VIDEO CODECS

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Information about VIDEO CODECS

Published on June 7, 2016

Author: VinayagamMariappan1

Source: slideshare.net

1. Director, eSILICON LABS, INDIA VIDEO CODEC Vinayagam M Next Generation Broadcasting Technology

2. 2

3. 3 Agenda HVS Images / Video Video / Image Compression Image Coding Video Coding Video Coder Architecture Video Codec Standards HEVC

4. 4 HVS

5. 5 HVS • HVS properties influence the design/tradeoffs of imaging/video systems • Basic properties of HVS “front-end” – 4 types of photo-receptors in the retina – Rods, 3 types of cones • Rods – Achromatic (no concept of color) – Used for scotopic vision (low light levels) – Concentrated in periphery • Cones – 3 types: S - Short, M- Medium, L - Long – Red, Green, and Blue peaks – Used for Photopic Vision (daylight levels) – Concentrated in fovea (center of the retina)

6. 6 HVS… • Eyes, optic nerve, parts of the brain • Transforms electromagnetic energy • Image Formation – Cornea, Sclera, Pupil, Iris, Lens, Retina, Fovea • Transduction – Retina, Rods, and Cones • Processing – Optic Nerve, Brain • Retina and Fovea – Retina has photosensitive receptors at back of eye – Fovea is small, dense region of receptors Only cones (no rods) Gives visual acuity – Outside Fovea Fewer receptors overall Larger proportion of rods Fov ea Retina

7. 7 HVS… • Transduction (Retina) – Transform light to neural impulses – Receptors signal bipolar cells – Bipolar cells signal ganglion cells – Axons in the ganglion cells form optic nerve • Image Formation in the Human Eye

8. 8 HVS… • HVS Properties – Tradeoff in resolution between space and time Low resolution for high spatial AND high temporal frequencies However, eye tracking can convert fast-moving object into low retinal frequency – Achromatic versus chromatic channels Achromatic channel has highest spatial resolution Yellow/Blue has lower spatial resolution than Red/Green channel – Color refers to how we perceive a narrow band of electromagnetic energy Source, Object, Observer

9. 9 HVS… • Visual System – Visual system transforms light energy into sensory experience of sight

10. 10 HVS… • Color Perception (Color Theory) – Hue Distinguishes named colors, e.g., RGB Dominant wavelength of the light – Saturation Perceived intensity of a specific color How far color is from a gray of equal intensity – Brightness (lightness) Perceived intensity Hue Scale SaturationOriginallightness

11. 11 HVS… • Visual Perception – Resolution and Brightness – Spatial Resolution depends on Image Size Viewing Distance – Brightness Perception of brightness is higher than perception of color Different perception of primary colors Relative Brightness: green:red:blue=59%:30%:11% – B/W vs. Color

12. 12 HVS… • Visual Perception – Temporal Resolution Effects caused by inertia of human eye Perception of 16 frames/second as continuous sequence Special Effect: Flicker Flicker Perceived if frame rate or refresh rate of screen too low (<50Hz) Especially in large bright areas Higher refresh rate requires Higher scanning frequency Higher bandwidth

13. 13 HVS… • Visual Perception Influence – Viewing distance – Display ratio (width/height – 4/3 for conventional TV) – Number of details still visible – Intensity (luminance)

14. 14 HVS… • Imaging / Visual System designed based on HVS principles • Example – Image Sensor – Television – Image / Video Display • Image Sensor – CCD (charge coupled device): Arrays of photo diodes Linearity Less light needed Electronic shuttering – CMOS Cheaper Easy manufacturing • Television – NTSC (National Television System Committee): 60 Hz, 30 fps, 525 scan lines North America, Japan, Korea …. – PAL (Phase Alteration by Line): 50 Hz, 25 fps, 625 scan lines Europe … • Image / Video Display – CRT Monitor – LCD TV/Display Monitor

15. 15 IMAGE / VIDEO

16. 16 IMAGE / VIDEO • Images – View Observation by HVS @ time instant – A multidimensional array of numbers (such as intensity image) or vectors (such as color image) Each component in the image called pixel associates with the pixel value (a single number in the case of intensity images or a vector in the case of color images)             39871532 22132515 372669 28161010             39656554 42475421 67965432 43567065             99876532 92438585 67969060 78567099

17. 17 IMAGE / VIDEO… • Video – Series of Frames (or Images)

18. 18 IMAGE / VIDEO… • Images / Video Frame – A multidimensional function of spatial coordinates – Spatial Coordinate (x,y) for 2D case such as photograph, (x,y,z) for 3D case such as CT scan images (x,y,t) for movies – The function f may represent intensity (for monochrome images) or color (for color images) or other associated values Image “After snow storm” f(x,y) x y Origin

19. 19 IMAGE / VIDEO… • Images / Video Frame – An image that has been discretized both in Spatial coordinates and associated value Consist of 2 sets:(1) a point set and (2) a value set Can be represented in the form – I = {(x,a(x)): x ε X, a(x) ε F} where X and F are a point set and value set, respectively An element of the image, (x,a(x)) is called a pixel where x is called the pixel location and a(x) is the pixel value at the location x – Conventional Coordinate for Image Representation

20. 20 IMAGE / VIDEO… • Images / Video Frame Representation – Basic Unit : Pixel – Dimensions Height Width – Frame rate determines how long the pixel exists, i.e. how it moves – Color Depth of the pixel How many bits are used to represent the color of each pixel?

21. 21 IMAGE / VIDEO… • Image Type – Binary Image – Intensity Image – Color Image – Index image

22. 22 IMAGE / VIDEO… • Binary Image – Binary image or black and white image – Each pixel contains one bit 1 represent white 0 represents black             1111 1111 0000 0000 Binary Data

23. 23 IMAGE / VIDEO… • Intensity Image – Intensity / Monochrome/ Gray Scale Image – Each pixel corresponds to light intensity normally represented in gray scale (gray level)             39871532 22132515 372669 28161010 Gray Scale Values

24. 24 IMAGE / VIDEO… • Color Image – Each pixel contains a vector representing red, green and blue components             39871532 22132515 372669 28161010             39656554 42475421 67965432 43567065             99876532 92438585 67969060 78567099 RGB Components

25. 25 IMAGE / VIDEO… • Index Image – Each pixel contains index number pointing to a color in a color table           256 746 941 Index Value Index No. Red component Green component Blue component 1 0.1 0.5 0.3 2 1.0 0.0 0.0 3 0.0 1.0 0.0 4 0.5 0.5 0.5 5 0.2 0.8 0.9 … … … … Color Table

26. 26 IMAGE / VIDEO… • Colourspace Representations – RGB (Red, Green, Blue) – Basic analog components (from camera/to TV) – YPbPr (Y,B-Y,R-Y) – ANALOG Colourspace (derived from RGB) Y=Luminance, B=Blue, – R=Red – YUV – Colour difference signals scaled to be modulated on a composite carrier – YIQ – Used in NTSC. I=In-phase, Q=Quadrature (IQ plane is 33o rotation of UV plane) – YCbCr/YCC – DIGITAL representation of the YPbPr Colourspace (8bit, 2s compliment)

27. 27 IMAGE / VIDEO… • RGB Color – All color can be composed by adding specific amounts of R, G, & B – 8-bits (28) specifies the amount of each color – This is the scheme used by most electronic displays to generate color; e.g. we often call our computer monitors, "RGB displays" 8-bits Red 8-bits Green 8-bits Blue

28. 28 IMAGE / VIDEO… • Color Reduction – Human eye is not as sensitive to color as it is to Luminance – To this end, to save costs the various standards decided to Maintain luminance information in our images, but Reduce color information Using RBG, though, how do we easily reduce color information without removing luminance? For this, and other technical reasons, a separate color space was chosen by most video standards …

29. 29 IMAGE / VIDEO… • Colour Image: RGB • YCbCr – Even though most displays actually use RGB to create the image, YCbCr is used most often in consumer electronics for transmission of the image – Historically, B/W televisions transmitted only luminance (Y) – The color signals were added later

30. 30 IMAGE / VIDEO… • YCbCr Generated By Sub sampling – YUV 4:4:4 = 8bits per Y,U,V channel (no downsampling the chroma channels) – YUV 4:2:2 = 4 Y pixels sampled for every 2 U and 2 V (2:1 horizontal downsampling, no vertical downsampling – YUV 4:2:0 = 2:1 horizontal downsampling, 2:1 vertical downsampling – YUV 4:1:1 = 4 Y pixels sampled for every 1 U and 1 V (4:1 horizontal downsampling, no vertical downsampling) • YUV 4:4:4 Y Y Y Y Y Y Y Y 4:4:4 Format (3 bytes/pixel): Cb Cr Cb Cr Cb Cr Cb Cr Cb Cr Cb Cr Cb Cr Cb Cr

31. 31 IMAGE / VIDEO… • YUV 4:2:2 • YUV 4:2:0 Y Y Y Y Y Y Y Y 4:2:2 Format (2 bytes/pixel): Cb Cr Cb Cr Cb Cr Cb Cr Y Y Y Y Y Y Y Y Cb Cr Cb Cr 4:2:0 Format (1.5 bytes/pixel):

32. 32 IMAGE / VIDEO… • Up sampling • Downsampling nT Input Signal 1 2 3 4 F(nT) F(nT/2) nT Intermediate Signal 12345678 Interpolating low-pass filter nT nT F(nT/2) Output Signal 12345678 nT Input Signal 1 2 3 4 F(nT) Decimating low-pass filter prevents alias at lower rate F(2nT) 1 Output Signal 2

33. 33 IMAGE / VIDEO… • RGB to YCbCr • RGB to YUV Conversion – Y = 0.299R + 0.587G + 0.114B – U= (B-Y)*0.565 – V= (R-Y)*0.713 U-V plane at Y=0.5 Clamp the output: Y=[16, 235], U,V=[16,239]

34. 34 VIDEO / IMAGE COMPRESSION

35. 35 VIDEO/IMAGE COMPRESSION • How can we use fewer bits? • To understand how image/audio/video signals are compressed to save storage and increase transmission efficiency • Reduces signal size by taking advantage of correlation – Spatial – Temporal – Spectral

36. 36 VIDEO/IMAGE COMPRESSION… • Compression Methods • Need to take advantage of redundancy – Images Space Frequency – Video Space Frequency Time Linear Predictive AutoRegressive Polynomial Fitting Model-Based Huffman Statistical Arithmetic Lempel-Ziv Universal Lossless Spatial/Time-Domain Subband Wavelet Filter-Based Fourier DCT Transform-Based Frequency-Domain Lossy Waveform-Based Compression Methods

37. 37 VIDEO/IMAGE COMPRESSION… • Need to take advantage of redundancy RGB YCbCr Blocks Macro Blocks I B P Remove Temporal Redundancy Transform QuantizationCoding 01100010101,0 Remove Spatial Redundancy Motion Compensation

38. 38 VIDEO/IMAGE COMPRESSION… • Spatial Redundancy – Take advantage of similarity among most neighboring pixels • RGB to YUV – Less information required for YUV (humans less sensitive to chrominance) • Macro Blocks – Take groups of pixels (16x16) • Discrete Cosine Transformation (DCT) – Based on Fourier analysis where represent signal as sum of sine's and cosine’s – Concentrates on higher-frequency values – Represent pixels in blocks with fewer numbers • Quantization – Reduce data required for coefficients • Entropy coding – Compress

39. 39 VIDEO/IMAGE COMPRESSION… • Spatial Redundancy Reduction Zig-Zag Scan, Run-length coding Quantization • major reduction • controls ‘quality’ “Intra-Frame Encoded”

40. 40 VIDEO/IMAGE COMPRESSION… • When may spatial redundancy elimination be ineffective? – High-resolution images and displays – May appear ‘coarse’ • What kinds of images/movies? – A varied image or ‘busy’ scene – Many colors, few adjacent Original (63 kb) Low (7kb) Very Low (4 kb)Due to Loss of Resolution Solution ? Temporal Redundancy Reduction

41. 41 VIDEO/IMAGE COMPRESSION… • Temporal Redundancy Reduction – Take advantage of similarity between successive frames 950 951 952

42. 42 VIDEO/IMAGE COMPRESSION… • Temporal Redundancy Reduction – Take advantage of similarity between successive frames

43. 43 VIDEO/IMAGE COMPRESSION… • Temporal Redundancy Reduction – Take advantage of similarity between successive frames

44. 44 VIDEO/IMAGE COMPRESSION… When may temporal redundancy reduction be ineffective?

45. 45 VIDEO/IMAGE COMPRESSION… • Many scene changes vs. few scene changes • Sometimes high motion

46. 46 VIDEO/IMAGE COMPRESSION… • Many scene changes vs. few scene changes • Sometimes high motion

47. 47 IMAGE CODING

48. 48 IMAGE CODING • Lossless Compression • Lossy Compression • Transform Coding

49. 49 IMAGE CODING… • Image 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

50. 50 IMAGE CODING… • Image 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

51. 51 IMAGE CODING… • Generally, the only operation that is lossy is the quantization stage • The fact that all the loss (distortion) is localized to a single operation greatly simplifies system design • Can design loss to exploit human visual system (HVS) properties • Source decoder performs the inverse of each of the three operations

52. 52 IMAGE CODING… • Representations - Transform and Subband Filtering Methods – Goal Transform signal into another domain where most of the information (energy) is concentrated into only a small fraction of the coefficients – Enables perceptual processing Exploiting HVS response to different frequency components

53. 53 IMAGE CODING… • Representations - Transform and Subband Filtering Methods – Examples of “traditional” transforms KLT, DFT, DCT – Examples of “traditional” Subband filtering methods Perfect reconstruction filter banks, wavelets – Transform and Subband interpretations All of the above are linear representations and can be interpreted from either a transform or a Subband filtering viewpoint – Transform viewpoint Express signal as a linear combination of basis vectors Stresses linear expansion (linear algebra) perspective – Subband filtering viewpoint Pass signal through a set of filters and examine the frequencies passed by each filter (Subband) Stresses filtering (signal processing) perspective

54. 54 IMAGE CODING… • Representations – Transform Image Coding – A good transform provides Most of the image energy is concentrated into a small fraction of the coefficients Coding only these small fraction of the coefficients and discarding the rest can often lead to excellent reconstructed quality The more energy compaction the better – Orthogonal transforms are particularly useful Energy in discarded coefficients is equal to energy in reconstruction error

55. 55 IMAGE CODING… • Representations – Transform Image Coding – Karhunen-Loeve Transform (KLT) Optimal energy compaction Requires knowledge of signal covariance In general, no simple computational algorithm – Discrete Fourier Transform (DFT) Fast algorithms Good energy compaction, but not as good as DCT – Discrete Cosine Transform (DCT) Fast algorithms Good energy compaction All real coefficients Overall good performance and widely used for image and video coding

56. 56 IMAGE CODING… • Discrete Cosine Transform (DCT) – 1-D Discrete Cosine Transform (N-point) – 1-D DCT basis vectors – 2-D DCT: Separable transform of 1-D DCT – 2-D DCT basis vectors? Basis pictures! – 2-D basis vectors for 2-D DCT are basis pictures! – 64 basis pictures for 8x8-pixel 2-D DCT – Image coding with the 2-D DCT is equivalent to approximating the image as a linear combination of these basis pictures!

57. 57 IMAGE CODING… • Representations – Coding Transform Coefficients – Selecting the basis pictures to approximate an image is equivalent to selecting the DCT coefficients to code – General methods of coding/discarding coefficients Zonal Coding ▫ Code all coefficients in a zone and discard others ▫ Example zone: Spatial low frequencies ▫ Only need to code coefficient amplitudes Threshold Coding ▫ Keep coefficients with magnitude above a threshold ▫ Coefficient amplitudes and locations must be coded ▫ Provides best performance

58. 58 IMAGE CODING… • Video / Image Coding are Block based Coding – Frames are divided into Sub-Block and then coded • Macroblock (MB) and Block Layer – Process the data in blocks of 8x8 samples – Convert Red-Green-Blue into Luminance (greyscale) and Chrominance (Blue color difference and Red color difference) – Use half resolution for Chrominance (because eye is more sensitive to greyscale than to color)

59. 59 IMAGE CODING… • Macroblock (MB) and Block Layer – Macroblock Consist of 16x16 luminance block 8x8 chrominance block Basic unit for motion estimation – Block 8 pixels by 8 lines Basic unit for DCT

60. 60 IMAGE CODING… • Lossless Compression – General-Purpose Compression: Entropy Encoding – Remove statistical redundancy from data – ie, Encode common values with short codes, uncommon values with longer codes • Lossless Compression – Huffman Coding – Example : ABCCDEAAB After compression: 1011000000001010111011 – Compression ratio According to probability of the characters appears in the uncompressed data C:12 D:13 F:5 E:9 B:16 A:45 1425 55 100 30 10 0 0 0 0 1 1 1 1 000 001 0100 0101 011 1

61. 61 IMAGE CODING… • Lossless Compression – Run-Length Coding Reduce the number of samples to code Implementation is simple Input Sequence 0,0,-3,5,1,0,-2,0,0,0,0,2,-4,3,-2,0,0,0,1,0,0,-2,EOB Run-Length Sequence (2,-3)(0,5)(0,1)(1,-2)(4,2)(0,-4)(0,3)(0,-2)(3,1)(2,-2)EOB

62. 62 IMAGE CODING… • Lossless Compression – Transform Coding (-1,1) (1,1) (0.4,1.4) = 0.4•(1,0)+1.4•(0,1) = 0.9•(1,1)+0.5•(-1,1) Basis vector { (1,0), (0,1) } New basis vector { (1,1), (-1,1) } New vector (0.9, 0.5) (0,1) (1,0)

63. 63 IMAGE CODING… • Lossless Compression – Transform Coding : DCT Transform blocks of images to frequency domain, code only the significant transform coefficients 2D DCT – Transform Coding : DCT 8x8 DCT Basis Function

64. 64 IMAGE CODING… • Lossless Compression – Transform Coding : DCT 2D DCT Coefficients

65. 65 IMAGE CODING… • Lossy Compression – Lossy Predictive Coding

66. 66 IMAGE CODING… • Lossy Compression – Quantization Many to one mapping Quantization is the most import means of irrelevancy reduction – Implementation Lookup Table Divide by quantization step-size (round/truncate)

67. 67 IMAGE CODING… • Lossy Compression – Divide by quantization step-size Input signal:0 1 2 3 4 5 6 7(3 bits) Step-size:2 Quantization:0 0 1 1 2 2 3 3(2 bits) Inverse quantization:0 0 2 2 4 4 6 6 Quantization Errors:0 1 0 1 0 1 0 1 – Lookup Table Divide each DCT coefficient by an integer, discard remainder Result: loss of precision Typically, a few non-zero coefficients are left

68. 68 IMAGE CODING… • Lossy Compression – Zigzag Scan Efficient encoding of the position of non-zero transform coefficients Scan” quantized coefficients in a zig-zag order Non-zero coefficients tend to be grouped together

69. 69 IMAGE CODING… • DCT + Quantization + Run-Level-Coding

70. 70 VIDEO CODING

71. 71 VIDEO CODING • Lossless Compression • Lossy Compression • Transform Coding • Motion Coding

72. 72 VIDEO CODING… • Video – Sequence of frames (images) that are related • Moving images contain significant temporal redundancy – Successive frames are very similar – Related along the temporal dimension - Temporal redundancy exists

73. 73 VIDEO CODING… • Video Coding – The objective of video coding is to compress moving images – Main addition over image compression Temporal redundancy Video coder must exploit the temporal redundancy – The MPEG (Moving Picture Experts Group) and H.26X are the major standards for video coding • Video coding algorithms usually contains two coding schemes : – Intraframe coding Intraframe coding does not exploit the correlation among adjacent frames Intraframe coding therefore is similar to the still image coding – Interframe coding The interframe coding should include motion estimation/compensation process to remove temporal redundancy • Basic Concept – Use interframe correlation for attaining better rate distortion

74. 74 VIDEO CODING… • 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

75. 75 VIDEO CODING… • Main addition over image compression – Exploit the temporal redundancy • Predict current frame based on previously coded frames • 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

76. 76 VIDEO CODING… • Motion-Compensated Prediction – Simple frame differencing fails when 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? • 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 Describe motion of each block No object identification required Good, robust performance

77. 77 VIDEO CODING… • Block-Matching Motion Estimation – Assumptions Translational motion within block All pixels within each block have the same motion – ME Algorithm Divide current frame into non-overlapping N1xN2 blocks For each block, find the best matching block in reference frame – MC-Prediction Algorithm Use best matching blocks of reference frame as prediction of blocks in current frame

78. 78 VIDEO CODING… • Block-Matching - Determining the Best Matching Block – For each block in the current frame search for best matching block in the reference frame Metrics for determining “best match” Candidate blocks: All blocks in, e.g., (± 32,±32) pixel area Strategies for searching candidate 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” • Motion Vectors and Motion Vector Field – Motion Vector Expresses the relative horizontal and vertical offsets (mv1,mv2), or motion, of a given block from one frame to another Each block has its own motion vector – Motion Vector Field Collection of motion vectors for all the blocks in a frame

79. 79 VIDEO CODING… • Example of Fast Search: 3-Step (Log) Search – Goal: Reduce number of search points Example:(± 7,±7) search area Dots represent search points Search performed in 3 steps (coarse-to-fine) – Step 1: (± 4 pixels ) – Step 2: (± 2 pixels ) – Step 3: (± 1 pixels ) – Best match is found at each step – Next step: Search is centered around the best match of prior step – Speedup increases for larger search areas

80. 80 VIDEO CODING… • Motion Vector Precision – Motivation Motion is not limited to integer-pixel offsets However, video only known at discrete pixel locations To estimate sub-pixel motion, frames must be spatially interpolated – Fractional MVs are used to represent the sub-pixel motion – Improved performance (extra complexity is worthwhile) – Half-pixel ME used in most standards: MPEG-1/2/4 – Why are half-pixel motion vectors better? Can capture half-pixel motion Averaging effect (from spatial interpolation) reduces prediction error -> Improved prediction For noisy sequences, averaging effect reduces noise -> Improved compression

81. 81 VIDEO CODING… • Practical Half-Pixel Motion Estimation Algorithm – Half-Pixel ME (coarse-fine) Algorithm Coarse Step: Perform integer motion estimation on blocks; find best integer- pixel MV Fine Step: Refine estimate to find best half-pixel MV Spatially interpolate the selected region in reference frame Compare current block to interpolated reference frame block Choose the integer or half-pixel offset that provides best match Typically, bilinear interpolation is used for spatial interpolation • Example – MC-Prediction for Two Consecutive Frames

82. 82 VIDEO CODING… • Bi-Directional MC-Prediction – Bi-Directional MC-Prediction is used to estimate a block in the current frame from a block in Previous frame Future frame Average of a block from the previous frame and a block from the future frame – Motion compensated prediction Predict the current frame based on reference frame(s) while compensating for the motion – Examples of block-based motion-compensated prediction (P-frame) and bi-directional prediction (B-frame)

83. 83 VIDEO CODING… • Motion Estimation and Compensation – The amount of data to be coded can be reduced significantly if the previous frame is subtracted from the current frame

84. 84 VIDEO CODING… • Motion Estimation and Compensation – Uses Block-Matching The MPEG and H.26X standards use block-matching technique for motion estimation /compensation In the block-matching technique, each current frame is divide into equal-size blocks, called source blocks Each source block is associated with a search region in the reference frame The objective of block-matching is to find a candidate block in the search region best matched to the source block The relative distances between a source block and its candidate blocks are called motion vectors Video Sequence The current frameThe reconstructed reference frame Bx: Search area associated with X MV: Motion Vector X: Source block for block-matching

85. 85 VIDEO CODING… • Motion Estimation and Compensation – Uses Block-Matching

86. 86 VIDEO CODING… • Motion Estimation and Compensation The Reconstructed Previous Frame The Current Frame Results of Block- Matching The Predicted Current Frame

87. 87 VIDEO CODING… • Motion Estimation and Compensation – Search Range – The size of the search range = – The number of candidate blocks = )2)(2( max2max1 yx dNdN   )12)(12( maxmax   yx dd

88. 88 VIDEO CODING… • Motion Estimation and Compensation – Motion Vector and Search Area    pnpn 22 Search Area: Motion vector: (u, v)

89. 89 VIDEO CODING… • Motion Estimation and Compensation – Matching Function Mean square error(MSE) Mean absolute difference(MAD) Number of threshold difference(NTD) Normalized cross-correlation function(NCF)       1 0 21 0 221121 21 21 1 1 1 1 )]1,,(),,([ 1 ),( N n N n tdndnftnnf NN ddMSE       1 0 1 0 221121 21 21 1 1 1 1 |)1,,(),,(| 1 ),( N n N n tdndnftnnf NN ddMAD

90. 90 VIDEO CODING… • Motion Estimation and Compensation – Algorithm Full search block matching (FSB) Fast algorithm ▫ 2D Logarithmic Search (TDL) ▫ Three Step Search (TSS) ▫ Cross-Search Algorithm (CSA) ▫ … – Full Search Algorithm If p=7, then there are (2p+1)(2p+1)=225 candidate blocks. u v Search Area Candidate Block

91. 91 VIDEO CODING… • Motion Estimation and Compensation – Full Search Algorithm Intensive computation Need for fast Motion Estimation !

92. 92 VIDEO CODING… • Motion Estimation and Compensation – 2D Logarithmic Search Diamond-shape search area Matching function ▫ MSE -7 –6 –5 –4 –3 –2 –1 0 +1 +2 +3 +4 +5 +6 +7 +7 +6 +5 +4 +3 +2 +1 0 -1 -2 -3 -4 -5 -6 -7 MV

93. 93 VIDEO CODING… • Motion Estimation and Compensation – Three-Step Search The first step involves block-matching based on 4-pel resolution at the nine location The second step involves block-matching based on 2-pel resolution around the location determined by the first step The third step repeats the process in the second step (but with resolution 1-pel) -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 11 1 11 11 1 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 3 3 333 3 3 3 2 2 2 2 222 2

94. 94 VIDEO CODING… • Motion Estimation and Compensation – Motion Vector Prediction predMVx = Median(MV1x, MV2x, MV3x) predMVy = Median(MV1y, MV2y, MV3y) MVx`=MVx - predMVx MVy`=MVy - predMVy

95. 95 VIDEO CODER ARCHITECTURE

96. 96 VIDEO CODER ARCHITECTURE • Image / Video Coding Based on Block-Matching – Assume frame f-1 has been encoded and reconstructed, and frame f is the current frame to be encoded • 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

97. 97 VIDEO CODER ARCHITECTURE… • Video Encoder – Divide frame f into equal-size blocks – For each source block, Find its motion vector using the block-matching algorithm based on the reconstructed frame f-1 Compute the DFD of the block – Transmit the motion vector of each block to decoder – Compress DFD’s of each block – Transmit the encoded DFD’s to decoder

98. 98 VIDEO CODER ARCHITECTURE… • Video Encoder

99. 99 VIDEO CODER ARCHITECTURE… • Video Decoder – Receive motion vector of each block from encoder – Based on the motion vector ,find the best-matching block from the reference frame ie,, Find the predicted current frame from the reference frame – Receive the encoded DFD of each block from encoder – Decode the DFD. – Each reconstructed block in the current frame = Its decompressed DFD + the best-matching block

100. 100 VIDEO CODER ARCHITECTURE… • Video Decoder

101. 101 VIDEO CODEC STANDARDS

102. 102 VIDEO CODEC STANDARDS • Goal of Standards – Ensuring Interoperability Enabling communication between devices made by different manufacturers – Promoting a technology or industry – Reducing costs What do the Standards Specify?

103. 103 VIDEO CODEC STANDARDS… 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

104. 104 VIDEO CODEC STANDARDS… • The Scope of Picture and Video Coding Standardization – Only the Syntax and Decoder are standardized: Permits optimization beyond the obvious Permits complexity reduction for implementability Provides no guarantees of Quality Pre-Processing Encoding Source Destination Post-Processing & Error Recovery Decoding Scope of Standard

105. 105 VIDEO CODEC STANDARDS…

106. 106 VIDEO CODEC 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 tools added 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

107. 107 VIDEO CODEC STANDARDS… • The Video Standards uses all the three types of frames as shown below Encoding order: I0, P3, B1, B2, P6, B4, B5, I9, B7, B8. Playback order: I0, B1, B2, P3, B4, B5, P6, B7, B8, I9.

108. 108 VIDEO CODEC STANDARDS… • Video Structure – Video standards code video sequences in hierarchy of layers – There are usually 5 Layers GOP (Group of Pictures) Picture Slice Macroblock Block

109. 109 VIDEO CODEC STANDARDS… • Video Structure – A GOP usually started with I frame, followed by a sequence of P and B frames – A Picture is indeed a frame in the video sequence – A Slice is a portion in a picture Some standards do not have slices Some view a slice as a row Each slice in H.264 is not necessary to be a row It can be any shape containing integral number of macroblocks – A Macroblock is a 16×16 block Many standards use Marcoblocks as the basic unit for block-matching operations – A Block is a 8×8 block Many standards use the Blocks as the basic unit for DCT

110. 110 VIDEO CODEC STANDARDS… • Scalable Video Coding – Three classes of scalable video coding techniques Temporal Scalability Spatial Scalability SNR Scalability – Uses B frames for attaining temporal scalability B frames depend on other frames No other frames depend on B frames Discard B frames without affecting other frames

111. 111 VIDEO CODEC STANDARDS… • Scalable Video Coding – Spatial Scalability – Basically Resolution Scalability Here the base layer is the low resolution version of the video sequence – The base layer uses coaster quantizer for DFD coding – The residuals in the base layer is refined in the enhancement layer

112. 112 VIDEO CODEC STANDARDS…

113. 113 HEVC

114. 114 HEVC • Video Coding Standards Overview Next Generation Broadcasting

115. 115 HEVC… • MPEG-H – High Efficiency Coding and Media Delivery in Heterogeneous Environments a new suite of standards providing technical solutions for emerging challenges in multimedia industries – Part 1: System, MPEG Media Transport (MMT) Integrated services with multiple components in a hybrid delivery environment, providing support for seamless and efficient use of heterogeneous network environments, including broadcast, multicast, storage media and mobile networks – Part 2: Video, High Efficiency Video Coding (HEVC) Highly immersive visual experiences, with ultra high definition displays that give no perceptible pixel structure even if viewed from such a short distance that they subtend a large viewing angle (up to 55 degrees horizontally for 4Kx2K resolution displays, up to 100 degrees for 8Kx4K) – Part 3: Audio, 3D-Audio Highly immersive audio experiences in which the decoding device renders a 3D audio scene. This may be using 10.2 or 22.2 channel configurations or much more limited speaker configurations or headphones, such as found in a personal tablet or smartphone.

116. 116 HEVC… • Transport/System Layer Integration – On going definitions (MPEG, IETF,…,DVB): benefit from H.264/AVC – MPEG Media Transport (MMT) ?

117. 117 HEVC… • HEVC = High Efficiency Video Coding • Joint project between ISO/IEC/MPEG and ITU-T/VCEG – ISO/IEC: MPEG-H Part 2 (23008-2) – ITU-T: H.265 • JCT-VC committee – Joint Collaborative Team on Video Coding – Co-chairs: Dr. Gary Sullivan (Microsoft, USA) and Dr. Jens-Reiner Ohm (RWTH Aachen, Germany) • Target – Roughly half the bit-rate at the same subjective quality compared to H.264/AVC (50% over H.264/AVC) – x10 complexity max for encoder and x2/3 max for decoder • Requirements – Progressive required for all profiles and levels Interlaced support using field SEI message – Video resolution: sub QVGA to 8Kx4K, with more focus on higher resolution video content (1080p and up) – Color space and chroma sampling: YUV420, YUV422, YUV444, RGB444 – Bit-depth: 8-14 bits – Parallel Processing Architecture

118. 118 HEVC… • H.264 Vs H.265

119. 119 HEVC… • Potential applications – Existing applications and usage scenarios IPTV over DSL : Large shift in IPTV eligibility Facilitated deployment of OTT and multi-screen services More customers on the same infrastructure: most IP traffic is video More archiving facilities – Existing applications and usage scenarios 1080p60/50 with bitrates comparable to 1080i Immersive viewing experience: Ultra-HD (4K, 8K) Premium services (sports, live music, live events,…): home theater, Bars venue, mobile HD 3DTV Full frame per view at today’s HD delivery rates What becomes possible with 50% video rate reduction?

120. 120 HEVC… • Tentative Timeline

121. 121 HEVC… • History

122. 122 HEVC… • H.264 Vs H.265

123. 123 HEVC… • H.264 Vs H.265

124. 124 HEVC… • HEVC Encoder

125. 125 HEVC… • HEVC Decoder

126. 126 HEVC… • Video Coding Techniques : Block-based hybrid video coding – Interpicture prediction Temporal statistical dependences – Intrapicture prediction Spatial statistical dependences – Transform coding Spatial statistical dependences • Uses YCbCr color space with 4:2:0 subsampling – Y component Luminance (luma) Represents brightness (gray level) – Cb and Cr components Chrominance (chroma). Color difference from gray toward blue and red

127. 127 HEVC… • Video Coding Techniques : Block-based hybrid video coding – Motion compensation Quarter-sample precision is used for the MVs 7-tap or 8-tap filters are used for interpolation of fractional-sample positions – Intrapicture prediction 33 directional modes, planar (surface fitting), DC (flat) Modes are encoded by deriving most probable modes (MPMs) based on those of previously decoded neighboring PBs – Quantization control Uniform reconstruction quantization (URQ) – Entropy coding Context adaptive binary arithmetic coding (CABAC) – In-Loop deblocking filtering Similar to the one in H.264 and More friendly to parallel processing – Sample adaptive offset (SAO) Nonlinear amplitude mapping For better reconstruction of amplitude by histogram analysis

128. 128 HEVC… • Coding Tree Unit (CTU) - A picture is partitioned into CTUs – The CTU is the basic processing unit instead of Macro Blocks (MB) – Contains luma CTBs and chroma CTBs A luma CTB covers L × L samples Two chroma CTBs cover each L/2 × L/2 samples – HEVC supports variable-size CTBs The value of L may be equal to 16, 32, or 64. Selected according to needs of encoders - In terms of memory and computational requirements Large CTB is beneficial when encoding high-resolution video content – CTBs can be used as CBs or can be partitioned into multiple CBs using quadtree structures – The quadtree splitting process can be iterated until the size for a luma CB reaches a minimum allowed luma CB size (8 × 8 or larger).

129. 129 HEVC… • Block Structure – Coding Tree Units (CTU) Corresponds to macroblocks in earlier coding standards (H.264, MPEG2, etc) Luma and chroma Coding Tree Blocks (CTB) Quadtree structure to split into Coding Units (CUs) 16x16, 32x32, or 64x64, signaled in SPS

130. 130 HEVC… • A new framework composed of three new concepts – Coding Units (CU) – Prediction Units (PU) – Transform Units (TU) • The decision whether to code a picture area using inter or intra prediction is made at the CU level Goal: To be as flexible as possible and to adapt the compression-prediction to image peculiarities

131. 131 HEVC… • Block Structure – Coding Units (CU) Luma and chroma Coding Blocks (CB) Rooted in CTU Intra or inter coding mode Split into Prediction Units (PUs) and Transform Units (TUs)

132. 132 HEVC… • Block Structure – Prediction Units (PU) Luma and chroma Prediction Blocks (PB) Rooted in CU Partition and motion info

133. 133 HEVC… • Block Structure – Transform Units (TU) Rooted in CU 4x4, 8x8, 16x16, 32x32 DCT, and 4x4 DST

134. 134 HEVC… • Relationship of CU, PU and TU

135. 135 HEVC… • Intra Prediction – 35 intra modes: 33 directional modes + DC + planar – For chroma, 5 intra modes: DC, planar, vertical, horizontal, and luma derived – Planar prediction (Intra_Planar) Amplitude surface with a horizontal and vertical slope derived from boundaries – DC prediction (Intra_DC) Flat surface with a value matching the mean value of the boundary samples – Directional prediction (Intra_Angular) 33 different directional prediction is defined for square TB sizes from 4×4 up to 32×32

136. 136 HEVC… • Intra Prediction – Adaptive reference sample filtering 3-tap filter: [1 2 1]/4 Not performed for 4x4 blocks For larger than 4x4 blocks, adaptively performed for a subset of modes Modes except vertical/near-vertical, horizontal/near-horizontal, and DC – Mode dependent adaptive scanning 4x4 and 8x8 intra blocks only All other blocks use only diagonal upright scan (left-most scan pattern)

137. 137 HEVC… • Intra Prediction – Boundary smoothing Applied to DC, vertical, and horizontal modes, luma only Reduces boundary discontinuity – For DC mode, 1st column and row of samples in predicted block are filtered – For Hor/Ver mode, first column/row of pixels in predicted block are filtered

138. 138 HEVC… • Inter Prediction – Fractional sample interpolation ¼ pixel precision for luma – DCT based interpolation filters 8-/7- tap for luma 4-tap for chroma Supports 16-bit implementation with non-normative shift – High precision interpolation and biprediction – DCT-IF design Forward DCT, followed by inverse DCT

139. 139 HEVC… • Inter Prediction – Asymmetric Motion Partition (AMP) for Inter PU – Merge Derive motion (MV and ref pic) from spatial and temporal neighbors Which spatial/temporal neighbor is identified by merge_idx Number of merge candidates (≤ 5) signaled in slice header Skip mode = merge mode + no residual – Advanced Motion Vector Prediction (AMVP) Use spatial/temporal PUs to predict current MV

140. 140 HEVC… • Transforms – Core transforms: DCT based 4x4, 8x8, 16x16, and 32x32 Square transforms only Support partial factorization Near-orthogonal Nested transforms – Alternative 4x4 DST 4x4 intra blocks, luma only – Transform skipping mode By-pass the transform stage Most effective on “screen content” 4x4 TBs only

141. 141 HEVC… • Scaling and Quantization – HEVC uses a uniform reconstruction quantization (URQ) scheme controlled by a quantization parameter (QP). – The range of the QP values is defined from 0 to 51

142. 142 HEVC… • Entropy Coding – One entropy coder, CABAC Reuse H.264 CABAC core algorithm More friendly to software and hardware implementations Easier to parallelize, reduced HW area, increased throughput – Context modeling Reduced # of contexts Increased use of by-pass bins Reduced data dependency – Coefficient coding Adaptive coefficient scanning for intra 4x4 and 8x8 ▫ Diagonal upright, horizontal, vertical Processed in 4x4 blocks for all TU sizes Sign data hiding: ▫ Sign of first non-zero coefficient conditionally hidden in the parity of the sum of the non-zero coefficient magnitudes ▫ Conditions: 2 or more non-zero coefficients, and “distance” between first and last coefficient > 3

143. 143 HEVC… • Entropy Coding - CABAC – Binarization: CABAC uses Binary Arithmetic Coding which means that only binary decisions (1 or 0) are encoded. A non-binary-valued symbol (e.g. a transform coefficient or motion vector) is "binarized" or converted into a binary code prior to arithmetic coding. This process is similar to the process of converting a data symbol into a variable length code but the binary code is further encoded (by the arithmetic coder) prior to transmission. – Stages are repeated for each bit (or "bin") of the binarized symbol. – Context model selection: A "context model" is a probability model for one or more bins of the binarized symbol. This model may be chosen from a selection of available models depending on the statistics of recently coded data symbols. The context model stores the probability of each bin being "1" or "0". – Arithmetic encoding: An arithmetic coder encodes each bin according to the selected probability model. Note that there are just two sub-ranges for each bin (corresponding to "0" and "1"). – Probability update: The selected context model is updated based on the actual coded value (e.g. if the bin value was "1", the frequency count of "1"s is increased)

144. 144 HEVC… • Parallel Processing Tools – Slices – Tiles – Wavefront parallel processing (WPP) – Dependent Slices • Slices – Slices are a sequence of CTUs that are processed in the order of a raster scan. Slices are self-contained and independent – Each slice is encapsulated in a separate packet

145. 145 HEVC… • Tile – Self-contained and independently decodable rectangular regions – Tiles provide parallelism at a coarse level of granularity Tiles more than the cores  Not efficient  Breaks dependencies

146. 146 HEVC… • WPP – A slice is divided into rows of CTUs. Parallel processing of rows – The decoding of each row can be begun as soon a few decisions have been made in the preceding row for the adaptation of the entropy coder. – Better compression than tiles. Parallel processing at a fine level of granularity. No WPP with tiles !!

147. 147 HEVC… • Dependent Slices – Separate NAL units but dependent (Can only be decoded after part of the previous slice) – Dependent slices are mainly useful for ultra low delay applications Remote Surgery – Error resiliency gets worst – Low delay – Good Efficiency  Goes well with WPP

148. 148 HEVC… • Slice Vs Tile – Tiles are kind of zero overhead slices Slice header is sent at every slice but tile information once for a sequence Slices have packet headers too Each tile can contain a number of slices and vice versa – Slices are for : Controlling packet sizes Error resiliency – Tiles are for: Controlling parallelism (multiple core architecture) Defining ROI regions

149. 149 HEVC… • Tile Vs WPP – WPP Better compression than tiles Parallel processing at a fine level of granularity But … Needs frequent communication between processing units If high number of cores Can’t get full utilization – Good for when Relatively small number of nodes Good inter core communication No need to match to MTU size Big enough shared cache

150. 150 HEVC… • In-Loop Filters – Two processing steps, a deblocking filter (DBF) followed by an sample adaptive offset (SAO) filter, are applied to the reconstructed samples The DBF is intended to reduce the blocking artifacts due to block- based coding The DBF is only applied to the samples located at block boundaries The SAO filter is applied adaptively to all samples satisfying certain conditions. e.g. based on gradient.

151. 151 HEVC… • Loop Filters: Deblocking – Applied to all samples adjacent to a PU or TU boundary Except the case when the boundary is also a picture boundary, or when deblocking is disabled across slice or tile boundaries – HEVC only applies the deblocking filter to the edge that are aligned on an 8×8 sample grid This restriction reduces the worst-case computational complexity without noticeable degradation of the visual quality It also improves parallel-processing operation – The processing order of the deblocking filter is defined as horizontal filtering for vertical edges for the entire picture first, followed by vertical filtering for horizontal edges.

152. 152 HEVC… • Loop Filters: Deblocking – Simpler deblocking filter in HEVC (vs H.264 ) – Deblocking filter boundary strength is set according to Block coding mode Existence of non zero coefficients Motion vector difference Reference picture difference

153. 153 HEVC… • Loop Filters: SAO – A process that modifies the decoded samples by conditionally adding an offset value to each sample after the application of the deblocking filter, based on values in look-up tables transmitted by the encoder. – SAO: Sample Adaptive Offsets New loop filter in HEVC Non-linear filter – For each CTB, signal SAO type and parameters – Encoder decides SAO type and estimates SAO parameters (rate- distortion opt.)

154. 154 HEVC… • Special Coding – I_PCM mode The prediction, transform, quantization and entropy coding are bypassed The samples are directly represented by a pre-defined number of bits Main purpose is to avoid excessive consumption of bits when the signal characteristics are extremely unusual and cannot be properly handled by hybrid coding – Lossless mode The transform, quantization, and other processing that affects the decoded picture are bypassed The residual signal from inter- or intrapicture prediction is directly fed into the entropy coder It allows mathematically lossless reconstruction SAO and deblocking filtering are not applied to this regions – Transform skipping mode Only the transform is bypassed Improves compression for certain types of video content such as computer- generated images or graphics mixed with camera-view content Can be applied to TBs of 4×4 size only

155. 155 HEVC… • High Level Parallelism – Independently decodable packets – Sequence of CTUs in raster scan – Error resilience – Parallelization – Independently decodable (re-entry) – Rectangular region of CTUs – Parallelization (esp. encoder) – 1 slice = more tiles, or 1 tile = more slices – Rows of CTUs – Decoding of each row can be parallelized – Shaded CTU can start when gray CTUs in row above are finished – Main profile does not allow tiles + WPP combination

156. 156 HEVC… • Profiles, Levels and Tiers – Historically, profile defines collection of coding tools, whereas Level constrains decoder processing load and memory requirements – The first version of HEVC defined 3 profiles Main Profile: 8-bit video in YUV4:2:0 format Main 10 Profile: same as Main, up to 10-bit Main Still Picture Profile: same as Main, one picture only – Levels and Tiers Levels: max sample rate, max picture size, max bit rate, DPB and CPB size, etc Tiers: “main tier” and “high tier” within one level

157. 157 HEVC… • Complexity Analysis – Software-based HEVC decoder capabilities (published by NTT Docomo) Single-threaded: 1080p@30 on ARMv7 (1.3GHz),1080p@60 decoding on i5 (2.53GHz) Multi-threaded: 4Kx2K@60 on i7 (2.7GHz), 12Mbps, decoding speed up to 100fps – Other independent software-based HEVC real-time decoder implementations published by Samsung and Qualcomm during HEVC development – Decoder complexity not substantially higher More complex modules: MC, Transform, Intra Pred, SAO Simpler modules: CABAC and deblocking

158. 158 HEVC… • Quality Performance

159. 159 THANK YOU

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