mode flag is signalled to indicate which method
(bilateral matching or template matching) is to be used to derive
motion information for the block.
[0070] At encoder side, the decision on whether using FRUC merge
mode for a CU is based on RD cost selection as done for normal
merge candidate. That is the two matching modes (bilateral matching
and template matching) are both checked for a CU by using RD cost
selection. The one leading to the minimal cost is further compared
to other CU modes. If a FRUC matching mode is the most efficient
one, FRUC flag is set to true for the CU and the related matching
mode is used.
[0071] Motion derivation process in FRUC merge mode has two steps.
A CU-level motion search is first performed, then followed by a
Sub-CU level motion refinement. At CU level, an initial motion
vector is derived for the whole CU based on bilateral matching or
template matching. First, a list of MV candidates is generated and
the candidate which leads to the minimum matching cost is selected
as the starting point for further CU level refinement. Then a local
search based on bilateral matching or template matching around the
starting point is performed and the MV results in the minimum
matching cost is taken as the MV for the whole CU. Subsequently,
the motion information is further refined at sub-CU level with the
derived CU motion vectors as the starting points.
[0072] For example, the following derivation process is performed
for a W.times.H CU motion information derivation. At the first
stage, MV for the whole W.times.H CU is derived. At the second
stage, the CU is further split into M.times.M sub-CUs. The value of
M is calculated as in (16), D is a predefined splitting depth which
is set to 3 by default in the JEM. Then the MV for each sub-CU is
derived.
M = max { 4 , min { M 2 D , N 2 D } } ( 3 ) ##EQU00001##
[0073] As shown in the FIG. 10, the bilateral matching is used to
derive motion information of the current CU by finding the closest
match between two blocks along the motion trajectory of the current
CU in two different reference pictures. Under the assumption of
continuous motion trajectory, the motion vectors MV0 and MV1
pointing to the two reference blocks shall be proportional to the
temporal distances, i.e., TD0 and TD1, between the current picture
and the two reference pictures. As a special case, when the current
picture is temporally between the two reference pictures and the
temporal distance from the current picture to the two reference
pictures is the same, the bilateral matching becomes mirror based
bi-directional MV.
[0074] As shown in FIG. 11, template matching is used to derive
motion information of the current CU by finding the closest match
between a template (top and/or left neighbouring blocks of the
current CU) in the current picture and a block (same size to the
template) in a reference picture. Except the aforementioned FRUC
merge mode, the template matching is also applied to AMVP mode. In
the JEM, as done in HEVC, AMVP has two candidates. With template
matching method, a new candidate is derived. If the newly derived
candidate by template matching is different to the first existing
AMVP candidate, it is inserted at the very beginning of the AMVP
candidate list and then the list size is set to two (meaning remove
the second existing AMVP candidate). When applied to AMVP mode,
only CU level search is applied.
2.2.2. CU Level MV Candidate Set
[0075] The MV candidate set at CU level consists of: [0076] (i)
Original AMVP candidates if the current CU is in AMVP mode [0077]
(ii) all merge candidates, [0078] (iii) several MVs in the
interpolated MV field, which is introduced in section 2.2.7.3.
[0079] (iv) top and left neighbouring motion vectors
[0080] When using bilateral matching, each valid MV of a merge
candidate is used as an input to generate a MV pair with the
assumption of bilateral matching. For example, one valid MV of a
merge candidate is (MVa, refa) at reference list A. Then the
reference picture refb of its paired bilateral MV is found in the
other reference list B so that refa and refb are temporally at
different sides of the current picture. If such a refb is not
available in reference list B, refb is determined as a reference
which is different from refa and its temporal distance to the
current picture is the minimal one in list B. After refb is
determined, MVb is derived by scaling MVa based on the temporal
distance between the current picture and refa, refb.
[0081] Four MVs from the interpolated MV field are also added to
the CU level candidate list. More specifically, the interpolated
MVs at the position (0, 0), (W/2, 0), (0, H/2) and (W/2, H/2) of
the current CU are added.
[0082] When FRUC is applied in AMVP mode, the original AMVP
candidates are also added to CU level MV candidate set.
[0083] At the CU level, up to 15 MVs for AMVP CUs and up to 13 MVs
for merge CUs are added to the candidate list.
2.2.3. Sub-CU Level MV Candidate Set
[0084] The MV candidate set at sub-CU level consists of:
[0085] (i) an MV determined from a CU-level search,
[0086] (ii) top, left, top-left and top-right neighbouring MVs,
[0087] (iii) scaled versions of collocated MVs from reference
pictures,
[0088] (iv) up to 4 ATMVP candidates,
[0089] (v) up to 4 STMVP candidates
[0090] The scaled MVs from reference pictures are derived as
follows. All the reference pictures in both lists are traversed.
The MVs at a collocated position of the sub-CU in a reference
picture are scaled to the reference of the starting CU-level
MV.
[0091] ATMVP and STMVP candidates are limited to the four first
ones.
[0092] At the sub-CU level, up to 17 MVs are added to the candidate
list.
2.2.4. Generation of Interpolated MV Field
[0093] Before coding a frame, interpolated motion field is
generated for the whole picture based on unilateral ME. Then the
motion field may be used later as CU level or sub-CU level MV
candidates.
[0094] First, the motion field of each reference pictures in both
reference lists is traversed at 4.times.4 block level. For each
4.times.4 block, if the motion associated to the block passing
through a 4.times.4 block in the current picture (as shown in FIG.
13) and the block has not been assigned any interpolated motion,
the motion of the reference block is scaled to the current picture
according to the temporal distance TD0 and TD1 (the same way as
that of MV scaling of TMVP in HEVC) and the scaled motion is
assigned to the block in the current frame. If no scaled MV is
assigned to a 4.times.4 block, the block's motion is marked as
unavailable in the interpolated motion field.
2.2.5. Interpolation and Matching Cost
[0095] When a motion vector points to a fractional sample position,
motion compensated interpolation is needed. To reduce complexity,
bi-linear interpolation instead of regular 8-tap HEVC interpolation
is used for both bilateral matching and template matching.
[0096] The calculation of matching cost is a bit different at
different steps. When selecting the candidate from the candidate
set at the CU level, the matching cost is the absolute sum
difference (SAD) of bilateral matching or template matching. After
the starting MV is determined, the matching cost C of bilateral
matching at sub-CU level search is calculated as follows:
C=SAD+w(|MV.sub.x-MV.sub.x.sup.s|+|MV.sub.y-MV.sub.y.sup.s|)
(4)
where w is a weighting factor which is empirically set to 4, MV and
MV.sup.S indicate the current MV and the starting MV, respectively.
SAD is still used as the matching cost of template matching at
sub-CU level search.
[0097] In FRUC mode, MV is derived by using luma samples only. The
derived motion will be used for both luma and chroma for MC inter
prediction. After MV is decided, final MC is performed using 8-taps
interpolation filter for luma and 4-taps interpolation filter for
chroma.
2.2.6. MV Refinement
[0098] MV refinement is a pattern based MV search with the
criterion of bilateral matching cost or template matching cost. In
the JEM, two search patterns are supported--an unrestricted
center-biased diamond search (UCBDS) and an adaptive cross search
for MV refinement at the CU level and sub-CU level, respectively.
For both CU and sub-CU level MV refinement, the MV is directly
searched at quarter luma sample MV accuracy, and this is followed
by one-eighth luma sample MV refinement. The search range of MV
refinement for the CU and sub-CU step are set equal to 8 luma
samples.
2.2.7. Selection of Prediction Direction in Template Matching FRUC
Merge Mode
[0099] In the bilateral matching merge mode, bi-prediction is
always applied since the motion information of a CU is derived
based on the closest match between two blocks along the motion
trajectory of the current CU in two different reference pictures.
There is no such limitation for the template matching merge mode.
In the template matching merge mode, the encoder can choose among
uni-prediction from list0, uni-prediction from list1 or
bi-prediction for a CU. The selection is based on a template
matching cost as follows: [0100] If costBi<=factor*min (cost0,
cost1) [0101] bi-prediction is used; [0102] Otherwise, if
cost0<=cost1 [0103] uni-prediction from list0 is used; [0104]
Otherwise, [0105] uni-prediction from list1 is used; where cost0 is
the SAD of list0 template matching, cost1 is the SAD of list1
template matching and costBi is the SAD of bi-prediction template
matching. The value of factor is equal to 1.25, which means that
the selection process is biased toward bi-prediction.
[0106] The inter prediction direction selection is only applied to
the CU-level template matching process.
2.2.8. Decoder-Side Motion Vector Refinement
[0107] In bi-prediction operation, for the prediction of one block
region, two prediction blocks, formed using a motion vector (MV) of
list0 and a MV of list1, respectively, are combined to form a
single prediction signal. In the decoder-side motion vector
refinement (DMVR) method, the two motion vectors of the
bi-prediction are further refined by a bilateral template matching
process. The bilateral template matching applied in the decoder to
perform a distortion-based search between a bilateral template and
the reconstruction samples in the reference pictures in order to
obtain a refined MV without transmission of additional motion
information.
[0108] In DMVR, a bilateral template is generated as the weighted
combination (i.e. average) of the two prediction blocks, from the
initial MV0 of list0 and MV1 of list1, respectively, as shown in
FIG. 13. The template matching operation consists of calculating
cost measures between the generated template and the sample region
(around the initial prediction block) in the reference picture. For
each of the two reference pictures, the MV that yields the minimum
template cost is considered as the updated MV of that list to
replace the original one. In the JEM, nine MV candidates are
searched for each list. The nine MV candidates include the original
MV and 8 surrounding MVs with one luma sample offset to the
original MV in either the horizontal or vertical direction, or
both. Finally, the two new MVs, i.e., MV0' and MV1' as shown in
FIG. 13, are used for generating the final bi-prediction results. A
sum of absolute differences (SAD) is used as the cost measure.
Please note that when calculating the cost of a prediction block
generated by one surrounding MV, the rounded MV (to integer pel) is
actually used to obtain the prediction block instead of the real
MV.
[0109] DMVR is applied for the merge mode of bi-prediction with one
MV from a reference picture in the past and another from a
reference picture in the future, without the transmission of
additional syntax elements. In the JEM, when LIC, affine motion,
FRUC, or sub-CU merge candidate is enabled for a CU, DMVR is not
applied.
2.2.9. Examples of Problems
[0110] DMVD methods like DMVR and FRUC perform motion estimation to
derive or refine the motion information, which is very complex for
the decoder. During motion estimation, they share one common
problem: difference (absolute difference, square difference etc.)
between template and candidate block is calculated for all pixels
in the block and added up, and is then used to select the best
matching block. This is not necessary because difference of partial
pixels may be good enough for selecting the best candidate block or
MV. Meanwhile, usually only luma component is used in derivation or
refinement of motion vectors, and chroma components are not
considered.
[0111] For DMVR, it has another complexity issue: it performs
motion compensation twice, one for generating the template, and one
for generating the final prediction block. As a result, for each
reference picture list (i.e., prediction direction), it performs
both horizonal interpolation and vertical interpolation twice, in
case that the initial MV and the refined MV only have fractional
components. This increases the worst-case complexity dramatically.
Meanwhile, DMVR only works in merge mode and cannot work in AMVP
mode. In MV refinement, it takes signaled MV (derived MV from a
merge candidate) as the starting MV, and checks its surrounding
MVs. However, MV precision of the signaled MV is not considered. In
AMVR, low precision MV maybe selected. For example, suppose the
highest allowable MV precision is 1/4 pel, in AMVR, a 4 pel or 1
pel MV may be used. In this case, DMVR can be used to refine the MV
precision. Unlike FRUC which can be applied at sub-block level,
DMVR is performed at block level except for the ATMVP and STMVP
case, which may lead to coding performance loss.
[0112] For FURC, when performing the bilateral matching, it
considers the MV difference between the starting MV and the
candidate MV to suppress unreliable motion vectors, as in Eq. 4.
The MV difference is multiplied by a fixed weighting factor, which
may be unreasonable. For larger blocks, the SAD plays a dominant
role and the MV difference is neglectable, and for smaller blocks,
the MV difference may be too large.
2.2.10. Example Embodiments
[0113] We propose several aspects to reduce the complexity and
improve the coding performance of DMVD methods. The disclosed
methods could be applied to existing DMVD methods, but also to
future methods for motion/mode derivation at decoder side.
[0114] First, the cost (e.g., difference, distortion or the cost
considering both distortion and MV) between template and a
candidate block is calculated only for partial pixels in the
decoder side motion estimation, i.e., in motion information
derivation or refinement procedure. Second, for DMVR, the
interpolation times is reduced. Third, some embodiments that use
the disclosed techniques apply DMVR to AMVP mode. Fourth, weighting
factor of MV difference can be different for different block
sizes.
[0115] The following listing of examples provides some ways by
which the disclosed techniques can be embodied into a video
decoding process.
[0116] Denote prec as the motion vector precision, when prec is
equal to N, it means the motion vector is with 1/2{circumflex over
( )}N pel precision. N can be positive integers, zero, or negative
integers. [0117] 1. The cost (e.g., difference) between the
template and candidate blocks is calculated only for partially
selected rows in motion information derivation or refinement
procedure. [0118] a. In one example, selected rows are defined as
all of the i.sup.th rows of every N rows, where N>1 and
1<=i<=N. For example, N is equal to 2 and i is equal to 1.
[0119] b. In one example, for each group with N rows, certain rows
within the group are used as the selected rows. For example, the
first row and the second row of every 4 rows are utilized. [0120]
c. In one example, cost is calculated for arbitrarily selected rows
of the block, e.g., the first row and the last row, or the first
two rows and last two rows. [0121] d. Same rule can be applied to
all block sizes when selecting partial rows. Alternatively,
different rules can be applied to different block sizes and/or
block shapes (e.g., square or rectangular or ratios between block
width and block height). [0122] i. In one example, during the cost
calculation, more rows are skipped for larger block size and vice
versa. E.g., difference is calculated for the first row of every 2
rows when the block size is smaller than 16.times.16 (i.e.,
width*height<16*16), but is calculated for the first row of
every 4 rows for other block sizes. [0123] ii. In one example,
during the cost calculation, more rows are skipped for block shapes
with larger height and vice versa. E.g., cost is calculated for the
first row of every 2 rows when height of the block is smaller than
16, but is calculated for the first row of every 4 rows for other
block sizes. [0124] iii. In one example, such simplification is
only applied to one or several smallest block sizes (i.e., smallest
width*height) to suppress the worst-case complexity. For example,
the simplification is only applied to blocks with areas smaller
than 8.times.8. [0125] iv. In one example, such simplification is
only applied to one or several largest block sizes. For example,
the simplification is only applied to blocks with areas larger than
32.times.32. [0126] v. In one example, such simplification is only
applied to one or several block shapes with largest block heights
or width. [0127] vi. In one example, such simplification is only
applied some selected block shapes. [0128] 2. For each row of a
block or each selected row of a block, cost is calculated for all
columns or only partial columns. [0129] a. In one example, the cost
is calculated for M continuous columns (can started at any valid
column Y) of every T columns, where T>0, 1<=M<=T,
1<=Y<=T-M+1. For example, T=8, M=4 and Y=1. [0130] b. In one
example, the cost is calculated for M selected columns of every T
columns. [0131] c. In one example, the cost is calculated for M
arbitrarily selected columns of the row (e.g., the first K columns
and the last L columns). [0132] d. Same rule can be applied to all
block sizes when selecting partial columns. Alternatively,
different rules can be applied to different block sizes and/or
block shapes (e.g., square or rectangular or ratios between block
width and block height). [0133] i. In one example, during the cost
calculation, more columns are skipped for larger block size and
vice versa. E.g., difference is calculated for the first 4 columns
of every 8 columns when the block size is smaller than 16.times.16,
but is calculated for the first 4 columns of every 16 columns for
other block sizes. When the column is smaller than 8 or 16, only
the first 4 columns are used to calculated difference. [0134] ii.
In one example, during the cost calculation, more columns are
skipped for block shape with larger width and vice versa. E.g.,
cost is calculated for the first 4 columns of every 8 columns when
width of the block is smaller than 16, but is calculated for the
first 4 columns of every 16 columns for other block sizes. [0135]
iii. In one example, such simplification is only applied to one or
several smallest block sizes to suppress the worst-case complexity.
[0136] iv. In one example, such simplification is only applied to
one or several largest block sizes. [0137] v. In one example, such
simplification is only applied to one or several block shapes with
largest block widths. [0138] vi. In one example, such
simplification is only applied some selected block shapes. [0139]
3. In DMVR, when generating the template, motion compensation is
performed using integer MV or MV with integer horizonal component
or vertical component instead of using the real MV as in JEM.
[0140] a. In one example, MV (both horizonal component and vertical
component) is rounded to integer precision for both prediction
directions. [0141] b. In one example, MV of one prediction
direction is rounded to integer precision, and MV of the other
prediction direction is not changed. [0142] c. In one example, only
one MV component (either horizonal component or vertical component)
is rounded to integer precision for each prediction direction.
[0143] d. In one example, MV of one prediction direction is rounded
to integer precision, and only one MV component of the other
prediction direction is rounded to integer precision. [0144] e. In
one example, MV of one prediction direction is not changed, and
only one MV component of the other prediction direction is rounded
to integer precision. [0145] f. Denote fmv as the fractional my,
and denote imv as the rounded integer precision mv.
[0145] Denote sign ( x ) as the sign of x , and sign ( x ) = { 1 if
x .gtoreq. 0 - 1 if x < 0 . ##EQU00002## [0146] i.
imv=(fmv+(1<<(prec-1)))>>prec [0147] ii. Alternatively,
imv=fmv>>prec [0148] iii. Alternatively,
imv=(fmv+sign(fmv)*(1<<(prec-1)))>>prec [0149] g. Such
simplification may be applied to all block sizes or only one or
several block sizes and/or certain block shapes. [0150] i. In one
example, it is applied to one or several smallest block sizes, like
4.times.4 in JEM or BMS (benchmark set), or 4.times.8 and 8.times.4
in HEVC. [0151] ii. In one example, it is applied to the one or
several largest block sizes. [0152] iii. In one example, it is
applied to some selected block sizes. [0153] 4. Alternatively, in
DMVR, when generating the template, shorter tap of interpolation
filter (such as bi-linear filter) is used in the motion
compensation. [0154] 5. It is proposed that DMVR is performed in
sub-block level. A block can be split into sub-blocks in different
ways. [0155] a. In one example, all blocks are split into fixed
M.times.N sub-block size, e.g., 4.times.4, or 4.times.8 or
8.times.4 or 8.times.8 or 8.times.16 or 16.times.8 or 16.times.16
etc. When the block width/height is integral multiple of the
sub-block width/height, it is split into sub-blocks; otherwise, it
is not split into sub-blocks. [0156] b. In one example, a block is
split into K sub-blocks with equal size, wherein K>=2. For
example, a M.times.N block is split into 4 (M/2).times.(N/2)
sub-blocks, or 2 (M/2).times.N sub-blocks, or 2 M.times.(N/2)
blocks. [0157] c. In one example, the split method depends on block
sizes or block shapes or other coded information. For example, an
8.times.32 block is split into 4.times.8 sub-blocks and a
32.times.8 block is split into 8.times.4 sub-blocks. [0158] d. In
one example, when generating a template of a sub-block, the derived
motion information of the whole block may be utilized as in current
block-level DMVR. [0159] i. Alternatively, the refined motion
information of neighboring sub-block(s) with or without the derived
motion information of the whole block may be utilized to form the
template. [0160] e. In one example, the searching point of a
sub-block may also consider the refined motion information from
other sub-block(s). [0161] 6. In one example, the template used by
template matching (in PMMVD) only includes pixels above the current
block, excluding the pixels left to the current block, as shown in
FIG. 14. [0162] 7. In existing DMVD methods, only luma component is
considered to derive or refine the motion vectors. It is proposed
to further consider the chroma components. Denote the costs of
three color components of a given motion vector by Ci (wherein i
indicates the color component index). [0163] a. The final cost is
defined as Wi*Ci wherein Wi indicates the weights for the i-th
color component. [0164] b. Alternatively, the final cost is defined
as (W0*C0+W1*(C1+C2)). In some examples, either W0 or W1 is equal
to 1. [0165] c. In one example, when applying DMVR to the chroma
components, rounding of motion vectors may be applied so that
integer motion vectors may be utilized and there is no need to
apply interpolation for chroma components. [0166] d. In one
example, when applying DMVR to the chroma components, if
interpolation is required, shorter tap of interpolation filter
(such as bi-linear filter) may be applied. [0167] 8. The above
methods may be applied to certain color component, or all color
components. [0168] a. Different rules may be applied to different
color components, or luma and chroma components may utilize
different rules. [0169] b. Alternatively, how and whether to apply
the above methods may be further signaled in sequence parameter
set, picture parameter set, slice header, etc.
[0170] FIG. 19 is a flowchart for an example method 1900 of video
decoding. The method 1900 includes decoding (1902) motion
information for a current video block from the bitstream, dividing
(1904) the current video block into multiple sub-blocks, refining
(1906) the motion information for at least one sub-block of the
multiple sub-blocks and reconstructing (1908) the current video
block using the refined motion information.
[0171] In some embodiments, the refining step is performed by
estimating matching cost for each sub-block using one or more
templates respective to the sub-block. For example, each of the one
or more templates includes a video block with multiple samples. The
refining may include selecting a template having a minimum matching
cost for the respective sub-block.
[0172] In some embodiments, the dividing step includes splitting
the current video block into sub-blocks of only square size.
[0173] In some embodiments, the dividing step includes splitting
the current video block into sub-blocks that are same-sized.
[0174] In some embodiments, the dividing step includes splitting
the current video block into sub-blocks from at least two groups
from a first group of sub-blocks are square sized and a second
group of sub-blocks are vertical rectangular and a third group of
sub-blocks that are horizontal rectangular. In some embodiments,
the splitting is dependent on a coding type of the current video
block.
[0175] Section 2.2.10 provides additional example embodiments and
variations that can be implemented by method 1900.
[0176] FIG. 20 shows a block diagram of an example embodiment of a
hardware device 2000 that can be utilized to implement various
portions of the presently disclosed technology. The hardware device
2000 can be a laptop, a smartphone, a tablet, a camcorder, or other
types of devices that are capable of processing videos. The device
2000 includes a processor or controller 2002 to process data, and
memory 2004 in communication with the processor 2002 to store
and/or buffer data. For example, the processor 2002 can include a
central processing unit (CPU) or a microcontroller unit (MCU). In
some implementations, the processor 2002 can include a
field-programmable gate-array (FPGA). In some implementations, the
device 2000 includes or is in communication with a graphics
processing unit (GPU), video processing unit (VPU) and/or wireless
communications unit for various visual and/or communications data
processing functions of the smartphone device. For example, the
memory 2004 can include and store processor-executable code, which
when executed by the processor 2002, configures the device 2000 to
perform various operations, e.g., such as receiving information,
commands, and/or data, processing information and data, and
transmitting or providing processed information/data to another
device, such as an actuator or external display. To support various
functions of the device 2000, the memory 2004 can store information
and data, such as instructions, software, values, images, and other
data processed or referenced by the processor 2002. For example,
various types of Random Access Memory (RAM) devices, Read Only
Memory (ROM) devices, Flash Memory devices, and other suitable
storage media can be used to implement storage functions of the
memory 2004. The device 2000 may further include dedicated video
processing circuitry 2006 for performing repetitive computational
functions such as transforms and decoding.
[0177] FIG. 21 is a block diagram showing an example implementation
of a video encoder. The video encoder operates on video pictures
that are coded using coding tools such as transform, motion
estimation and coding of residual errors. Encoded video frames are
reconstructed (motion compensated) at the encoder and used as
reference pictures for inter coding of other video pictures. The
techniques described in the present document may be implemented by
the video encoder, or a video decoder, using a hardware platform
such as described with respect to FIG. 20.
[0178] From the foregoing, it will be appreciated that specific
embodiments of the presently disclosed technology have been
described herein for purposes of illustration, but that various
modifications may be made without deviating from the scope of the
invention. Accordingly, the presently disclosed technology is not
limited except as by the appended claims.
[0179] The disclosed and other embodiments, modules and the
functional operations described in this document can be implemented
in digital electronic circuitry, or in computer software, firmware,
or hardware, including the structures disclosed in this document
and their structural equivalents, or in combinations of one or more
of them. The disclosed and other embodiments can be implemented as
one or more computer program products, i.e., one or more modules of
computer program instructions encoded on a computer readable medium
for execution by, or to control the operation of, data processing
apparatus. The computer readable medium can be a machine-readable
storage device, a machine-readable storage substrate, a memory
device, a composition of matter effecting a machine-readable
propagated signal, or a combination of one or more them. The term
"data processing apparatus" encompasses all apparatus, devices, and
machines for processing data, including by way of example a
programmable processor, a computer, or multiple processors or
computers. The apparatus can include, in addition to hardware, code
that creates an execution environment for the computer program in
question, e.g., code that constitutes processor firmware, a
protocol stack, a database management system, an operating system,
or a combination of one or more of them. A propagated signal is an
artificially generated signal, e.g., a machine-generated
electrical, optical, or electromagnetic signal, that is generated
to encode information for transmission to suitable receiver
apparatus.
[0180] A computer program (also known as a program, software,
software application, script, or code) can be written in any form
of programming language, including compiled or interpreted
languages, and it can be deployed in any form, including as a
stand-alone program or as a module, component, subroutine, or other
unit suitable for use in a computing environment. A computer
program does not necessarily correspond to a file in a file system.
A program can be stored in a portion of a file that holds other
programs or data (e.g., one or more scripts stored in a markup
language document), in a single file dedicated to the program in
question, or in multiple coordinated files (e.g., files that store
one or more modules, sub programs, or portions of code). A computer
program can be deployed to be executed on one computer or on
multiple computers that are located at one site or distributed
across multiple sites and interconnected by a communication
network.
[0181] The processes and logic flows described in this document can
be performed by one or more programmable processors executing one
or more computer programs to perform functions by operating on
input data and generating output. The processes and logic flows can
also be performed by, and apparatus can also be implemented as,
special purpose logic circuitry, e.g., an FPGA (field programmable
gate array) or an ASIC (application specific integrated
circuit).
[0182] Processors suitable for the execution of a computer program
include, by way of example, both general and special purpose
microprocessors, and any one or more processors of any kind of
digital computer. Generally, a processor will receive instructions
and data from a read only memory or a random-access memory or both.
The essential elements of a computer are a processor for performing
instructions and one or more memory devices for storing
instructions and data. Generally, a computer will also include, or
be operatively coupled to receive data from or transfer data to, or
both, one or more mass storage devices for storing data, e.g.,
magnetic, magneto optical disks, or optical disks. However, a
computer need not have such devices. Computer readable media
suitable for storing computer program instructions and data include
all forms of non-volatile memory, media and memory devices,
including by way of example semiconductor memory devices, e.g.,
EPROM, EEPROM, and flash memory devices; magnetic disks, e.g.,
internal hard disks or removable disks; magneto optical disks; and
CD ROM and DVD-ROM disks. The processor and the memory can be
supplemented by, or incorporated in, special purpose logic
circuitry.
[0183] While this patent document contains many specifics, these
should not be construed as limitations on the scope of any
invention or of what may be claimed, but rather as descriptions of
features that may be specific to particular embodiments of
particular inventions. Certain features that are described in this
patent document in the context of separate embodiments can also be
implemented in combination in a single embodiment. Conversely,
various features that are described in the context of a single
embodiment can also be implemented in multiple embodiments
separately or in any suitable subcombination. Moreover, although
features may be described above as acting in certain combinations
and even initially claimed as such, one or more features from a
claimed combination can in some cases be excised from the
combination, and the claimed combination may be directed to a
subcombination or variation of a subcombination.
[0184] Similarly, while operations are depicted in the drawings in
a particular order, this should not be understood as requiring that
such operations be performed in the particular order shown or in
sequential order, or that all illustrated operations be performed,
to achieve desirable results. Moreover, the separation of various
system components in the embodiments described in this patent
document should not be understood as requiring such separation in
all embodiments.
[0185] Only a few implementations and examples are described and
other implementations, enhancements and variations can be made
based on what is described and illustrated in this patent
document.
* * * * *