U.S. patent application number 14/171159 was filed with the patent office on 2014-06-05 for mean absolute difference prediction for video encoding rate control.
This patent application is currently assigned to MICROSOFT CORPORATION. The applicant listed for this patent is Microsoft Corporation. Invention is credited to Mei-Hsuan Lu, Tin Qian.
Application Number | 20140153643 14/171159 |
Document ID | / |
Family ID | 46795569 |
Filed Date | 2014-06-05 |
United States Patent
Application |
20140153643 |
Kind Code |
A1 |
Lu; Mei-Hsuan ; et
al. |
June 5, 2014 |
MEAN ABSOLUTE DIFFERENCE PREDICTION FOR VIDEO ENCODING RATE
CONTROL
Abstract
Mean absolute difference (MAD) prediction for video encoding may
be provided. Upon receiving a video stream comprising a plurality
of quality layers, a first quantization parameter (QP) may be
selected for a first frame of the video stream according to a
second QP associated with a second frame and a third QP associated
with a third frame. The first frame may then be encoded according
to the selected first QP.
Inventors: |
Lu; Mei-Hsuan; (Redmond,
WA) ; Qian; Tin; (Bellevue, WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Microsoft Corporation |
Redmond |
WA |
US |
|
|
Assignee: |
MICROSOFT CORPORATION
Redmond
WA
|
Family ID: |
46795569 |
Appl. No.: |
14/171159 |
Filed: |
February 3, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
13044630 |
Mar 10, 2011 |
8644383 |
|
|
14171159 |
|
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Current U.S.
Class: |
375/240.03 ;
375/240.16 |
Current CPC
Class: |
H04N 19/56 20141101;
H04N 19/147 20141101; H04N 19/573 20141101; H04N 19/172 20141101;
H04N 19/146 20141101; H04N 19/126 20141101 |
Class at
Publication: |
375/240.03 ;
375/240.16 |
International
Class: |
H04N 19/146 20060101
H04N019/146; H04N 19/147 20060101 H04N019/147; H04N 19/126 20060101
H04N019/126; H04N 19/51 20060101 H04N019/51; H04N 19/172 20060101
H04N019/172 |
Claims
1. A computer implemented method for providing Mean Absolute
Difference prediction, the method comprising: receiving a plurality
of video frames associated with a bitstream; predicting a mean
absolute difference of a current frame according to a first mean
absolute difference associated with an immediately previous
temporal frame and a second mean absolute difference associated
with a similar frame that has a similar rate distortion
characteristic to the current frame; and encoding the current frame
according to the predicted mean absolute difference.
2. The computer implemented method of claim 1, further comprising
selecting a quantization parameter of the current frame according
to a bandwidth constraint and the predicted mean absolute
difference of the current frame.
3. The computer implemented method of claim 2, wherein the
quantization parameter of the current frame is determined by a
quadratic rate-quantizer model using the predicted mean absolute
difference of the current frame.
4. The computer implemented method of claim 1, further comprising
predicting the mean absolute difference of the current frame
according to a linear regression model, wherein the first mean
absolute difference associated with the immediately previous
temporal frame and the second mean absolute difference associated
with the similar frame comprise regressors associated with the
linear regression model.
5. The computer implemented method of claim 1, wherein the similar
frame is identified by: computing a rate distortion characteristic
of each of the plurality of video frames; and comparing the rate
distortion characteristic of each of the plurality of video frames
to a current rate distortion characteristic of the current
frame.
6. The computer implemented method of claim 1, wherein the
bitstream comprises a base layer and at least one enhancement
layer.
7. The computer implemented method of claim 6, wherein the
immediately previous temporal frame is associated with a different
layer than a layer of the current frame.
8. The computer implemented method of claim 6, wherein the
immediately previous temporal frame is a previous frame in a
highest frame rate layer.
9. A computer-readable storage device which stores a set of
instructions which when executed performs a method for providing
quantization parameter prediction, the method comprising: receiving
a plurality of video frames associated with a bitstream; predicting
a quantization parameter for a current frame according to a
quantization parameter associated with an immediately previous
temporal frame and a quantization parameter associated with a
similar frame that has a similar rate distortion characteristic to
the current frame; and encoding the current frame according to the
quantization parameter.
10. The computer-readable storage device of claim 9, wherein the
quantization parameter of the current frame is selected according
to a bandwidth constraint.
11. The computer-readable storage device of claim 9, further
comprising selecting the quantization parameter of the current
frame according to a predicting a mean absolute difference of the
current frame according to a mean absolute difference associated
with the immediately previous temporal frame and a mean absolute
difference associated with the similar frame that has the similar
rate distortion characteristic to the current frame.
12. The computer-readable storage device of claim 10, wherein the
quantization parameter of the current frame is determined by a
quadratic rate-quantizer model using the predicted mean absolute
difference of the current frame.
13. The computer-readable storage device of claim 10, further
comprising predicting a mean absolute difference of the current
frame according to a linear regression model, wherein the mean
absolute difference associated with the immediately previous
temporal frame and the mean absolute difference associated with the
similar frame comprise regressors associated with the linear
regression model.
14. The computer-readable storage device of claim 9, wherein the
similar frame is identified by: computing a rate distortion
characteristic of each of the plurality of video frames; and
comparing the rate distortion characteristic of each of the
plurality of video frames to a current rate distortion
characteristic of the current frame.
15. The computer-readable storage device of claim 9, wherein the
bitstream comprises a base layer and at least one enhancement
layer.
16. The computer-readable storage device of claim 9, wherein the
immediately previous temporal frame is associated with a different
layer than a layer of the current frame.
17. The computer-readable storage device of claim 16, wherein the
immediately previous temporal frame is a previous frame in a
highest frame rate layer.
18. A system for providing quantization parameter prediction, the
system comprising: a memory storage; and a processing unit coupled
to the memory storage, wherein the processing unit is operative to:
receive a plurality of video frames associated with a bitstream;
predict a mean absolute difference of a current frame according to
a mean absolute difference associated with an immediately previous
temporal frame and a mean absolute difference associated with a
similar frame that has a similar rate distortion characteristic to
the current frame; select a quantization parameter for the current
frame according to the predicted mean absolute difference of the
current frame; and encode the current frame according to the
quantization parameter.
19. The system of claim 18, wherein the quantization parameter of
the current frame is selected according to a bandwidth constraint
and the predicted mean absolute difference of the current
frame.
20. The system of claim 18, wherein the immediately previous
temporal frame is a previous frame in a highest frame rate layer.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] The present application is a continuation of U.S.
application Ser. No. 13/044,630 filed Mar. 10, 2011 entitled "Mean
Absolute Difference Prediction for Video Encoding Rate Control,"
now U.S. Pat. No. 8,644,383 which is hereby incorporated by
reference.
BACKGROUND
[0002] Mean Absolute Difference (MAD) may be used as an index for
video coding complexity in an H.264 rate control model. In
conventional systems, MAD is predicted by a linear regression model
using the actual MAD of the previous stored frames. For bitstreams
coded with temporal and quality scalability, such as the Annex G
extension of H.264 of Scalable Video Coding (SVC), it may be
difficult to select which regressor should be used to achieve
accurate prediction of the current MAD. In some situations, the
inaccuracy of the MAD can lead to inappropriate selection of a
Quantization Parameter (QP), resulting in a poor rate control
performance.
SUMMARY
[0003] MAD prediction for video encoding rate control may be
provided. This Summary is provided to introduce a selection of
concepts in a simplified form that are further described below in
the Detailed Description. This Summary is not intended to identify
key features or essential features of the claimed subject matter.
Nor is this Summary intended to be used to limit the claimed
subject matter's scope.
[0004] Mean absolute difference (MAD) prediction for video encoding
may be provided. Upon receiving a video stream comprising a
plurality of quality layers, a first quantization parameter (QP)
may be selected for a first frame of the video stream according to
a second QP associated with a second frame and a third QP
associated with a third frame. The first frame may then be encoded
according to the selected first QP.
[0005] Both the foregoing general description and the following
detailed description provide examples and are explanatory only.
Accordingly, the foregoing general description and the following
detailed description should not be considered to be restrictive.
Further, features or variations may be provided in addition to
those set forth herein. For example, embodiments may be directed to
various feature combinations and sub-combinations described in the
detailed description.
BRIEF DESCRIPTION OF THE DRAWINGS
[0006] The accompanying drawings, which are incorporated in and
constitute a part of this disclosure, illustrate various
embodiments of the present invention. In the drawings:
[0007] FIG. 1 is a block diagram of an operating environment;
[0008] FIG. 2 is a diagram illustrating an SVC bitstream;
[0009] FIG. 3 is a flow chart of a method for providing MAD
prediction for video encoding; and
[0010] FIG. 4 is a block diagram of a system including a computing
device.
DETAILED DESCRIPTION
[0011] The following detailed description refers to the
accompanying drawings. Wherever possible, the same reference
numbers are used in the drawings and the following description to
refer to the same or similar elements. While embodiments of the
invention may be described, modifications, adaptations, and other
implementations are possible. For example, substitutions,
additions, or modifications may be made to the elements illustrated
in the drawings, and the methods described herein may be modified
by substituting, reordering, or adding stages to the disclosed
methods. Accordingly, the following detailed description does not
limit the invention. Instead, the proper scope of the invention is
defined by the appended claims.
[0012] Mean Absolute Difference (MAD) prediction for video encoding
rate control may be provided. Consistent with embodiments of the
present invention, two regressors may be used in a single linear
regression model. The first regressor may comprise the MAD of a
closest frame to the current frame to be encoded across higher,
lower, or current temporal layers. The second regressor may
comprise the MAD of a closest frame with respect to rate distortion
(RD) characteristics. The predicted MAD according to the two
regressors may then be used by a quadratic rate-quantizer model to
decide an appropriate quantization parameter (QP) for the encoding
of the current frame.
[0013] FIG. 1 is a block diagram of an operating environment 100
comprising a capture source 110 and a network headend 120
comprising at least a video encoder 130 and a channel multiplexer
135. Capture source 110 may comprise, for example, a real-time
video capture device such as a video camera, a video conferencing
server, and/or a live video stream provided via a provider network
(e.g., a fiber and/or satellite network). Headend 120 may be
coupled to an access network 130, such as a hybrid-fiber coax (HFC)
cable television network, that may be further connected to a viewer
premises 140 comprising a video decoder 150 coupled to a display
155. Consistent with embodiments of the invention, other operating
environments may be used to provide the systems and methods
described herein. For example, a server coupled to a public network
such as the Internet may be operative to encode videos using MAD
prediction for provision to users associated with decoding capable
computing devices coupled to the network.
[0014] FIG. 2 is a diagram illustrating a scalable video coding
(SVC) bitstream 200. Bitstream 200 may comprise two layers: a base
layer 210 and an enhancement layer 220. Consistent with embodiments
of the invention, SVC bitstreams may comprise multiple quality
enhancement layers in addition to base layer 210. Consistent with
embodiments of the invention, base layer 210 may comprise multiple
temporal layers. The bitstream may comprise a plurality of frames
associated with each layer, each of which is identified by a number
indicating a temporal position of the frame and a letter indicating
whether the frame is associated with base layer 210 (i.e.,
plurality of frames Xb) or enhancement layer 220 (i.e., plurality
of frames Xe).
[0015] FIG. 2 further illustrates corresponding regressors for some
frames in bitstream 200. For example, video encoder 130 may predict
a MAD for each frame that may be used to calculate an appropriate
quantization parameter (QP). The QP may, in conjunction with the
data size of the frame, be used to control the transmission rate of
bitstream 200 based on a bandwidth constraint. The QP may comprise
a value ranging from 0-51, with lower values resulting in a larger
data size for the resulting encoded frame and a concurrently higher
quality for that frame, while higher QP values result in a smaller
data size and a lower quality.
[0016] To predict the MAD for the frame to be encoded, video
encoder 130 may calculate MADs for two other frames of bitstream
200 and use those as regressors in a linear regression model. The
first regressor, MAD.sub.Temp may comprise the MAD of the closest
temporal frame across higher, lower, or current temporal layers.
For base layer 210, for example, this may comprise the previous
frame in the highest frame rate layer. For enhancement layer 220,
this may comprise the corresponding base layer 210 frame in the
same temporal layer. This regressor may allow the capture of abrupt
changes in a hierarchical prediction sequence.
[0017] The second regressor, MAD.sub.RDC, may comprise the MAD of
the closest frame with similar rate distortion (RD)
characteristics. The RD characteristics of a frame may be functions
of that frame's QP and the QP of its reference frame. The
similarity of RD characteristics between frames i and j, where
QP(i) is denoted as the QP of frame i and Ref(i) as the reference
frame of frame i is defined in Equation 1, below. Table 1, also
below, shows QPs that may be used in Equation 1 to calculate values
for MAD.sub.RDC.
1 QP ( i ) - QP ( j ) + QP ( i ) - QP ( Ref ( i ) ) - QP ( j ) - QP
( Ref ( j ) ) Equation 1 ##EQU00001##
TABLE-US-00001 TABLE 1 X 0 1 2 3 4 5 6 7 Xb K K + 5 K + 4 K + 5 K K
+ 5 K + 4 K + 5 Xe K + 6 K + K + 10 K + 11 K + 6 K + 11 K + 10 K +
11 11
[0018] Table 2, below, shows example regressors for a second Group
of Pictures (GOP) interval of bitstream 200.
TABLE-US-00002 TABLE 2 5b 5e 6b 6e 7b 7e 8b 8e MAD.sub.Temp 4b 5b
5b 6b 6b 7b 7b 8b MAD.sub.RDC 3b 3e 5b 5e 6b 6e 4b 4e
[0019] A predicted MAD for the frame to be encoded may be computed
according to the second order linear regression model shown in
Equation 2, below.
MAD'=c.sub.2MAD.sub.Temp+c.sub.1MAD.sub.RDC+c.sub.0 Equation 2
[0020] FIG. 3 is a flow chart setting forth the general stages
involved in a method 300 consistent with an embodiment of the
invention for providing Mean Absolute Difference (MAD) prediction
for video coding. Method 300 may be implemented using a computing
device 400 as described in more detail below with respect to FIG.
4. Ways to implement the stages of method 300 will be described in
greater detail below. Method 300 may begin at starting block 305
and proceed to stage 310 where computing device 400 may receive a
current frame for encoding. For example, the current frame may
comprise a frame associated with a scalable video coding (SVC)
bitstream. The SVC bitstream may comprise a plurality of layers
comprising a base layer and at least one quality enhancement layer.
The base layer may comprise one and/or more temporal layers.
[0021] Method 300 may then advance to stage 315 where computing
device 400 may compute a first mean absolute difference (MAD) of a
first frame. For example, where the current frame comprises frame
5b of bitstream 200, encoder 130 may compute a regressor value for
the MAD of frame 4b of bitstream 200. Consistent with embodiments
of the invention, the first frame may comprise a temporally
previous frame of the current frame (e.g., the immediately
preceding frame associated with the same layer as the current
frame).
[0022] Method 300 may then advance to stage 320 where computing
device 400 may identify a second frame comprising a similar rate
distortion characteristic of the current frame. For example, where
the current frame comprises frame 5b of bitstream 200, encoder 130
may compute a regressor value for the MAD of frame 3b of bitstream
200. Encoder 130 may compute a rate distortion for the current
frame and for a plurality of other frames of bitstream 200 and
determine which of the plurality of other frames comprises a rate
distortion characteristic most similar to the current frame.
Consistent with embodiments of the invention, the second frame and
the current frame may each be associated with a same or a different
layer of the plurality of layers.
[0023] Method 300 may then advance to stage 325 where computing
device 400 may compute a second mean absolute difference (MAD) of
the identified second frame.
[0024] Method 300 may then advance to stage 330 where computing
device 400 may predict a current MAD associated with the current
frame according to a second order linear regression model. For
example, the first MAD and the second MAD may each comprise
regressors associated with the second order linear regression model
shown in Equation 2, above.
[0025] Method 300 may then advance to stage 335 where computing
device 400 may select a quantization parameter (QP) for the current
frame according to the predicted current MAD and a bandwidth
constraint associated with a transmission network. For example, the
quantization parameter may comprise a value between 0 and 51,
inclusive.
[0026] Method 300 may then advance to stage 340 where computing
device 400 may encode the current frame according to the selected
quantization parameter according to the H.264 video coding
standard. The encoded frame may, based on the selected QP, comprise
a size consistent with a desired transmission rate for the
bitstream.
[0027] Method 300 may then advance to stage 345 where computing
device 400 may transmit the encoded frame over a transmission
network. For example, headend 120 may transmit the encoded frame
over access network 130 that may comprise a hybrid-fiber coax (HFC)
cable television network and/or an Internet Protocol (IP)
network.
[0028] Method 300 may then advance to stage 350 where computing
device 400 may update the linear model parameters. For example,
values c0, c1 and c2 from Equation 2 may be updated according to
the actual MAD computed from the encoded frame. The model
parameters may be updated after encoding each frame because the
statistics of a nonstationary video sequence may change with time.
Method 300 may then end at stage 355
[0029] An embodiment consistent with the invention may comprise a
system for providing mean absolute difference prediction in a video
encoder. The system may comprise a memory storage and a processing
unit coupled to the memory storage. The processing unit may be
operative to receive a video stream comprising a plurality of
quality layers, select a first quantization parameter (QP) for a
first frame of the video stream according to a second QP associated
with a second frame and a third QP associated with a third frame,
and encode the first frame according to the predicted first QP. The
video stream may comprise, for example, a scalable video coding
(SVC) bitstream comprising a base layer and at least one
enhancement layer.
[0030] Being operative to select the quantization parameter for the
first frame may comprise the processing unit being operative to
identify the second frame according to a similarity between a first
rate distortion characteristic associated with the first frame and
a second rate distortion characteristic associated with the second
frame and identify the third frame as a closest previous temporal
frame of the first frame. The first, second, and third frames may
be associated with the same and/or different layers of the
bitstream. The processing unit may be operative to compute a Mean
Absolute Difference (MAD) of the second and third frames and
predict a MAD of the first frame according to a linear regression
model, wherein the MAD of the second frame and the MAD of the third
frame comprise regressor associated with the linear regression
model. The first QP may then be selected according to the predicted
MAD of the first frame and a bandwidth constraint associated with a
transmission network.
[0031] Another embodiment consistent with the invention may
comprise a system for providing mean absolute difference prediction
in a video encoder. The system may comprise a memory storage and a
processing unit coupled to the memory storage. The processing unit
may be operative to receive a plurality of video frames associated
with a scalable video coding (SVC) bitstream comprising a plurality
of layers, identify an immediately previous temporal frame to a
current frame, identify a similar frame to the current frame,
predict a mean absolute difference (MAD) of the current frame
according to a first MAD associated with the immediately previous
temporal frame and a second MAD associated with the similar frame,
and encode the current frame according to the predicted MAD. Being
operative to identify the similar frame may comprise the processing
unit being operative to compute a rate distortion characteristic of
each of a subset of the plurality of video frames and compare the
rate distortion characteristic of each of the subset of the
plurality of video frames to a current rate distortion
characteristic of the current frame. The processing unit may be
further operative to identify the similar frame as a frame of the
subset of the plurality of video frames comprising a rate
distortion characteristic similarity measure greater than or equal
to 0.25 relative to the current frame. The immediately previous
temporal frame may be associated with a different layer of the
plurality of layers than the current frame. The processing unit may
be further operative to select a quantization parameter (QP) of the
current frame according to a bandwidth constraint and the predicted
MAD of the current frame. The processing unit may be further
operative to transmit the encoded frame over a network such as a
hybrid-fiber coax (HFC) cable television network and/or an Internet
Protocol (IP) network.
[0032] Yet another embodiment consistent with the invention may
comprise a system for providing mean absolute difference prediction
in a video encoder. The system may comprise a memory storage and a
processing unit coupled to the memory storage. The processing unit
may be operative to receive a current frame for encoding, wherein
the current frame is associated with a scalable video coding (SVC)
bitstream comprising a plurality of layers and wherein the
plurality of layers comprises a base layer and at least one
enhancement layer, compute a first mean absolute difference (MAD)
of a first frame, wherein the first frame comprises a temporally
previous frame of the current frame, identify a second frame
comprising a similar rate distortion characteristic of the current
frame, wherein the second frame and the current frame are each
associated with a same layer of the plurality of layers, compute a
second mean absolute difference (MAD) of the second frame, predict
a current MAD associated with the current frame according to a
second order linear regression model, wherein the first MAD and the
second MAD each comprise regressors associated with the second
order linear regression model, select a quantization parameter for
the current frame according to the predicted current MAD and a
bandwidth constraint associated with a transmission network,
wherein the quantization parameter comprises a value between 0 and
51, inclusive, encode the current frame according to the selected
quantization parameter according to the H.264 video coding
standard, and transmit the encoded frame over the transmission
network.
[0033] FIG. 4 is a block diagram of a system including computing
device 400. Consistent with an embodiment of the invention, the
aforementioned memory storage and processing unit may be
implemented in a computing device, such as computing device 400 of
FIG. 4. Any suitable combination of hardware, software, or firmware
may be used to implement the memory storage and processing unit.
For example, the memory storage and processing unit may be
implemented with computing device 400 or any of other computing
devices 418, in combination with computing device 400. The
aforementioned system, device, and processors are examples and
other systems, devices, and processors may comprise the
aforementioned memory storage and processing unit, consistent with
embodiments of the invention. Furthermore, computing device 400 may
comprise operating environment 100 as described above. Methods
described in this specification may operate in other environments
and are not limited to computing device 400.
[0034] With reference to FIG. 4, a system consistent with an
embodiment of the invention may include a computing device, such as
computing device 400. In a basic configuration, computing device
400 may include at least one processing unit 402 and a system
memory 404. Depending on the configuration and type of computing
device, system memory 404 may comprise, but is not limited to,
volatile (e.g. random access memory (RAM)), non-volatile (e.g.
read-only memory (ROM)), flash memory, or any combination. System
memory 404 may include operating system 405, one or more
programming modules 406, and may include video encoder 130.
Operating system 405, for example, may be suitable for controlling
computing device 400's operation. Furthermore, embodiments of the
invention may be practiced in conjunction with a graphics library,
other operating systems, or any other application program and is
not limited to any particular application or system. This basic
configuration is illustrated in FIG. 4 by those components within a
dashed line 408.
[0035] Computing device 400 may have additional features or
functionality. For example, computing device 400 may also include
additional data storage devices (removable and/or non-removable)
such as, for example, magnetic disks, optical disks, or tape. Such
additional storage is illustrated in FIG. 4 by a removable storage
409 and a non-removable storage 410. Computing device 400 may also
contain a communication connection 416 that may allow device 400 to
communicate with other computing devices 418, such as over a
network in a distributed computing environment, for example, an
intranet or the Internet. Communication connection 416 is one
example of communication media.
[0036] The term computer readable media as used herein may include
computer storage media. Computer storage media may include volatile
and nonvolatile, removable and non-removable media implemented in
any method or technology for storage of information, such as
computer readable instructions, data structures, program modules,
or other data. System memory 404, removable storage 409, and
non-removable storage 410 are all computer storage media examples
(i.e memory storage.) Computer storage media may include, but is
not limited to, RAM, ROM, electrically erasable read-only memory
(EEPROM), flash memory or other memory technology, CD-ROM, digital
versatile disks (DVD) or other optical storage, magnetic cassettes,
magnetic tape, magnetic disk storage or other magnetic storage
devices, or any other medium which can be used to store information
and which can be accessed by computing device 400. Any such
computer storage media may be part of device 400. Computing device
400 may also have input device(s) 412 such as a keyboard, a mouse,
a pen, a sound input device, a touch input device, etc. Output
device(s) 414 such as a display, speakers, a printer, etc. may also
be included. The aforementioned devices are examples and others may
be used.
[0037] The term computer readable media as used herein may also
include communication media. Communication media may be embodied by
computer readable instructions, data structures, program modules,
or other data in a modulated data signal, such as a carrier wave or
other transport mechanism, and includes any information delivery
media. The term "modulated data signal" may describe a signal that
has one or more characteristics set or changed in such a manner as
to encode information in the signal. By way of example, and not
limitation, communication media may include wired media such as a
wired network or direct-wired connection, and wireless media such
as acoustic, radio frequency (RF), infrared, and other wireless
media.
[0038] As stated above, a number of program modules and data files
may be stored in system memory 404, including operating system 405.
While executing on processing unit 402, programming modules 406
(e.g., video encoder 130) may perform processes including, for
example, one or more of method 300's stages as described above. The
aforementioned process is an example, and processing unit 402 may
perform other processes. Other programming modules that may be used
in accordance with embodiments of the present invention may include
electronic mail and contacts applications, word processing
applications, spreadsheet applications, database applications,
slide presentation applications, drawing or computer-aided
application programs, etc.
[0039] Generally, consistent with embodiments of the invention,
program modules may include routines, programs, components, data
structures, and other types of structures that may perform
particular tasks or that may implement particular abstract data
types. Moreover, embodiments of the invention may be practiced with
other computer system configurations, including hand-held devices,
multiprocessor systems, microprocessor-based or programmable
consumer electronics, minicomputers, mainframe computers, and the
like. Embodiments of the invention may also be practiced in
distributed computing environments where tasks are performed by
remote processing devices that are linked through a communications
network. In a distributed computing environment, program modules
may be located in both local and remote memory storage devices.
[0040] Furthermore, embodiments of the invention may be practiced
in an electrical circuit comprising discrete electronic elements,
packaged or integrated electronic chips containing logic gates, a
circuit utilizing a microprocessor, or on a single chip containing
electronic elements or microprocessors. Embodiments of the
invention may also be practiced using other technologies capable of
performing logical operations such as, for example, AND, OR, and
NOT, including but not limited to mechanical, optical, fluidic, and
quantum technologies. In addition, embodiments of the invention may
be practiced within a general purpose computer or in any other
circuits or systems.
[0041] Embodiments of the invention, for example, may be
implemented as a computer process (method), a computing system, or
as an article of manufacture, such as a computer program product or
computer readable media. The computer program product may be a
computer storage media readable by a computer system and encoding a
computer program of instructions for executing a computer process.
The computer program product may also be a propagated signal on a
carrier readable by a computing system and encoding a computer
program of instructions for executing a computer process.
Accordingly, the present invention may be embodied in hardware
and/or in software (including firmware, resident software,
micro-code, etc.). In other words, embodiments of the present
invention may take the form of a computer program product on a
computer-usable or computer-readable storage medium having
computer-usable or computer-readable program code embodied in the
medium for use by or in connection with an instruction execution
system. A computer-usable or computer-readable medium may be any
medium that can contain, store, communicate, propagate, or
transport the program for use by or in connection with the
instruction execution system, apparatus, or device.
[0042] The computer-usable or computer-readable medium may be, for
example but not limited to, an electronic, magnetic, optical,
electromagnetic, infrared, or semiconductor system, apparatus,
device, or propagation medium. More specific computer-readable
medium examples (a non-exhaustive list), the computer-readable
medium may include the following: an electrical connection having
one or more wires, a portable computer diskette, a random access
memory (RAM), a read-only memory (ROM), an erasable programmable
read-only memory (EPROM or Flash memory), an optical fiber, and a
portable compact disc read-only memory (CD-ROM). Note that the
computer-usable or computer-readable medium could even be paper or
another suitable medium upon which the program is printed, as the
program can be electronically captured, via, for instance, optical
scanning of the paper or other medium, then compiled, interpreted,
or otherwise processed in a suitable manner, if necessary, and then
stored in a computer memory.
[0043] Embodiments of the present invention, for example, are
described above with reference to block diagrams and/or operational
illustrations of methods, systems, and computer program products
according to embodiments of the invention. The functions/acts noted
in the blocks may occur out of the order as shown in any flowchart.
For example, two blocks shown in succession may in fact be executed
substantially concurrently or the blocks may sometimes be executed
in the reverse order, depending upon the functionality/acts
involved.
[0044] While certain embodiments of the invention have been
described, other embodiments may exist. Furthermore, although
embodiments of the present invention have been described as being
associated with data stored in memory and other storage mediums,
data can also be stored on or read from other types of
computer-readable media, such as secondary storage devices, like
hard disks, floppy disks, or a CD-ROM, a carrier wave from the
Internet, or other forms of RAM or ROM. Further, the disclosed
methods' stages may be modified in any manner, including by
reordering stages and/or inserting or deleting stages, without
departing from the invention.
[0045] All rights including copyrights in the code included herein
are vested in and the property of the Applicant. The Applicant
retains and reserves all rights in the code included herein, and
grants permission to reproduce the material only in connection with
reproduction of the granted patent and for no other purpose.
[0046] While the specification includes examples, the invention's
scope is indicated by the following claims. Furthermore, while the
specification has been described in language specific to structural
features and/or methodological acts, the claims are not limited to
the features or acts described above. Rather, the specific features
and acts described above are disclosed as example for embodiments
of the invention.
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