1. Compression video overview 演講者：林崇元

2. Outline Introduction Fundamentals of video compression Picture type Signal quality measure Video encoder and decoder Standard ’ s

3. Introduction Why we need to compression Picture A picture consists of three rectangular matrices representing luminance (Y) and two chrominance (Cb and Cr) values The Y matrix has an even number of rows and columns The Cb and Cr matrices are one-half the size of the Y matrix in each direction (horizontal and vertical).

4. Introduction Applications for image, video, and audio compression

5. Introduction Achieve high compression performance while keep good picture quality Theorem Spatial redundancy – DCT,DFT,subband,wavelet Temporal redundancy – MC/ME Statistical redundancy – VLC, Entropy coding Perceptual redundancy – VQ

6. Introduction Tradeoffs in lossy compression

7. Fundamentals of video compression Use the technique of the JPEG DCT based coding scheme DCT transform (2D)

8. Fundamentals of video compression Use the technique of the JPEG Discrete cosine transform

9. Fundamentals of video compression Use the technique of the JPEG DCT based coding system Image Spatial-to-DCT domain transformation 8 x 8 DCT Lossless coding of DCT domain samples Entropy Coding Discard unimportant DCT domain samples Quantization

10. Fundamentals of video compression Quantization Eyes are insensible to high-frequency components The greater quantizer means greater loss Lower frequency component has smaller quantizer, high frequency component has greater quantizer The quantiation tables in the encoder and decoder are the same

11. Fundamentals of video compression Use the technique of the JPEG The spatial domain is redundancy For the DCT-based coding system on an image- by-image, one can achieve close to 14Mbits per second, which is too high for practical uses For lower bit rate, we must introduce temporal redundancy

12. Fundamentals of video compression Temporal redundancy The temporal correlation in an image sequence

13. Fundamentals of video compression Temporal redundancy Instead of 3-D DCT, most video coders use a two- stage process to achieve good compression Two-stage video coding process

14. Fundamentals of video compression Temporal redundancy Motion estimation

15. Fundamentals of video compression Temporal redundancy Full search algorithm

16. Picture type Video bit stream

17. Picture type Slice One or more "contiguous'' macroblocks. The order of the macroblocks within a slice is from left-to-right and top-to- bottom. Macroblock A 16-pixel by 16-line section of luminance components and the corresponding 8-pixel by 8-line section of the two chrominance components. Block A block is an 8-pixel by 8-line set of values of a luminance or a chrominance component.

18. Picture type Intra picture Coded using only information present in the picture itself I-pictures provide potential random access points into the compressed video data. I-pictures use only transform coding

19. Picture type Predicted picture coded with respect to the nearest previous I- or P- picture. P-pictures use motion compensation Unlike I-pictures, P-pictures can propagate coding errors

20. Picture type Bidirectional picture Coded use both a past and future picture as a reference B-pictures provide the most compression and do not propagate errors

21. Picture type The choice of picture type The MPEG algorithm allows the encoder to choose the frequency and location of I-pictures is based on the application's need for random accessibility and the location of scene cuts in the video sequence The encoder also chooses the number of B- pictures between any pair of reference (I- or P-) pictures. This choice is based on factors such as the amount of memory in the encoder and the characteristics of the material being coded

22. Picture type Typical display order of picture types Video stream composition The MPEG encoder reorders pictures in the video stream to present the pictures to the decoder in the most efficient sequence

23. Signal quality measure SNR (signal-to-noise ratio) encoder input signal energy SNR = 10log 10 noise signal energy PSNR (peak signal-to-noise ratio) Instead of using the encoder input signal, one uses a hypothetical signal with a signal strength of 255

24. Video encoder

25. Video decoder

26. MPEG-1 Media storage Optimal for frame size 352x240x30 Bitrate : up to 1.5 Mbit/s International standard in 1992

27. MPEG-2 Applications from storage to HDTV Bitrate : standard definition TV:4-9 Mbit/s HDTV:15-25 Mbit/s Interlaced/non-interlaced Scalability Capable of decoding MPEG-1 bitstream International standard in 1994 Single chip for video and audio

28. MPEG-4 Applications for multimedia communication Bitrate : 10K-25 Mbit/s Object – based coding Natural and synthetic video Scalability Robust and error resilience International standard in 1998 Single chip for video and audio