Introduction to Video Compression
`
`Insight, Analysis, and Advice on Signal Processing Technology
`
`Introduction to Video Compression
`
`Jeff Bier
`Berkeley Design Technology, Inc.
`
`info@BDTI.com
`http://www.BDTI.com
`
`© 2005 Berkeley Design Technology, Inc.
`
`Outline
`• Motivation and scope
`• Still-image compression techniques
`• Motion estimation and compensation
`• Reducing artifacts
`• Color conversion
`• Conclusions
`
`© 2005 Berkeley Design Technology, Inc.
`
`2
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`© 2005 Berkeley Design Technology, Inc.
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`GSPx
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`October 2005
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`SAMSUNG-1023
`
`
`
`Insight, Analysis, and Advice on Signal Processing Technology
`
`Introduction to Video Compression
`
`Jeff Bier
`Berkeley Design Technology, Inc.
`
`info@BDTI.com
`http://www.BDTI.com
`
`© 2005 Berkeley Design Technology, Inc.
`
`Outline
`• Motivation and scope
`• Still-image compression techniques
`• Motion estimation and compensation
`• Reducing artifacts
`• Color conversion
`• Conclusions
`
`© 2005 Berkeley Design Technology, Inc.
`
`2
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`© 2005 Berkeley Design Technology, Inc.
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`GSPx
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`October 2005
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`SAMSUNG-1023
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`
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`Introduction to Video Compression
`
`Motivation and Scope
`• Consumer video products increasingly rely on video
`compression
`• DVDs, digital TV, personal video recorders, Internet video,
`multimedia jukeboxes, video-capable cell phones and PDAs,
`camcorders…
`• Video product developers need to understand the
`operation of video “codecs”
`• To select codecs, processors, software modules
`• To optimize software
`• This presentation covers:
`• Operation of video codecs and post-processing
`• Computational and memory demands of key codec and post-
`processing components
`
`© 2005 Berkeley Design Technology, Inc.
`
`Outline
`• Motivation and scope
`• Still-image compression techniques
`• Motion estimation and compensation
`• Reducing artifacts
`• Color conversion
`• Conclusions
`
`© 2005 Berkeley Design Technology, Inc.
`
`© 2005 Berkeley Design Technology, Inc.
`
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`Introduction to Video Compression
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`Still-Image Compression
`• Still-image compression
`• Still-image techniques provide a basis for video
`compression
`• Video can be compressed using still-image
`compression individually on each frame
`• E.g., “Motion JPEG” or MJPEG
`• But modern video codecs go well beyond this
`• Start with still-image compression techniques
`• Add motion estimation/compensation
`• Takes advantage of similarities between frames in a video
`sequence
`
`© 2005 Berkeley Design Technology, Inc.
`
`Still-Image Compression
`
`Transform
`
`Quantization
`
`Run-length coding
`
`Variable length coding
`
`Frame
`Header
`
`Frame
`Header
`
`Typical Still-Image Compression Data Flow
`
`© 2005 Berkeley Design Technology, Inc.
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`© 2005 Berkeley Design Technology, Inc.
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`Introduction to Video Compression
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`Block Transform: 8x8 DCT
`• 8x8 DCT blocks applied on Y, U,
`and V planes individually
`• The energy is concentrated in the
`low frequencies
`• Perceptual information also
`concentrated in low frequencies
`
`Y values
`
`8x8 DCT
`
`8x8 IDCT
`
`High energy
`Low energy
`
`Spatial domain
`
`Frequency domain
`
`© 2005 Berkeley Design Technology, Inc.
`
`Block Transform: Resource Reqt’s
`• Compute load:
`• Up to 30% of total video decoder processor cycles
`• MPEG-4 CIF (352x288) @ 30 fps:
`• 71,280 DCTs/s
`• ~40 MHz on a TMS320C55x DSP
`• ~10 MHz if using TMS320C55x DCT accelerator
`• Many implementation and optimization options
`• Can make compute requirements hard to predict
`• Memory usage: negligible
`
`© 2005 Berkeley Design Technology, Inc.
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`© 2005 Berkeley Design Technology, Inc.
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`Introduction to Video Compression
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`Quantizer
`
`•Removes perceptually
`less significant data
`•Reduces the number of
`bits per DCT coefficient
`9
`
`Quantization
`
`Original
`
`Reconstructed
`
`Spatial domain
`
`Frequency
`domain
`
`© 2005 Berkeley Design Technology, Inc.
`
`Quantization: Resource Reqt’s
`• Quantization (encoder) and dequantization
`(decoder and encoder) have similar compute
`loads
`• Compute load:
`• From 3% to about 15% of total decoder
`processor cycles
`• Typically near the lower end of this range
`• MPEG-4 CIF (352x288) @ 30 fps:
`• ~10 MHz on a TMS320C55x DSP (estimated)
`• Memory usage: negligible
`
`© 2005 Berkeley Design Technology, Inc.
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`Introduction to Video Compression
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`Coding Quantized DCT Coefficients
`Goal: Reduce the number of bits required to
`transmit the quantized coefficients
`
`Occurrence
`
`Quantized DCT values
`
`Observation: Unequal distribution of quantized
`DCT coefficient values
`
`© 2005 Berkeley Design Technology, Inc.
`
`11
`
`Variable Length Coding (VLC/VLD)
`• Allocates fewer bits to the most frequent symbols
`(e.g., using Huffman)
`• Integer number of bits per symbol
`• Not the most efficient coding method
`• Arithmetic coding more efficient, but expensive
`• Run-length coding improves efficiency of VLC/VLD for
`image and video coding
`
`Code
`
`10
`
`11
`0101
`0100
`0011
`0010
`...
`
`Frequency
`22
`16
`
`9742.
`
`..
`
`Symbol
`
`ABCDEF.
`
`..
`
`© 2005 Berkeley Design Technology, Inc.
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`
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`Introduction to Video Compression
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`Run-Length Coding
`Encodes value and number of successive
`occurrences
`Takes advantage of the high number of
`recurring zeros
`
`20,1 ; 18,1 ; 20,1 ; 18,4 ; ... ; 0, 37
`
`© 2005 Berkeley Design Technology, Inc.
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`13
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`Variable Length Coding: Processing
`Reqt’s
`• VL decoding much more computationally demanding
`than VL encoding
`• VLD compute load:
`• Up to 25% of total video decoder processor cycles
`• MPEG-4 CIF (352x288) @ 30 fps, 700 kbps:
`• ~15-25 MHz on a TMS320C55x DSP (estimated)
`• About 11 operations per bit on average
`• Memory usage
`• A few KB of memory for lookup tables
`• More for speed optimizations
`
`© 2005 Berkeley Design Technology, Inc.
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`Introduction to Video Compression
`
`Resynchronization Markers
`• Without markers, a single bit error in the coded
`bitstream prevents decoding of the rest of the frame
`• Size of a corrupted variable-length code word is unknown
`• Therefore, the start of the next code word (and all following
`code words) is unknown
`• Resynchronization markers help the decoder recover
`from bitstream errors
`• Provide a known bit pattern interspersed throughout the
`bitstream
`• In case of an error, decoder searches for next marker, then
`continues decoding
`
`Corrupted codeword
`
`Un-decodable data
`
`Frame
`Header
`
`Frame
`Header
`
`© 2005 Berkeley Design Technology, Inc.
`
`Resynchronization markers
`
`15
`
`Reversible VLC
`• Reversible VLC, in conjunction with resynchronization
`markers, further assists error recovery
`• Code words can be decoded forward and backward
`• In case of error, data can be decoded backward from the
`next resynchronization marker
`• More data is recovered compared to resynchronization
`markers alone
`
`Corrupted codeword
`
`Frame
`Header
`
`Data decoded
`backward
`
`Normal decoding
`continues forward
`
`Frame
`Header
`
`Resynchronization markers
`
`© 2005 Berkeley Design Technology, Inc.
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`
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`Introduction to Video Compression
`
`AC-DC Prediction Spatial
`domain
`
`Frequency
`domain
`
`Current block
`Candidates not selected
`for prediction
`Candidate selected for
`prediction
`
`AC-DC prediction: encode first
`differential row and column
`DC prediction: encode
`differential DC value
`
`© 2005 Berkeley Design Technology, Inc.
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`17
`
`AC-DC Prediction
`
`• AC-DC prediction cannot be used in
`conjunction with motion compensation
`• ⇒ Used mostly for compressing a
`single image
`• DC prediction used in JPEG
`• AC-DC prediction often uses simple
`filters to predict each coefficient value
`from one or more adjacent blocks
`
`© 2005 Berkeley Design Technology, Inc.
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`Introduction to Video Compression
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`AC-DC Prediction: Processing Reqt’s
`• Compute load:
`• DC prediction has negligible load
`• AC-DC prediction used in about 8% of frames in
`typical video
`• Negligible average load (~2% of processor cycles in
`decoder)
`• Substantial per-frame load (~20-30% of cycles required to
`decode a frame that uses AC-DC prediction)
`• Memory usage:
`• Under 2 KB for CIF (352x288) resolution
`• But more memory (up to 10 KB) can result in faster code
`• May be overlapped with other memory structures
`not in use during prediction
`
`© 2005 Berkeley Design Technology, Inc.
`
`19
`
`Outline
`• Motivation and scope
`• Still-image compression techniques
`• Motion estimation and compensation
`• Reducing artifacts
`• Color conversion
`• Conclusions
`
`© 2005 Berkeley Design Technology, Inc.
`
`20
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`© 2005 Berkeley Design Technology, Inc.
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`GSPx
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`Page 10
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`October 2005
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`
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`Introduction to Video Compression
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`Motion Estimation and Compensation
`• Still image compression ignores the
`correlation between frames of video
`• JPEG achieves ~10:1 compression ratio
`• Wavelet transform-based image coding reaches
`compression ratios up to ~30:1
`• Adding motion estimation and compensation
`results in much higher compression ratios
`• Good video quality at compression ratios as high
`as ~200:1
`
`© 2005 Berkeley Design Technology, Inc.
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`21
`
`• P frame depends on previously
`displayed reference frame
`
`Motion Estimation and Compensation
`• I frame is encoded as a still
`• Requires at least one
`image and doesn’t depend on
`“reference frame”
`any reference frame
`• Reference frame must be
`I
`encoded before the
`current frame
`• But, reference frame can
`be a future frame in the
`display sequence
`• Three kinds of frames:
`I, P, and B
`
`P
`
`• B frame depends on previous
`and future reference frames
`
`© 2005 Berkeley Design Technology, Inc.
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`22
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`B
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`© 2005 Berkeley Design Technology, Inc.
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`Introduction to Video Compression
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`Typical Sequence of I, P, B frames
`
`B
`
`B
`
`P
`
`B
`
`I
`
`Display order (left to right)
`
`I
`
`B
`
`P
`
`B
`
`B
`
`P
`
`B
`
`B
`
`B
`
`Encoding order (top to bottom)
`
`© 2005 Berkeley Design Technology, Inc.
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`23
`
`Motion Estimation
`• Predict the contents of each macroblock
`based on motion relative to reference frame
`• Search reference frame for a 16x16 region that
`matches the macroblock
`• Encode motion vectors
`• Encode difference between predicted and actual
`macroblock pixels
`
`MV
`
`MV=
`Motion
`Vector
`
`Reference frame
`© 2005 Berkeley Design Technology, Inc.
`
`Current frame
`
`24
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`© 2005 Berkeley Design Technology, Inc.
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`
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`Introduction to Video Compression
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`Video Encoder Block Diagram
`
`(Current MB)
`Input
`*MB
`
`+
`
`Diff block
`(2.5%)
`
`-
`
`8x8 DCT
`(3.4%)
`
`Intra/Inter
`mode
`
`0
`
`Motion
`Compensation
`(1%)
`
`Motion Estimation (63.4%):
`-
`Fast Integer ME: 37%
`- Half-Pel + 4MV refine: 26%
`
`Quantize
`(0.4%)
`
`Inv-Quantize
`(0.5%)
`
`8x8 IDCT
`(1.7%)
`
`+
`
`+
`Add block
`(2.9%)
`
`Frame
`Store
`
`(Reference MB)
`
`VLC + scan
`(7.3%)
`
`Bitstream
`MUX
`
`Bitstream Out
`
`Encode Intra MB
`(1.8%)
`
`Encode Inter MB
`control code
`(3.9%)
`
`Misc functions
`(4.1%)
`
`MV Coder
`
`© 2005 Berkeley Design Technology, Inc.
`
`*MB – Macro-Block Source: Motorola SPS
`
`25
`
`Motion Estimation: The Problem
`Search area
`
`• Search on 16x16 blocks
`• Typically on luminance only
`
`• Sub-pixel interpolation required
`for non-integer motion vectors
`
`Σ|Y[i]|2
`SSD
`
`or
`Σ|Y[i]|
`SAD
`
`• SAD more often used
`
`© 2005 Berkeley Design Technology, Inc.
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`26
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`© 2005 Berkeley Design Technology, Inc.
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`October 2005
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`
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`Introduction to Video Compression
`
`Motion Estimation: Efficient Motion
`Vector Search
`• Exhaustive search is impractical
`• Evaluate only promising candidate
`motion vectors
`• Don’t have to find absolute best match
`• Trade video quality for computational load
`• Many methods in use
`• Often proprietary
`• Refine candidate vector selection in stages
`• Predict candidate vectors from surrounding
`macroblocks and/or previous frames
`
`~
`
`~Σ
`
`Motion vector search approach is a key differentiator
`between video encoder implementations
`
`© 2005 Berkeley Design Technology, Inc.
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`27
`
`Motion Estimation: Processing
`Reqt’s
`• Compute load
`• Most demanding task in video compression
`• Up to 80% of total encoder processor cycles
`• Many search methods exist; requirements vary by method
`• May vary with video program content
`• Makes encoder computational demand several times
`greater than that of the decoder
`• Dominated by SAD computation
`• Memory usage
`• Motion estimation requires reference frame buffers
`• Frame buffers dominate the memory requirements of the
`encoder
`• E.g., 152,064 bytes per frame @ CIF (352x288) resolution
`• High memory bandwidth required
`© 2005 Berkeley Design Technology, Inc.
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`October 2005
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`
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`Introduction to Video Compression
`
`Motion Compensation: Processing
`Reqt’s
`• Motion compensation copies pixels from reference
`frame to predict current macroblock
`• Requires interpolation for non-integer motion vector values
`• Compute load
`• Varies with video program content
`• Can require from 5% to 40% of total decoder processor
`cycles
`• MPEG-4 CIF (352x288) @ 30 fps:
`• Roughly 15-25 MHz on a TMS320C55x DSP (estimated)
`• Memory usage
`• Requires reference frame buffers
`• Frame buffers dominate decoder memory requirements
`• Good memory bandwidth is desirable, but less critical
`compared to motion estimation
`
`© 2005 Berkeley Design Technology, Inc.
`
`29
`
`Outline
`• Motivation and scope
`• Still-image compression techniques
`• Motion estimation and compensation
`• Reducing artifacts
`• Color conversion
`• Conclusions
`
`© 2005 Berkeley Design Technology, Inc.
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`30
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`© 2005 Berkeley Design Technology, Inc.
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`GSPx
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`October 2005
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`
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`Introduction to Video Compression
`
`Artifacts: Blocking and Ringing
`• Blocking: Borders of 8x8 blocks
`visible in reconstructed frame
`
`• Ringing: Distortions near edges of image
`features
`
`Original
`image
`
`Reconstructed
`image
`(with ringing
`Artifacts)
`
`© 2005 Berkeley Design Technology, Inc.
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`31
`
`Deblocking and Deringing Filters
`Low-pass filters are used to smooth the
`image where artifacts occur
`• Deblocking:
`• Low-pass filter the pixels at borders of 8x8 blocks
`• One-dimensional filter applied perpendicular to 8x8
`block borders
`• Deringing:
`• Detect edges of image features
`• Adaptively apply 2D filter to smooth out areas near
`edges
`• Little or no filtering applied to edge pixels in order
`to avoid blurring
`
`© 2005 Berkeley Design Technology, Inc.
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`Introduction to Video Compression
`
`Artifact Reduction
`Example: Deblocking
`
`Original MPEG Still Frame
`
`Horizontally & Vertically
`Deblocked Still Frame
`
`© 2005 Berkeley Design Technology, Inc.
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`33
`
`Artifact Reduction: Post-processing
`vs. In-loop
`• Deblocking/deringing often applied after
`the decoder (post-processing)
`• Reference frames are not filtered
`• Developers free to select best filters for the
`application or not filter at all
`
`Video
`Decoder
`
`Deblock
`Dering
`
`Reference
`frame
`
`• Deblocking/deringing can be incorporated
`in the compression algorithm (in-loop
`filtering)
`• Reference frames are filtered
`• Same filters must be applied in encoder and
`decoder
`• Better image quality at very low bit-rates
`
`Video
`Decoder
`
`Deblock
`Dering
`
`Reference
`frame
`
`© 2005 Berkeley Design Technology, Inc.
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`Introduction to Video Compression
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`Artifact Reduction: Processing Reqt’s
`• Deblocking and deringing filters can require
`more processor cycles than the video decoder
`• Example: MPEG-4 Simple Profile, Level 1
`(176x144, 15 fps) decoding requires 14 MIPS on
`ARM’s ARM9E for a relatively complex video
`sequence
`• With deblocking and deringing added, load
`increases to 39 MIPS
`• Nearly 3x increase compared to MPEG-4 decoding alone!
`• Post-processing may require an additional
`frame buffer
`
`© 2005 Berkeley Design Technology, Inc.
`
`35
`
`Outline
`• Motivation and scope
`• Still-image compression techniques
`• Motion estimation and compensation
`• Reducing artifacts
`• Color conversion
`• Conclusions
`
`© 2005 Berkeley Design Technology, Inc.
`
`36
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`© 2005 Berkeley Design Technology, Inc.
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`GSPx
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`October 2005
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`
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`Introduction to Video Compression
`
`Color Space Conversion
`• Need for color conversion
`• Capture and display video equipment: RGB...
`• ...while codecs use YUV
`• Computational demand
`• 12 operations per pixel ⇒ 36 million
`operations/second for CIF (352x288) @ 30 fps
`• About 36 MHz on a TMS320C55x DSP
`• Not including interpolation of chrominance planes
`• Roughly 1/3 to 2/3 as many processor cycles as
`the video decoder
`
`© 2005 Berkeley Design Technology, Inc.
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`37
`
`Outline
`• Motivation and scope
`• Still-image compression techniques
`• Motion estimation and compensation
`• Reducing artifacts
`• Color conversion
`• Conclusions
`
`© 2005 Berkeley Design Technology, Inc.
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`Introduction to Video Compression
`
`Conclusions
`Understanding the computational and memory
`requirements of video compression is critical but
`challenging
`• Application design choices are driven by computational and
`memory requirements
`• Algorithm selection, processor selection, software optimization
`• Video processing often stresses processing resources
`• But video applications combine many different signal-
`processing techniques
`• Transforms, prediction, quantization, entropy coding, image
`filtering, etc.
`• And there is large variation in computational and memory
`requirements among different applications
`• E.g., digital camcorder has vastly different requirements from a
`video-enabled cell phone, even when using the same
`compression standard
`
`© 2005 Berkeley Design Technology, Inc.
`
`39
`
`Conclusions, cont.
`• Understanding computational load
`• Computational load of encoder is several times greater than
`that of decoder due to motion estimation
`• Computational load proportional to frame size and rate for
`most functions
`• Note: VLD computational load is proportional to bit rate
`• Post-processing steps—deblocking, deringing, color space
`conversion—add considerably to the computational load
`• Computational load can be difficult to predict
`• Many different approaches to motion estimation
`• Computational load of some tasks can fluctuate wildly
`depending on video program content
`• E.g., motion compensation
`
`© 2005 Berkeley Design Technology, Inc.
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`October 2005
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`
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`Introduction to Video Compression
`
`Conclusions, cont.
`• Understanding memory requirements:
`• Memory requirements dominated by frame buffers
`• A decoder that supports only I and P frames requires two frame
`buffers (current and reference)
`• A decoder that supports I, P, and B frames requires three
`buffers (current and two reference)
`• Deblocking/deringing/color conversion may require an additional
`buffer
`• Program memory, tables, other data comprise a non-
`negligible portion of memory use
`• But this portion is still several times smaller than frame buffers
`• High memory use often necessitates off-chip memory
`• Off-chip memory bandwidth can be a performance bottleneck
`
`© 2005 Berkeley Design Technology, Inc.
`
`41
`
`Conclusions, cont.
`• Video compression used in many products
`• DVDs, digital TV, personal video recorders, Internet video,
`multimedia jukeboxes, video-capable cell phones and PDAs,
`camcorders…
`• Different products have different needs
`• Wide range of frame sizes and rates, video quality, bit rates,
`post-processing options, etc.
`• Result in wide range of computational and memory
`requirements
`• Need to understand the operation of video codecs
`• To understand computational and memory requirements
`• To select codecs, processors, software modules
`• To optimize software
`
`© 2005 Berkeley Design Technology, Inc.
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`42
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`© 2005 Berkeley Design Technology, Inc.
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`October 2005
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`
`
`Introduction to Video Compression
`
`For More Information…
`www.BDTI.com
`
`Inside [DSP] newsletter and quarterly reports
`Benchmark scores for dozens of processors
`Pocket Guide to Processors for DSP
`• Basic stats on over 40 processors
`Articles, white papers, and presentation slides
`• Processor architectures and performance
`• Signal processing applications
`• Signal processing software optimization
`comp.dsp FAQ
`
`© 2005 Berkeley Design Technology, Inc.
`
`2004 Edition
`
`43
`
`© 2005 Berkeley Design Technology, Inc.
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`GSPx
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`Page 22
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`October 2005
`
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