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Transcript of M. Wu: ENEE631 Digital Image Processing (Spring'09) Basics on Video Coding Spring ’09 Instructor:...
M. Wu: ENEE631 Digital Image Processing (Spring'09)
Basics on Video CodingBasics on Video Coding
Spring ’09 Instructor: Min Wu
Electrical and Computer Engineering Department,
University of Maryland, College Park
bb.eng.umd.edu (select ENEE631 S’09) [email protected]
ENEE631 Spring’09ENEE631 Spring’09Lecture 15 (3/30/2009)Lecture 15 (3/30/2009)
M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [2]
Overview and LogisticsOverview and Logistics Last Time:
– Bit allocation issues in image compression– Optimal transform KLT ~ unitary transform; decorrelate data
optimal MMSE approximation under basis restriction
Comments on issues arising from mid-term exam– Linearity and shift invariance: check by definition
Is piecewise linear stretching a linear operation? If ignoring boundary effect, are median filtering and point
operations (including histogram based processing) shift invariant? Give examples on shift variant operations
– Quantization: MMSE criterion vs. Minmax criterion
Today:– Image interpolation– Video coding: explore temporal and spatial redundancy
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M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [5]
Image Interpolation: Image Interpolation:
A Quick Extension from 1-D InterpolationA Quick Extension from 1-D Interpolation
Useful in image enlargement, rotation, motion estimation, etc.Useful in image enlargement, rotation, motion estimation, etc.
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M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [6]
Examples of Image InterpolationExamples of Image Interpolation
4x zoom (nearest neighbor) 4x zoom (bilinear)
M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [7]
Interpolation / ZoomingInterpolation / Zooming
How to make up the new pixels?
Replication according to the nearest neighbor– Simple but leaves zig-zag boundary
(reflect spectrum artifacts; equiv. to interlace zero & LPF with a constant mask)
(p,q)
(p’,q’)
(p,q+1)
(p+1,q+1)(p+1,q)
a
b
a 1-a
f1
f2
– Do two horizontal and one vertical 1-D interpolation
F( p’, q’ ) = (1-a) [ (1-b) F(p, q) + b F(p, q+1) ] + a [(1-b) F(p+1, q) + b F(p+1, q+1) ]
For zoom in by 2 in each dimension:F(p’, q’) = 0.5 [0.5 F(p,q) + 0.5 F(p,q+1)] + 0.5 [0.5 F(p+1,q) + 0.5 F(p+1,q+1)]
=> equiv. to F(x, y) = r x + s y + u xy + v solve parameters using 4 known pixels
Bilinear interpolation– Extend 1-D linear interpolation: (1-a) f1 + a f2
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M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [8]
Review: 1-D Frequency-Domain InterpretationReview: 1-D Frequency-Domain Interpretation
From Crochiere-Rabiner “Multirate DSP” book Fig.2.15-16
M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [9]
Frequency-Domain InterpretationFrequency-Domain Interpretation Review multirate signal processing (ENEE630)
For Images: extend to the 2-D transform
Downsampling– Aliasing as spectra replicas becomes closer– LPF to avoid aliasing
Upsampling– Upsampling with zero interlacing ~ replicated spectrum– LPF to filter out the spectra replicas in high-frequency part– Ideal filter vs. practical filters
nearest neighbor approach for 2x zoom use [think] what equiv. filters used for bilinear interpolation?
Sampling rate conversion with rational rate M / N– Upsample with zero interlacing by M LPF Downsample
1 1
1 1
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1/2 1
1/4 1/2
1/4 1/2
1/2
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M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [11]
More on InterpolationMore on Interpolation
Other filters
– Bi-cubic interpolation (3rd order polynomial on index variables) Based on combination of 16-pixel neighborhood
– Can build p-th order interpolation by recursive filtering After upsample by p, convolve with linear interpolation filter p
times
Interpolation that avoids blurred edges and textures
– Sharpening– Edge-preserving interpolation
( recent research papers in ICIP and Trans. on Image Proc. )
=> Will discuss more on 2-D sampling and frequency domain interpretation in a few lectures
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M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [13]
From Image Coding to Video CodingFrom Image Coding to Video Coding
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M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [14]
ReviewReview
Basic tools for compression
– PCM coding, entropy coding, run-length coding– Quantization and truncation– Predictive coding– Transform coding: DCT-based
JPEG image compression
– 8x8 Block-DCT based transform coding– Use predictive coding, quantization, run-length coding, and
entropy coding
Today: digital video and video compression
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M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [15]
Bring in Motion Bring in Motion Video (Motion Pictures) Video (Motion Pictures)
Capturing video
– Video as a 3-D signal 2 spatial dimensions & time dimension continuous I( x, y, t ) => discrete I( m, n, tk )
– Frame by frame => image sequence
Encode digital video
– Simplest way ~ compress each frame image individually e.g., “motion-JPEG” only spatial redundancy is explored and reduced
– How about temporal redundancy? Is differential coding good? Pixel-by-pixel difference could still be large due to motion
Need better prediction
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M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [16]
Video ExamplesVideo Examples
1. NASA shuttle
2. “Talking Head”
M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [18]
Explore Temporal Redundancy – 1Explore Temporal Redundancy – 1stst try try
– Difference between corresponding pixels of two video frames
From Gonzalez-Woods 3/e Fig. 8.34-8.35
M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [19]
Explore MotionExplore MotionFrom Gonzalez-Woods
3/e Fig. 8.37
M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [21]
Motion EstimationMotion Estimation
Help understanding the content of image sequence– Useful for surveillance
Stabilizing video by detecting and removing small, noisy global motions– For building stabilizer in camcorder
Reduce temporal redundancy of video for compression[What estimation accuracy and resolution are necessary for this purpose?]
one motion displacement vector per picture? (extreme case: DPCM)
one vector per pixel?
=> Tradeoff: (1) effectiveness & complexity in approximating commonly seen motions; (2) overhead in describing the motion model.
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M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [22]
Block-Matching by Exhaustive SearchBlock-Matching by Exhaustive Search Modeling: assume movements are block-based translation
Search every possibility over a specified range for the best matching block – MAD (mean absolute difference) often used for simplicity
=> Flash Demo (by Dr. Ken Lam @ Hong Kong PolyTech Univ.)
From Wang’s Preprint Fig.6.6U
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M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [23]
Motion Compensation Motion Compensation
– Help reduce temporal redundancy of video
PREVIOUS FRAME CURRENT FRAME
PREDICTED FRAME PREDICTION ERROR FRAME
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Revised from R.Liu Seminar Course ’00 @ UMD
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Complexity of Exhaustive Block-MatchingComplexity of Exhaustive Block-Matching
Assumptions– Block size NxN and image size S=M1 x M2– Search step size is 1 pixel ~ “integer-pel accuracy”– Search range +/–R pixels both horizontally and vertically
Computation complexity# Candidate matching blocks = (2R+1)2 # Operations for computing MAD for one block ~ O(N2)# Operations for MV estimation per blk ~ O((2R+1)2 N2); # Blocks = S / N2 – Total # operations for entire frame ~ O((2R+1)2 S)
i.e., overall computation load is independent of block size! block size affects encoding bit rate and effectiveness of motion
compensation.
E.g., M=512, N=16, R=16, 30fps => On the order of 8.55 x 109 operations per second!– Was difficult for real time estimation, but possible with parallel hardware
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M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [25]
Exhaustive Search: Cons and ProsExhaustive Search: Cons and Pros
Pros– Guaranteed optimality within search range and motion model
Cons– Can only search among finitely many candidates
What if the motion is “fractional”?
– High computation complexity On the order of [search-range-size x image-size] for 1-pixel step
size
How to improve accuracy?
– Include blocks at fractional translation as candidates => require interpolation
How to improve speed?– Try to exclude unlikely candidates
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M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [26]
Fractional Accuracy Search for Block MatchingFractional Accuracy Search for Block Matching For motion accuracy of 1/K pixel
– Upsample (interpolate) reference frame by a factor of K– Search for the best matching block in the upsampled reference frame
Half-pel accuracy ~ K=2– Significant accuracy improvement over integer-pel
(esp. for low-resolution)– Complexity increase
(From Wang’s Preprint Fig.6.7)
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M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [27]
No motion compensation
1-pixel precision
½ pixel precision
¼ pixel precision
Fractional Accuracy for Motion: ExampleFractional Accuracy for Motion: ExampleFrom Gonzalez-Woods
3/e Fig. 8.38
M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [28]
Fast Algorithms for Block MatchingFast Algorithms for Block Matching
Basic ideas– Matching errors near the best match are generally smaller than far away– Skip candidates that are unlikely to give good match
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(From Wang’s Preprint Fig.6.6)
M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [29]
M24
M15 M14 M13
M16
M11
M12
M5 M4 M3
M17 M18 M19
-6 M6 M1 M2 +6
M7 M8 M9
dx
dy
Fast Algorithm: 3-Step Search Fast Algorithm: 3-Step Search
Search candidates at 8 neighbor positions
Step-size cut down by 2 after each iteration– Start with step size
approx. half of max. search range
motion vector {dx, dy} = {1, 6}
Total number of computations: 9 + 82 = 25 (3-step) (2R+1)2 = 169 (full search)
(Fig. from Ken Lam – HK Poly Univ. short course in summer’2001)
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=> See Flash demo by Jane Kim (UMD)
M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [30]
Lowest resolution
medium resolution
Original resolution
Hierarchical Block MatchingHierarchical Block Matching Problem with fast search at full resolution
– Small mis-alignment may give high displacement error (EDFD) esp. for texture and edge blocks
Hierarchical (multi-resolution) block matching– Match with coarse resolution to narrow down search range– Match with high resolution to refine motion estimation
(From Wang’s Preprint Fig.6.19)
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M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [31]
Summary of Today’s LectureSummary of Today’s Lecture
Interpolation
Block-based motion estimation and compensation
Next Lecture: video compression through hybrid coding
=> Given what we discussed, how to design a video codec?
Exploit spatial redundancy via transform coding Exploit temporal redundancy via predictive coding
~ motion estimation and compensation
Reading assignment– Gonzalez’s 3/e book 2.4.4 (interpolation); 8.2.9 (motion compensation)
– To explore further: Wang’s video textbook 9.3.1, 6.4
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Hybrid Coding for Video Hybrid Coding for Video
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DCT-M.E. Hybrid Video CodingDCT-M.E. Hybrid Video Coding “Hybrid” ~ combined transform coding & predictive coding Spatial redundancy removal
– Use DCT-based transform coding for reference frame Temporal redundancy removal
– Use motion-based predictive coding for next frames estimate motion and use reference frame to predict only encode MV & prediction residue (“motion compensation residue”)
(From Princeton EE330 S’01 by B.Liu)
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M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [34]
Review: Predictive Coding with QuantizationReview: Predictive Coding with Quantization Consider: high correlation between successive samples
Predictive coding– Basic principle: Remove redundancy between successive pixels and only
encode residual between actual and predicted – Residue usually has much smaller dynamic range
Allow fewer quantization levels for the same MSE => get compression
– Compression efficiency depends on intersample redundancy
First try:
Any problem with this codec?
uQ (n)
Predictor+
eQ(n)
uP(n) = f[uQ(n-1)] DecodeDecode
rr
u(n)
Predictor
Quantizer_
e(n) eQ(n)
EncodeEncoderr
u’P(n) = f[u(n-1)]
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M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [35]
Predictive Coding (cont’d)Predictive Coding (cont’d)
Problem with 1st try– Input to predictor are different at
encoder and decoder decoder doesn’t know u(n)!
– Mismatch error could propagate to future reconstructed samples
Solution: Differential PCM (DPCM)
– Use quantized sequence uQ(n) for prediction at both encoder and decoder
– Simple predictor f[ x ] = x– Prediction error e(n)– Quantized prediction error eQ(n)
– Distortion d(n) = e(n) – eQ(n)
uQ (n)
Predictor+
eQ(n)
uP(n)= f[uQ(n-1)]
DecodeDecoderr
EncodeEncoderr
u(n)
Predictor
Quantizer_
e(n) eQ(n)
+uP(n)=f[uQ(n-1)]
uQ(n)
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Note: “Predictor” contains one-step buffer as input to the prediction
M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [36]
Hybrid MC-DCT Video EncoderHybrid MC-DCT Video Encoder(From R.Liu’s Handbook Fig.2.18)
• Intra-frame: encoded without prediction• Inter-frame: predictively encoded => use quantized frames as ref for residue
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M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [37]
Hybrid MC-DCT Video DecoderHybrid MC-DCT Video Decoder
(From R.Liu’s Handbook Fig.2.18)
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M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [39]
Hybrid Video Coding: Problems to Be SolvedHybrid Video Coding: Problems to Be Solved Not all regions are easily inferable from previous frame
– Occlusion ~ solvable by backward prediction using future frames as ref.– Adaptively decide using prediction or not
Drifting and error propagation
Solution: Encode reference regions or frames from time to time (“intra coding”)
Random access: e.g. want to get 95th frame
Solution: Encode frame without prediction from time to time
How to allocate bits?– Based on visual model and statistics: JPEG-like quant. steps; entropy coding
– Consider constant or variable bit-rate requirement Constant-bit-rate (CER) vs. Variable-bit-rate (VER)
Wrap up all solutions ~ MPEG-like codec
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M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [40]
M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [41]
M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [43]
Background Reviews onBackground Reviews on
Video Acquisition and DisplayVideo Acquisition and Display
M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [44]
Video CameraVideo Camera
Frame-by-frame capturing
CCD sensors (Charge-Coupled Devices)– 2-D array of solid-state sensors– Each sensor corresponding to a pixel– Store in a buffer and sequentially read out– Small and light => widely used
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M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [45]
Video DisplayVideo Display
CRT (Cathode Ray Tube)
– Large dynamic range– Bulky for large display
CRT physical depth has to be similar to screen width
LCD Flat-panel display
– Use electrical field to change the optical properties hence the brightness/color of liquid crystal
– Generating the electrical field by an array of transistors: active-matrix thin-film transistors by plasma
“Active-matrix display” (also known as TFT) has a transistor located at each pixel, allowing display be switched more frequently and less current to control pixel luminance. Passive matrix LCD has a grid of conductors with pixels located at the grid intersections
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M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [46]
Composite vs. Component VideoComposite vs. Component Video
Component video– Three separate signals for tristimulus color representation or
luminance-chrominance representation – Pro: higher quality– Con: need high bandwidth and synchronization
Composite video– Multiplex into a signal signal– Historical reason for transmitting color TV through monochrome
channel– Pro: save bandwidth– Con: cross talk
S-video: luminance sig. + single multiplexed chrominance sig.
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M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [47]
Analog Video RasterAnalog Video Raster
Line-by-line “Raster Scan”– Represent line-by-line image frame with 1-D analog
waveform– Synchronization signal for horizontal and vertical retrace
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M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [48]
Forming Picture on TV Tube (Monochrome)Forming Picture on TV Tube (Monochrome)
How many lines?
From B.Liu EE330S’01 Princeton
M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [49]
How Many TV Lines?How Many TV Lines?
Determined by spatial freq. response of HVS(Recall Lecture-2)
dot
dot
Cannot resolve if
distance > 2000 x separation
(~ 0.03 degree viewing angle)
From B.Liu EE330S’01 Princeton
N = 500 for D=4H
M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [50]
Progressive vs. Interlaced scanProgressive vs. Interlaced scanFrom B.Liu EE330S’01 Princeton
M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [51]
Analog Color TV SystemsAnalog Color TV Systems
Historical notes – Color TV system had to be compatible with earlier monochrome TV system
3 formats– NTSC ~ North American + Japan/Taiwan – PAL ~ Western Europe + Asia(China) + Middle East– SECAM ~ Eastern Europe + France– What format in your home country?
From Wang’s Preprint Fig.1.5
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Comparison of Three Analog TV SystemsComparison of Three Analog TV Systems
– Spatial and temporal resolution– Color coordinate– Signal bandwidth– Multiplexing of luminance, chrominance, and audio
(From Wang’s Book Preprint)
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NTSCNTSC
4:3 aspect ratio (width:height)
525 lines/frame, 2:1 interlace at field rate 59.94Hz– 483 active lines per frame; vertical retrace takes time of 9 lines– rest for broadcaster’s info. like closed caption
YIQ color coordinate for transmission– RGB primary slightly different from PAL– Orthogonal chrominance
I ~ orange-to-cyan; Q ~ green-to-purple (need less bandwidth)
Multiplexing over 6M Hz total bandwidth– Artifacts due to cross talk between luminance and chrominanceU
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NTSC 6MHz Bandwidth NTSC 6MHz Bandwidth From Wang’s BookPreprint Fig.1.6(b)
M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [55]
Analog Video RecordingAnalog Video Recording
Comparison of common formats
From Wang’s BookPreprint Table 1.2
M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [56]
Digital Video FormatsDigital Video Formats
ITU-R BT.601 recommendation Downsampled chrominance
– Y Cb Cr coordinate and four subsampling formats
Inter. Telecomm. Union – Radio sector
Wang’sBookPreprint Fig.1.8
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Summary: Source Video FormatsSummary: Source Video Formats
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Channel BandwidthsChannel Bandwidths
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M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [59]
Channel Bandwidth (cont’d)Channel Bandwidth (cont’d)
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M. Wu: ENEE631 Digital Image Processing (Spring'09) Lec15 – Hybrid Video Coding [60]
Application RequirementsApplication Requirements
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