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Digital Video Compression and Television Evolution

This lecture introduces the concepts of analog and digital television, the need for compression and compression algorithms classification, basic coding concepts, and compression ratios. It also explores the history and concepts of PAL and NTSC video standards, HDTV resolutions and frame rates, aspect ratios and refresh rates, and digital video and TV formats.

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Digital Video Compression and Television Evolution

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  1. CS 414 – Multimedia Systems DesignLecture 6 – Digital Video and Introduction to Compression Klara Nahrstedt Spring 2014 CS 414 - Spring 2014

  2. Administrative • MP1 is posted • See Class website and compass • MP1 lecture will be on February 7 (Friday) in class. Please, read the MP1 before attending the class CS 414 - Spring 2014

  3. Today Introduced Concepts • Analog and Digital Television • Need for compression and compression algorithms classification • Basic Coding Concepts • Fixed-length coding and variable-length coding • Compression Ratio • Entropy CS 414 - Spring 2014

  4. Television History (Analog) • 1927, Hoover made a speech in Washington while viewers in NY could see, hear him • AT&T Bell Labs had the first “television” • 18 fps, 2 x 3 inch screen, 2500 pixels

  5. Analog Television Concepts • Production (capture) • 2D • structured formats • Representation and Transmission • popular formats include NTSC, PAL, SECAM • Re-construction • scanning • display issues (refresh rates, temporal resolution) • relies on principles of human visual system CS 414 - Spring 2014

  6. PAL video standard Y is luminance UV are chrominance YUV from RGB Y = .299R + .587G + .114BU = 0.492 (B - Y)V = 0.877 (R - Y) Color Space: YUV Y U-V plane at Y=0.5 U V CS 414 - Spring 2014 Source: wikipedia

  7. YIQ (NTSC) • YIQ from RGB Y = .299R + .587G + .114B I = .74 (R - Y) - .27 (B - Y)Q = 0.48 (R - Y) + 0.41 (B - Y) YIQ with Y=0.5 CS 414 - Spring 2014 Source: wikipedia

  8. Video Representations CS 414 - Spring 2014

  9. TV History CS 414 - Spring 2014

  10. HDTV (Digital) • Resolutions: • 1920x1080 (1080p) – Standard HD (HDTV) • 2160p, … • 4096x2304 (4096p) – 4K High HD • Frame rate: • HDTV - 50 or 60 frames per second • HDTV – 120 fps CS 414 - Spring 2014

  11. HDTV • Interlaced (i) and/or progressive (p) formats • Conventional TVs – use interlaced formats • Computer displays (LCDs) – use progressive scanning • MPEG-2 compressed streams • In Europe (Germany) – MPEG-4 compressed streams CS 414 - Spring 2014

  12. Aspect Ratio and Refresh Rate • Aspect ratio • Conventional TV is 4:3 (1.33) • HDTV is 16:9 (2.11) • Cinema uses 1.85:1 or 2.35:1 • Frame Rate • NTSC is 60Hz interlaced (actually 59.94Hz) • PAL/SECAM is 50Hz interlaced • Cinema is 24Hz non-interlaced CS 414 - Spring 2014 Source: wikipedia

  13. Digital Video and TV • Bit rate: amount of information stored per unit time (second) of a recording • Color Coding: YCrCb • Subset of YUV that scales and shifts the chrominance values into range 0..1 Y = 0.299R + 0.587G + .114BCr = ((B-Y)/2) + 0.5Cb = ((R-Y)/1.6) + 0.5 Y Cr Cb CS 414 - Spring 2014

  14. Digital Video and TV • Color space compression • YUV444 • 24 bits per pixel • YUV422 • 16 bits/pixel • YUV411 • 12 bits/pixel CS 414 - Spring 2014

  15. Digital Video and TV • DVD video • Since 1997 • Resolution and frame rate • 704x480 at 29.97 fps • 704x576 at 25 fps • Bitrate: 9.8 Mbps CS 414 - Spring 2014

  16. Digital Video and TV • Blu-ray video • since 2006 • Resolution and frame rate • 1920i (@59.94 fps) – interlaced • 1920p (@24 fps) – progressive • …. • Bitrate : 40 Mbps CS 414 - Spring 2014

  17. 3DTV Refresh rate no less than 120 Hz Synchronized shutter glasses to enable different views for different eyes CS 414 - Spring 2014

  18. Today Introduced Concepts • Analog and Digital Television • Need for compression and compression algorithms classification • Basic Coding Concepts • Fixed-length coding and variable-length coding • Compression Ratio • Entropy CS 414 - Spring 2014

  19. Reading • Media Coding and Content Processing, Steinmetz, Nahrstedt, Prentice Hall, 2002 • Data Compression – chapter 7 • Basic coding concepts – Sections 7.1-7.4 and lecture notes CS 414 - Spring 2014

  20. Integrating Aspects of Multimedia Audio/Video Presentation Playback Image/Video Capture Audio/Video Perception/ Playback Image/Video Information Representation Transmission Transmission Compression Processing Audio Capture Media Server Storage Audio Information Representation A/V Playback CS 414 - Spring 2014

  21. Uncompressed audio 8 KHz, 8 bit 8K per second 30M per hour 44.1 KHz, 16 bit 88.2K per second 317.5M per hour 100 Gbyte disk holds 315 hours of CD quality music Uncompressed video 640 x 480 resolution, 8 bit color, 24 fps 7.37 Mbytes per second 26.5 Gbytes per hour 640 x 480 resolution, 24 bit (3 bytes) color, 30 fps 27.6 Mbytes per second 99.5 Gbytes per hour 100 Gbyte disk holds 1 hour of high quality video Need for Compression CS 414 - Spring 2014

  22. Broad Classification • Entropy Coding (statistical) • lossless; independent of data characteristics • e.g. RLE, Huffman, LZW, Arithmetic coding • Source Coding • lossy; may consider semantics of the data • depends on characteristics of the data • e.g. DCT, DPCM, ADPCM, color model transform • Hybrid Coding (used by most multimedia systems) • combine entropy with source encoding • e.g., JPEG-2000, H.264, MPEG-2, MPEG-4, MPEG-7 CS 414 - Spring 2014

  23. Data Compression • Branch of information theory • minimize amount of information to be transmitted • Transform a sequence of characters into a new string of bits • same information content • length as short as possible CS 414 - Spring 2014

  24. Concepts • Coding (the code) maps source messages from alphabet (A) into code words (B) • Source message (symbol) is basic unit into which a string is partitioned • can be a single letter or a string of letters • EXAMPLE: aa bbb cccc ddddd eeeeee fffffffgggggggg • A = {a, b, c, d, e, f, g, space} • B = {0, 1} CS 414 - Spring 2014

  25. Today Introduced Concepts • Analog and Digital Television • Need for compression and compression algorithms classification • Basic Coding Concepts • Fixed-length coding and variable-length coding • Compression Ratio • Entropy CS 414 - Spring 2014

  26. Taxonomy of Codes • Block-block • source msgs and code words of fixed length; e.g., ASCII • Block-variable • source message fixed, code words variable; e.g., Huffman coding • Variable-block • source variable, code word fixed; e.g., RLE • Variable-variable • source variable, code words variable; e.g., Arithmetic CS 414 - Spring 2014

  27. Example of Block-Block • Coding “aa bbb cccc ddddd eeeeee fffffffgggggggg” • Requires 120 bits

  28. Example of Variable-Variable • Coding “aa bbb cccc ddddd eeeeee fffffffgggggggg” • Requires 30 bits • don’t forget the spaces

  29. Concepts (cont.) • A code is • distinct if each code word can be distinguished from every other (mapping is one-to-one) • uniquely decodableif every code word is identifiable when immersed in a sequence of code words • e.g., with previous table, message 11 could be defined as either ddddd or bbbbbb CS 414 - Spring 2014

  30. Static Codes • Mapping is fixed before transmission • message represented by same codeword every time it appears in message (ensemble) • Huffman coding is an example • Better for independent sequences • probabilities of symbol occurrences must be known in advance; CS 414 - Spring 2014

  31. Dynamic Codes • Mapping changes over time • also referred to as adaptive coding • Attempts to exploit locality of reference • periodic, frequent occurrences of messages • dynamic Huffman is an example • Hybrids? • build set of codes, select based on input CS 414 - Spring 2014

  32. Traditional Evaluation Criteria • Algorithm complexity • running time • Amount of compression • redundancy • compression ratio • How to measure? CS 414 - Spring 2014

  33. Measure of Information • Consider symbolssi and the probability of occurrence of each symbol p(si) • In case of fixed-length coding , smallest number of bits per symbol needed is • L ≥ log2(N) bits per symbol • Example: Message with 5 symbols need 3 bits (L ≥ log25) CS 414 - Spring 2014

  34. Variable-Length Coding- Entropy • What is the minimum number of bits per symbol? • Answer: Shannon’s result – theoretical minimum average number of bits per code word is known as Entropy (H) CS 414 - Spring 2014

  35. Entropy Example • Alphabet = {A, B} • p(A) = 0.4; p(B) = 0.6 • Compute Entropy (H) • -0.4*log2 0.4 + -0.6*log2 0.6 = .97 bits CS 414 - Spring 2014

  36. Summary • Symmetric compression • requires same time for encoding and decoding • used for live mode applications (teleconference) • Asymmetric compression • performed once when enough time is available • decompression performed frequently, must be fast • used for retrieval mode applications (e.g., an interactive CD-ROM) CS 414 - Spring 2014

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