The Core Technology of Digital TV
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1 the Japan-Vietnam International Student Seminar on Engineering Science in Hanoi The Core Technology of Digital TV Kosuke SATO Osaka University November 18-24, 2007
2 What is compression of digital data and analog transmission? An introduction to image data Analog video compression Psycho-visual model Predictive encoding Transform encoding Motion compensation JPEG, MPEG
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10 R G B
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13 Conversion RGB color space to YCbCr color space Y = 0.30*R *G *B Cb = B-Y = -0.30*R *G *B Cr = R-Y = 0.70*R *G *B Brightness(Y) is necessary for B/W television. Chroma-blue, Chroma-red are for color television. R Analog calculation G B Brightness(Y)
14 Q axis I axis Color Map IQ Coordinates
15 Video Signal Sound Signal High-cut of IQ Signal Freq. Imposing of YIQ
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17 Photoreceptor Cods cell Optic nerve Corn cell Retina
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27 Video input (Raster scan) Reversible Predictive Error Predictive pixel value Prediction Circuit Memory Pixel value Analog Value Transmission Storage Video output (Raster scan) Memory Pixel value Prediction Circuit
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30 Previous frame Difference Present frame Motion vector and Newly appearing region
31 Previous frame Difference Motion vector and Newly appearing region Present frame
32 Search Blocking (8 8, 16 16) Search the minimum point on error Sum of absolute error Sum of square error Previous frame Present frame
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34 Left pixel i(x, y-1) Interest pixel i(x, y)
35 Left pixel i(x, y-1) i(x, y) - i(x, y-1) Interest pixel i(x, y) i(x, y) + i(x, y-1)
36 Left pixel i(x, y-1) Prediction error i(x, y-1)-i(x, y) Interest pixel i(x, y) Interest pixel i(x, y)
37 Predictive Coding Transform Coding Blocking (regarding spatial continuity) Processing Unit Pixel Block (Conversion from n-d to n-d)
38 k k k k Σ Basis function Original image k k k k k k k k k k k k k k k k Array of coefficient
39 Y Cb Cr RGB distribution YCbCr distribution
40 1D Basis Haar transform Hadamard transform DCT
41 Ave. Sq. Error 2D Hadamard transform (Black=1 White=-1) Fourie Haar Hadamard K-L Slant Block size
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43 Theoretically DCT is the second optimal transformation in terms of S/N, while KL transform is the optimum one. Multi-dimensional DCT can be calculated by incremental 1D DCT. No complex operation Fast algorithm at N log N, while DCT is 0(N^2) Easy implementation to hardware
44 Input intensity data, which distributes arbitrary, bigger numbers converge around low frequency parts and smaller number converge around high frequency parts after DCT transformation. Coefficient of higher frequency parts tend to become zero by quantization. Coefficient (0,0) is called DC component, other coefficients are called AC components.
45 KL transform is the theoretically optimum one for stationary stochastic process. Two-pass algorithm is necessary for calculation; 1- pass = variance analysis, 2-pass = adaptive transform) Difficult to arrange transform basis preliminarily because of 2-pass processing. Difficult to implementing to hardware
46 Intensity Coefficient Code Blocking De-Blocking 2D DCT 2D I-DCT Quantization De-Quantization Quantized Coefficient Intensity Coefficient
47 Prediction error Image Predictive Coding Transform Coding Code Coefficient Image Transform Coding Predictive Coding Code
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49 ransmit video quality still image Transmit high quality natural picture
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54 Memory Groupin g 1D Huffman Coding Cod e Table
55 Table Zigza g Scan Run Length Count 2D Huffman Coding Cod e Groupin g
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57 DCT
58 Video Sequence GOP Picture Slice Macro Block Block
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66 Object Coding Automatic Image Understanding is difficult Z camera CG Animation For-ground object Object Extarction Bach-ground object Coding Decoding Coding Decoding Coding Decoding Shape/Position α plane Composition
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