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Topic for lecture 2
• Topic: video compression • The ultimate compression task?• Color image (300 x 300 x 24bit):
– 2.16Mbit/image x 30 image/s = 64.8Mbps
• Motion picture: 90min = 64.8Mbps x 60 x 90 = 349.92Gbit
• 56.6K modem => Raw download time (excl. sound and overhead) ~ 1717 hours or ~ 72 days!!!
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Agenda for lecture 2
• What makes video compression possible?
• Implementations of motion compensation– Block matching
• The YCbCr color representation
• MPEG
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Video compression • A sequence of images that needs to be
compressed: storage and/or transmission
• Ignore audio as images >> audio
• Straight forward methods– Motion JPEG – 3D DCT
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Temporal redundancy• Less than 10% of the pixels changes more than
1% between frames
• Temporal redundancy or interframe correlation
• Temporal redundancy > spatial redundancy
• Origin: slow camera- and object movements
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Motion compensated coding
• Second generation of temporal compression method• More efficient (especially with rapid changes) but also more
complex: – Ok since the cost of computer power is decreasing faster than the
cost of bandwidth
• Basic idea: only difference between two images are the moving objects (draw)
• Estimate the motion and simply code this information• From prediction and the initial frame we can encode/decode
all other frames
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Practical issues• Due to noise, camera movements, light changes etc. =>
the object and background changes =>– Calculate the predicted error (difference) and code this
• Very hard to track and describe a general object (contour and texture) instead a block of pixels is used as ’object’
• The estimated motion is represented as pure translation: no rotation and scaling– This is justified since we have high frame rates and ’slow’
changes
– Denoted the displacement vector or motion vector
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Procedure for motion compensated coding • Image sequence => image => blocks of pixels• Step 1: Motion analysis:
– Estimate the motion vector of the current block, i.e. the position of the block in the previous image(s)
• Step 2: Prediction and differentiation– Predict how the block found in the previous image(s) will look
like in the current image– Subtract the predicted block from the current block =>
difference • Step 3: Entropy encoding of the difference and motion vector• Encoded difference and motion vector << raw image =>
video compression• Step 3 we know
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Motion analysis and prediction• In general we seek the trajectory of a block so we
can predict its current position e.g. using weights• In praxis this is too complicated and instead a 0th
order predictor is applied:– Predicted block(x,y,t) = block(a,b,t-1)– MPEG uses two 0th order predictors
• The only unknown issue: step 1: how do we find the block in the previous frame that best matches the block in the current frame?
• Three methods:– Block matching (by far the most applied method)– Pel-recursion (block = 1 pixel)– Optical flow (block = 1 pixel)
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Block matching (1)• Principle• The displacement of
the pixels in a block are assumed to have the same motion vector
• Search window– Maximum from frame rate and context– Usually a square region
• Usually p=q => square block• The smaller the block size => the better prediction, but
more overhead (motion vectors)• Usually block size = 16 x 16
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Block matching (2)• Overlapping blocks improve reconstructed
image quality but decrease the bit-rate– Usually non-overlapping blocks are applies
• Block matching via a similarity measure:– Sum of squared differences (SSD): S(u,v) = (u-v)^2– Mean absolute differences (MAD): S(u,v) = |u-v|
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Searching strategies• Full search:
– Finds global minimum but requires heavy processing!
• Only one minimum in the search region => A less computational demanding search strategy
• Accept a local minimum => – Larger difference but less processing
• Searching strategies with one (local) minimum:– Coarse-fine three-step search– 2D logarithmic search– Conjugate direction search– Etc.
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Coarse-fine three-step search• Step 1) Test 9 points within a fixed pattern
• Step 2+3) Centre the pattern around the best match and change the distance within the pattern
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YCbCr color representation
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YCbCr color representation
• A camera captures color in RGB format (show)• We would like a representation where the intensity and color is separated:
– So we can transmit and decode both a color and gray-scale signal – [R,G,B]: [50,50,50] same color as [100,100,100]– HSI (hue-saturation-intensity)– HSI is complex to calculate so we seek a more simple rep.
• YUV-representation is a simple approximation:– Y = Luminance (intensity) = 0.299 R + 0.587 G + 0.114 B– The non-uniform weighting comes from the HVS– U = B – intensity = ”pure” blue color = 0.492 (B - Y)– V = R – intensity = ”pure” red color = 0.877 (R - Y)– Rough approximation but very simple to compute
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YCbCr color representation (3)• The HVS is more sensitive to intensity (Y)
than to color (Cb and Cr) so more bits can be used to represent the intensity
• Formats:
1
2
3
4
1
2
3
4
1
2
3
4
= Y sample = Cb and Cr sample
4:4:4 (24 bits) 4:2:2 (16 bits) 4:2:0 (12 bits)
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MPEG• MPEG = Moving pictures experts group• International standard for compression of video (image,
sound, and system info.), due to grows in the digital media (e.g. CD-rom, DVD) market. Both transmission and storage
• MPEG-1: 1991• MPEG-2: 1994
– MPEG-2 is MPEG-1 compatible, hence only MPEG-2 used today
• MPEG is NOT an algorithm but rather a frameworkwith several algorithms and MANY user-settings. – Fixed protocol, hence fixed decoders (encoder not specified! )– Asymmetrical codec ~ 100:1 ( JPEG ~1:1 )
• MPEG is a lossy compression algorithm
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MPEG-1• MPEG-2 is an ”add-on” to MPEG-1• Typical bit rate for MPEG-1 = 1.5Mbps
– Meaning that an MPEG-1 decoder can decode and show real-time video that has been compressed to 1.5Mbps. MPEG: Trade off between video quality and bandwidth
• Allows resolutions up to 4095 x 4095 at 60Hz– Most used is the CPB (constrained parameter bit steam)
• Fixed resolutions and frame rates =>
HW implementations
• Max. resolution = 768 x 576 at 30Hz
• Max. bit rate = 1.856Mbps
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MPEG-1 compression rate• BT.601 (digital TV-signal):• 704 x 576 x 24bit x 25Hz = 243Mbps• Compression factor: 243Mbps / 1.5Mbps = 162 • JPEG = 10-20• YCrCb 4:2:0 format: 12 bit per pixel• Basic operation: down-scale to SIF (source input format)
– Fixed resolution => HW solutions– 360 x 288 (ignore lines and/or interpolate)
• 360 x 288 x 12 x 25Hz = 30.4Mbps => comp. factor = 20• But can be higher or lower• In general: Fewer input data => better image quality (for
fixed bit rate)
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MPEG-1 principle (1)
• Full-motion-compensated DCT and difference coding
• Frames: 1,2,3,4,5,6,7,8,9, …
• 1: (DCT-JPEG)
• 2,3,4,5,6,7,8,9, … : difference coding– The difference is DCT coded and quantized =>
loosy compression– Problems? – Error propagation – No random access
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MPEG-1 principle (2)
• I-picture: intra-coded
– Similar to JPEG
• P-picture: predictive
coded via forward prediction
• B-picture: predictive coded via:
– forward-, backward-, or bi-directional prediction
• Errors in I and P are limited to max one GOP (group of pixels)
• Errors in B are limited to one picture
• High N and M => good coding but error propagation.
– Usually: 13<N<16 and 0<M<4
– Recommended: I each ½ sec. and whenever scene changes
• Coding order vs. visualisation order
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Entire sequence
16
16 Y
88Cb
88Cr
88
4:2:0-format
6 Blocks
Type: I,P,B
MB = Macro Block
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Coding one Block (8x8)
• Similar to JPEG except for adaptive quantization– DCT, quantization, zig-zag scan, entropy coding– Adaptive quantization controls the quality/amount of data– Intra vs. Inter coding:
• I-blocks: Intra
• P,B-blocks: Depending on DIFF: 0, motion vectors, Inter, Intra.
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Coding one Block (8x8)
• Encoding
• Decoding
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What to remember
• Video compression is done by removing the temporal redundancy• Principle: (at block level)
– Step 1: Motion analysis => motion vector– Step 2: Calculate the error/difference (subtraction)– Step 3: Entropy encoding of motion vector and difference
• Motion analysis:– Pel-recursion– Optical flow– Block matching (the currently applied method)
• Block matching– Block of pixels (16 x 16)– Similarity measure– Search region– Different search strategies to avoid the full search
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What to remember• Video compression is done by removing the temporal redundancy• Principle: (at (macro)block level)
– Step 1: Motion analysis (block matching) => motion vector– Step 2: Calculate the error/difference (subtraction)– Step 3: ’JPEG’-coding (DCT, quantization and entropy encoding)
• MPEG-1: – Bit rate ~1.5Mbps– Asymmetrical codec ~ 100:1 ( JPEG ~1:1 )– Compression rate < 400 (down scaling + YCbCr 4:2:0 => ~20)– Coding-style: I B B P B B P B B I
• Questions?• Presentations: email me [email protected]• The end
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Xtras
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Pel-recursion (1)• The block consists of only one pixel (= pel)• Problem formulation:
– Displaced frame difference function: – DFD(x,y,dx,dy) = i(x,y,t) – i(x-dx,y-dy,t-1)– Find (dx,dy) which minimises DFD^2 =>
most similar pixel => best displacement vector
• Solution:– Setting the partial derivatives = 0– Non-linear programming problem:
• Iterative algorithm• Steepest decent method• Newton-Raphson’s method• others
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Pel-recursion (2)• Algorithm:• Find the motion vector (dx,dy) for the first pixel• The motion vectors
are correlated =>– Use ’old’ (dx,dy) as
initial guess for the iterative algorithm =>recursion
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Optical flow
• The block consists of only one pixel
• Similar to Pel-recursive but calculated in a different manner
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Comparing the 3 types of motion analysis
• The three: pel-recursion, optical flow and block matching • Optical flow and pel-recursion calculated one motion
vector for each pixel =>– More precise => predicted block and current block are more
similar => smaller difference => more compact coding of the difference.
– More overhead as more motion vectors are to be coded– More complex to calculate– Pixel methods avoid the block artefacts of block matching
• Block matching is (at present) more suitable– Used in all coding standards
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Temporal methods
• Two methods which exploit both the spatial and temporal redundancies– Frame replenishment– Motion compensation
• Both utilise prediction => short summery
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Frame replenishment (1)
• Exploit the temporal redundancy• First generation of temporal compression method• If: value changed significantly:
| i(x,y,t) – i(x,y,t-1) | > TH • Then: code value and position: i(x,y,t) x,y• Else: code nothing => re-use i(x,y,t-1)• Enhancements:
– Send differences instead of values– Remove noise from the images prior to processing
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Frame replenishment (2)
• A fixed bit rate of 1Mbps means that the decoder can only decode and play-back real-time video compressed to 1Mbps
• Many changes between two images => many pixels to be coded.
• To achieve the same bit rate => TH is higher
=> only large changes are coded => poorer reconstructionaka. the dirty window effect
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2D logarithmic search• Test 5 points within a fixed pattern
• Centre the pattern around the best match
• When best match is in the centre or on the border: reduce distance in pattern
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Conjugate direction search• Step 1: Test 3 vertical points next to each other
• Step 2: Move to minimum point
• Continue step 1 and 2 until a minimum is found. Then repeat the process in the vertical direction
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YCbCr color representation (2)
• YUV-representation can have negative values, so YUV-representation is scaled and shifted to avoid this => YCbCr-representation
• Cb and Cr are denoted the chrominances
• YCbCr is the representation utilised in image/video compression
YUV
0.299 0.587 0.114-0.147 -0.289 0.436 0.615 -0.515 -0.100
=RGB
YCbCr
0.257 0.504 0.098-0.148 -0.291 0.439 0.439 -0.368 -0.071
=RGB
+ 16128128
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Audio in MPEG-1• 16 bit sampled at: 16, 22.05, 24, 32, 44.1 and 48Kbps
• Stereo at 44.1Kbps = 1.4Mbps• Compression based on psycho-acoustic redundancy:• Three methods:
– Layer 1: Target rate = 384Kbps– Layer 2: Target rate = 256Kbps– Layer 3: Target rate = 128Kbps
• Layer 3 is the most advanced and often applied– It has a nickname, which?
dB
Hz
dB
Hz
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MPEG-2• Defined in 1994• Developed for DTV but has lots of other applications• Based on MPEG-1 (backward compatible) • Bit rates: 1.5Mbps – 60Mbps. Target: 2-15Mbps (best: 4)• Lots of new features including:
– Support for fields, support for 4:4:4 and 4:2:2
– Alternative zig-zag scan, better motion vectors
– Scalability to allow any subset of a stream to be decoded and visualised, etc.
• MPEG-3: Purpose: HDTV – Merged with MPEG-2 => no MPEG-3 standard
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MPEG-4• Both for real video and synthetic video• Very low bit rates < 64Kbps => efficient coding• Content based coding: code the objects
– Shape, texture and sprite (background objects)
• Interactivity• Popular coding
standards: