video compression 2004
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April 22, 2004 Page 1 John G. Apostolopoulos
VideoCoding
Video Compression
MIT 6.344, Spring 2004
John G. ApostolopoulosStreaming Media Systems Group
Hewlett-Packard [email protected]
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Overview of Next Three Lectures
Video Compression (Thurs, 4/22)
Principles and practice of video coding Basics behind MPEG compression algorithms Current image & video compression standards
Video Communication & Video Streaming I(Tues, 4/27) Video application contexts & examples: DVD and Digital TV Challenges in video streaming over the Internet
Techniques for overcoming these challenges
Video Communication & Video Streaming II(Thurs, 4/29) Video over lossy packet networks and wireless links Error-
resilient video communications
Today
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Outline of Todays Lecture
Motivation for compression
Brief review of generic compression system (from prior lecture) Brief review of image compression (from last lecture) Video compression
Exploit temporal dimension of video signal Motion-compensated prediction Generic (MPEG-type) video coder architecture Scalable video coding
Overview of current video compression standards What do the standards specify? Frame-based video coding: MPEG-1/2/4, H.261/3/4
Object-based video coding: MPEG-4
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Motivation for Compression:Example of HDTV Video Signal
Problem:
Raw video contains an immense amount of data Communication and storage capabilities are limited
and expensive Example HDTV video signal:
720x1280 pixels/frame, progressive scanning at60 frames/s:
20 Mb/s HDTV channel bandwidth Requires compression by a factor of 70 (equivalent
to .35 bits/pixel)
sGbcolor
bits
pixel
colors frames
frame
pixels / 3.1
83
sec
601280720 =
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Achieving Compression
Reduce redundancy and irrelevancy
Sources of redundancy Temporal: Adjacent frames highly correlated Spatial: Nearby pixels are often correlated with
each other Color space: RGB components are correlated
among themselves Relatively straightforward to exploit
Irrelevancy Perceptually unimportant information Difficult to model and exploit
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Spatial and Temporal Redundancy
Why can video be compressed? Video contains much spatial and temporal redundancy.
Spatial redundancy: Neighboring pixels are similar Temporal redundancy: Adjacent frames are similar
Compression is achieved by exploiting the spatial and temporal redundancy inherent to video.
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Outline of Todays Lecture
Motivation for compression
Brief review of generic compression system (from prior lecture) Brief review of image compression (from last lecture) Video compression
Exploit temporal dimension of video signal Motion-compensated prediction Generic (MPEG-type) video coder architecture Scalable video coding
Overview of current video compression standards What do the standards specify? Frame-based video coding: MPEG-1/2/4, H.261/3/4
Object-based video coding: MPEG-4
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Generic Compression System
A compression system is composed of three key building blocks: Representation Concentrates important information into a few parameters
Quantization
Discretizes parameters Binary encoding Exploits non-uniform statistics of quantized parameters Creates bitstream for transmission
Representation(Analysis) Quantization BinaryEncoding
OriginalSignal
CompressedBitstream
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Generic Compression System (cont.)
Generally, the only operation that is lossy is thequantization stage
The fact that all the loss (distortion) is localized to asingle operation greatly simplifies system design
Can design loss to exploit human visual system (HVS)
properties
Representation(Analysis) Quantization
OriginalSignal
CompressedBitstream
BinaryEncoding
Generallylossless Lossy Lossless
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Outline of Todays Lecture
Motivation for compression
Brief review of generic compression system (from prior lecture) Brief review of image compression (from last lecture) Video compression
Exploit temporal dimension of video signal Motion-compensated prediction Generic (MPEG-type) video coder architecture Scalable video coding
Overview of current video compression standards What do the standards specify? Frame-based video coding: MPEG-1/2/4, H.261/3/4
Object-based video coding: MPEG-4
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Video Compression
Video : Sequence of frames(images) that are related
Related along the temporal dimension Therefore temporal redundancy exists
Main addition over image compression
Temporal redundancy Video coder mustexploit the temporal redundancy
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Temporal Processing
Usually high frame rate:Significant temporal redundancy
Possible representations along temporal dimension: Transform/subband methods
Good for textbook case of constant velocity uniform
global motion Inefficient for nonuniform motion, I.e. real-world motion Requires large number of frame stores
Leads to delay (Memory cost may also be an issue)
Predictive methods Good performance using only 2 frame stores However, simple frame differencing in not enough
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Video Compression
Goal: Exploit the temporal redundancy Predict current frame based on previously coded frames Three types of coded frames:
I-frame: Intra-coded frame, coded independently of allother frames
P-frame: Predictively coded frame, coded based onpreviously coded frame
B-frame: Bi-directionally predicted frame, coded basedon both previous and future coded frames
I frame P-frame B-frame
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Temporal Processing:Motion-Compensated Prediction
Simple frame differencingfailswhen there is motion
Must account for motion Motion-compensated (MC) prediction
MC-prediction generally provides significant improvements
Questions: How can we estimate motion? How can we form MC-prediction?
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Temporal Processing:Motion Estimation
Ideal situation:
Partition video into moving objects Describe object motion Generally very difficult
Practical approach: Block-Matching Motion Estimation Partition each frame into blocks, e.g. 16x16 pixels Describe motion of each block
No object identification required Good, robust performance
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Block-Matching Motion Estimation
1615
14
13
1211
109
87
6
5
432
1
1615
1413
1211
109
87
65
43
21
Reference Frame Current Frame
Motion Vector(mv1, mv2)
Assumptions:
Translational motion within block:
All pixels within each block have the same motion
ME Algorithm:1) Divide current frame into non-overlapping N1xN2 blocks2) For each block, find thebest matching block in reference frame
MC-Prediction Algorithm: Use best matching blocks of reference frame as prediction of
blocks in current frame
),,(),,( 221121 ref cur k mvnmvn f k nn f =
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Block Matching:Determining the Best Matching Block
For each block in the current frame search for best matchingblock in the reference frame
Metricsfor determining best match:
Candidate blocks: Strategies for searchingcandidate blocks for best match
Full search: Examine all candidate blocks Partial (fast) search: Examine a carefully selected subset
Estimate of motion for best matching block:motion vector
( )[ ]( ) =
21 ,
2221121 ),,(,,
nn Block ref cur k mvnmvn f k nn f MSE
( )( ) = 21 , 221121 ),,(,,nn Block ref cur k mvnmvn f k nn f MAE ( ) areapixel32,32e.g.,in,blocksAll
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Motion Vectors and Motion Vector Field
Motion vector
Expresses therelative horizontal and vertical offsets(mv 1,mv 2 ), or motion, of a given block from oneframe to another
Each block has its own motion vector Motion vector field
Collection of motion vectors for all the blocks in aframe
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Example of Fast Motion Estimation Search:3-Step (Log) Search
Goal: Reduce number of searchpoints
Example: Dots represent search points Search performed in 3 steps
(coarse-to-fine):Step 1:Step 2:Step 3:
Best match is found at each step
Next step: Search is centeredaround the best match of prior step
Speedup increases for largersearch areas
( ) pixels4( ) pixels2( ) pixels1
( ) areasearch7,7
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Practical Half-Pixel Motion Estimation Algorithm
Half-pixel ME (coarse-fine) algorithm:
1) Coarse step: Perform integer motion estimation on blocks; findbest integer-pixel MV
2) Fine step:Refine estimate to find best half-pixel MV
a) Spatially interpolate the selected region in reference frameb) Compare current block to interpolated reference frameblock
c) Choose the integer or half-pixel offset that provides bestmatch
Typically, bilinear interpolation is used for spatial interpolation
l d f
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Example: MC-Prediction for TwoConsecutive Frames
Previous Frame(Reference Frame)
Current Frame(To be Predicted)
161514
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1
16 15 1413
12 11 109
8 7 65
4 3 21
Reference Frame redicted Frame
E l MC P di i f T
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Example: MC-Prediction for TwoConsecutive Frames (cont.)
Prediction ofCurrent Frame
Prediction Error(Residual)
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Block Matching Algorithm: Summary Issues:
Block size? Search range? Motion vector accuracy?
Motion typically estimated only fromluminance Advantages:
Good, robust performance for compression Resulting motion vector field is easy to represent (one MV
per block) and useful for compression Simple, periodic structure, easy VLSI implementations
Disadvantages: Assumes translational motion model Breaks down for
more complex motion
Often produces blocking artifacts (OK for coding withBlock DCT)
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Video Compression
Main addition over image compression: Exploit the temporal redundancy
Predict current frame based on previously coded frames
Three types of coded frames: I-frame: Intra-coded frame, coded independently of all
other frames P-frame: Predictively coded frame, coded based on
previously coded frame B-frame: Bi-directionally predicted frame, coded based
on both previous and future coded frames
I frame P-frame B-frame
d Example Use of I P B frames:
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Example Use of I-,P-,B-frames:MPEG Group of Pictures (GOP)
Arrows show prediction dependencies between frames
MPEG GOP
I0
B1
B2
P3
B4
B5
P6
B7
B8
I9
Vid
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Summary of Temporal Processing
Use MC-prediction (P and B frames) to reduce temporalredundancy
MC-prediction usually performs well; In compression have asecond chance to recover when it performs badly MC-prediction yields:
Motion vectors MC-prediction error or residual Code error with
conventional image coder Sometimes MC-prediction mayperform badly
Examples: Complex motion, new imagery (occlusions) Approach:1. Identify frame or individual blocks where prediction fail2. Code without prediction
Vid
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Basic Video Compression Architecture
Exploiting the redundancies:
Temporal: MC-prediction (P and B frames) Spatial: Block DCT Color: Color space conversion
Scalar quantization of DCT coefficients Zigzag scanning, runlength and Huffman coding of the
nonzero quantized DCT coefficients
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Video
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Example Video Decoder
Huffman Decoder
Motion Compensation
Buffer YUV to RGB
Reconstructed Frame Residual
MV data
Output Video Signal
Input Bitstream
MC-Prediction
Inverse DCT
Inverse Quantize
Frame Store
Previous Reconstructed
Frame
Video
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Outline of Todays Lecture
Motivation for compression
Brief review of generic compression system (from prior lecture) Brief review of image compression (from last lecture) Video compression
Exploit temporal dimension of video signal Motion-compensated prediction Generic (MPEG-type) video coder architecture Scalable video coding
Overview of current video compression standards What do the standards specify? Frame-based video coding: MPEG-1/2/4, H.261/3/4 Object-based video coding: MPEG-4
Video Motivation for Scalable Coding
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Motivation for Scalable Coding
Basic situation:1. Diverse receiversmay request the same video
Different bandwidths, spatial resolutions, frame rates,
computational capabilities2. Heterogeneous networksand a priori unknown network conditions Wired and wireless links, time-varying bandwidths
When you originally code the video you dont know which clientor network situation will exist in the future Probably have multiple different situations, each requiring adifferent compressed bitstream
Need a different compressed video matched to each situation Possible solutions:
1. Compress & storeMANYdifferent versions of thesame video2. Real-time transcoding(e.g. decode/re-encode)
3. Scalable coding
Video
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Scalable Video Coding
Scalable coding: Decompose video intomultiple layers of prioritized
importance Code layers intobase and enhancement bitstreams Progressively combineone or more bitstreamsto produce
different levels of video quality Example of scalable coding with base and two enhancementlayers: Can produce three different qualities
1. Base layer2. Base + Enh1 layers3. Base + Enh1 + Enh2 layers
Scalability with respect to: Spatial or temporal resolution, bitrate, computation, memory
Higher quality
Video
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Example of Scalable Coding Encode image/video into three layers:
Encoder Base Enh1 Enh2
Low-bandwidth receiver: Send only Base layer
Decoder Low ResBase
Medium-bandwidth receiver: Send Base & Enh1 layers
Decoder Med ResBase Enh1
Decoder High ResBase Enh1 Enh2
High-bandwidth receiver: Send all three layers
Can adapt to different clients and network situations
Video
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Scalable Video Coding (cont.)
Three basic types of scalability (refine video quality
along three different dimensions): Temporal scalability Temporal resolution Spatial scalability Spatial resolution SNR (quality) scalability Amplitude resolution
Each type of scalable coding provides scalability of onedimension of the video signal
Can combine multiple types of scalability to providescalability along multiple dimensions
Video
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Scalable Coding: Temporal Scalability
Temporal scalability:Based on the use of B-framesto
refine thetemporal resolution B-frames are dependent on other frames However,no other frame depends on a B-frame Each B-frame may be discarded without affecting
other frames
PI B B PB B IB B
MPEG GOP
0 1 2 3 4 5 6 7 8 9
Video
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Scalable Coding: Spatial Scalability
Spatial scalability : Based on refining thespatial resolution Base layer is low resolutionversion of video Enh1contains coded differencebetween upsampled
base layer and original video Also called: Pyramid coding
2
EncBase layer
Enh layerEnc
2Dec
Dec
2
DecLow-Res Video
High-Res VideoOriginal Video
Video Scalable Coding: SNR (Quality)
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g (Q y)Scalability
SNR (Quality) Scalability:Based on refining the
amplitude resolution Base layer uses a coarse quantizer Enh1 applies a finer quantizer to the difference
between the original DCT coefficients and thecoarsely quantized base layer coefficients
I frame P-frame
EI frame EP frame
Note: Base & enhancementlayers are at the same spatiaresolution
Video
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Summary of Scalable Video Coding
Three basic types of scalable video coding: Temporal scalability
Spatial scalability SNR (quality) scalability
Scalable coding produces different layers with prioritized
importance Prioritized importance is key for a variety of applications: Adapting to different bandwidths, or client resources
such as spatial or temporal resolution or computationalpower
Facilitates error-resilience by explicitly identifying mostimportant and less important bits
Video
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Outline of Todays Lecture
Motivation for compression
Brief review of generic compression system (from prior lecture) Brief review of image compression (from last lecture) Video compression
Exploit temporal dimension of video signal Motion-compensated prediction Generic (MPEG-type) video coder architecture Scalable video coding
Overview of current video compression standards What do the standards specify? Frame-based video coding: MPEG-1/2/4, H.261/3/4 Object-based video coding: MPEG-4
Video
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Motivation for Standards
Goal of standards:
Ensuring interoperability:Enabling communicationbetween devices made by different manufacturers Promoting a technology or industry Reducing costs
Videod
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What do the Standards Specify?
Encoder Bitstream Decoder
VideoC di
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What do the Standards Specify?
Not the encoder Not the decoder Just the bitstream syntax and the decoding process (e.g. use IDCT,
but not how to implement the IDCT) Enables improved encoding & decoding strategies to be
employed in a standard-compatible manner
Encoder Bitstream Decoder
Scope of Standardization
(Decoding Process )
VideoC di
Current Image and Video
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Compression StandardsStandard Application Bit Rate
JPEG Continuous-tone still-imagecompression
Variable
H.261 Video telephony andteleconferencing over ISDN
p x 64 kb/s
MPEG-1 Video on digital storage media(CD-ROM)
1.5 Mb/s
MPEG-2 Digital Television 2-20 Mb/s
H.263 Video telephony over PSTN 33.6-? kb/sMPEG-4 Object-based coding, synthetic
content, interactivityVariable
JPEG-2000 Improved still image compression Variable
H.264 / MPEG-4 AVC
Improved video compression 10s to 100s kb/s
VideoC di g
Comparing Current Video Compression
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Standards
Based on the same fundamental building blocks Motion-compensated prediction (I, P, and B frames) 2-D Discrete Cosine Transform (DCT) Color space conversion Scalar quantization, runlengths, Huffman coding
Additional toolsadded for different applications: Progressive or interlaced video Improved compression, error resilience, scalability, etc.
MPEG-1/2/4, H.261/3/4: Frame-based coding MPEG-4:Object-based coding and Synthetic video
VideoCoding
MPEG Group of Pictures (GOP)
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Structure Composed of I, P, and B frames Arrows show prediction dependencies Periodic I-frames enable random access into the coded bitstream Parameters: (1) Spacing between I frames, (2) number of B frames
between I and P frames
MPEG GOP
I0
B1
B2
P3
B4
B5
P6
B7
B8
I9
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MPEG Structure
MPEG codes video in a hierarchy of layers. The
sequence layer is not shown.
P
GOP Layer Picture Layer
Macroblock Layer
Block Layer
8x8 DCT4 8x8 DCT
1 MV
Slice Layer
B
B
P
B
B
I
VideoCoding MPEG 2 P fil d L l
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MPEG-2 Profiles and Levels
Goal: To enable more efficient implementations for
different applications (interoperability points) Profile : Subset of the tools applicable for a family ofapplications
Level : Bounds on the complexity for any profile
Simple Main HighProfile
Level
Low
Main
High
DVD & SD Digital TV:Main Profile at Main Level(MP@ML)
HDTV: Main Profile atHigh Level (MP@HL)
VideoCoding
MPEG 4 N l Vid C di
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MPEG-4 Natural Video Coding
Extension of MPEG-1/2-type algorithms to codearbitrarily shaped objects
[MPEG Committee]
Frame-based Coding
Object-based Coding
Basic Idea: Extend Block-DCT and Block-ME/MC-predictionto code arbitrarily shaped objects
VideoCoding
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Example ofMPEG-4
Scene(Object-basedCoding)
[MPEG Committee]
VideoCoding
Example MPEG-4 Object Decoding Process
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[MPEG Committee]
VideoCoding Sprite Coding (Backgro nd Prediction)
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Sprite Coding (Background Prediction)
Sprite: Large background image
Hypothesis: Same background exists for many frames,changes resulting from camera motion and occlusions One possible coding strategy:
1. Code & transmit entire sprite once2. Only transmit camera motion parameters for each
subsequent frame Significant coding gain for some scenes
VideoCoding Sprite Coding Example
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Sprite Coding Example
Sprite (background) ForegroundObject
ReconstructedFrame [MPEG Committee]
VideoCoding Review of Todays Lecture
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Review of Today s Lecture
Motivation for compression Brief review of generic compression system (from prior lecture) Brief review of image compression (from last lecture) Video compression
Exploit temporal dimension of video signal
Motion-compensated prediction Generic (MPEG-type) video coder architecture Scalable video coding
Overview of current video compression standards What do the standards specify? Frame-based video coding: MPEG-1/2/4, H.261/3/4 Object-based video coding: MPEG-4
VideoCoding References and Further Reading
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References and Further Reading
General Video Compression References: J.G. Apostolopoulos and S.J. Wee, ``Video Compression Standards'',
Wiley Encyclopedia of Electrical and Electronics Engineering, John Wiley & Sons, Inc., New York, 1999.
V. Bhaskaran and K. Konstantinides,Image and Video CompressionStandards: Algorithms and Architectures, Boston, Massachusetts:
Kluwer Academic Publishers, 1997. J.L. Mitchell, W.B. Pennebaker, C.E. Fogg, and D.J. LeGall,MPEG Video Compression Standard , New York: Chapman & Hall, 1997.
B.G. Haskell, A. Puri, A.N. Netravali,Digital Video: An Introduction to MPEG-2,Kluwer Academic Publishers, Boston, 1997.
MPEG web site:http://drogo.cselt.stet.it/mpeg