1. COMPRESSION AND DECOMPRESSION 10/22/20151 A.Aruna, Assistant Professor, Faculty of Information Technology

2. Introduction  Video and audio have much higher storage requirements than text  Data transmission rates (in terms of bandwidth requirements) for sending continuous media are considerably higher than text  Efficient compression of audio and video data, including some compression standards

3. COMPRESSION  Compression is a reduction in the number of bits needed to represent data.  save storage capacity  speed file transfer  decrease costs for storage hardware and network bandwidth. 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 3

4. 10/22/20154 Multimedia Compression  Audio, image and video require vast amounts of data  320x240x8bits grayscale image: 77Kb  1100x900x24bits color image: 3MB  640x480x24x30frames/sec: 27.6 MB/sec  Low network’s bandwidth doesn't allow for real time video transmission  Slow storage or processing devices don't allow for fast playing back  Compression reduces storage requirements A.Aruna, Assistant Professor, Faculty of Information Technology

5. 10/22/20155 Classification of Techniques  Lossless: recover the original representation. Mechanisms:  Packbits encoding(Run Length Encoding)  CCITT Group 3 1D  CCITT Group 3 2D  CCITT Group 4  Lempel – Ziv and Welch Algorithm LZW A.Aruna, Assistant Professor, Faculty of Information Technology

6. Classification of Techniques  Lossy: recover a representation similar to the original one  graphics, audio, video and images Mechanisms:  Joint Photographic Experts Group  Moving Picture Experts Group  Intel DVI  CCITT H.26l Video Coding Algorithm  Fractals 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 6

7. BINARY IMAGE COMPRESSION  Used for Documents (Black & White)  Continuous Tone Information  Office & Business Document  Handwritten Text  Line Graphics  Engineering Drawing  Scanning Documents 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 7

8. BINARY IMAGE COMPRESSION  Scanning Process  Scanline – Top to Bottom, Left to right  Composed of Various Objects  CCD Array Sensor – B/W Dots- Memory  Eg: Faxing – 1 Page – 20 seconds 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 8

9. Packbits Encoding or Runlength Encoding  Simplest and earliest Data Compression Schemes  Binary Image  Consecutive Repeated – Two Bytes  First Byte – No.of times Character is Repeated  Second Byte – Character itself 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 9

10. Packbits Encoding or Runlength Encoding 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 10

11. CCITT Group 3 1-D Compression  Based on Runlength Encoding  Facsimile & Early document Imaging System  Large Size even after Compression  Modified Runlength encoding is Huffman Encoding 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 11

12. CCITT Group 3 1-D Compression  Huffman Encoding  Variable Length Encoding  Shorter Code – Frequently  Longer code – Less Frequently  Probability of Occurrence of white and black Pixel  It is based on a coding Tree, which is constructed based on the probability of occurrences of white pixels and black pixels in the run length or bit streams 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 12

13. CCITT Group 3 1-D Compression  probability of occurrences of bit stream of length Rn = P(Rn) 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 13

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16. Large Pixel Sequences 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 16

17. Example  16 White Pixel = 101010 - Frequently  16 Black Pixel = 0000010111  Quicker decoding  Tree Structure to be constructed 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 17

18.  Makeup code : length in Multiples of 64 pixels  Terminating code: length less than 64 pixels  132 white pixels is 100101011  Make up code for 128 = 10010  Terminating code for 4 = 1011 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 18

19. Coding Tree  16 white 101010 and black pixel 0000010111 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 19 1 0 1 0 1 0

20. CCITT Group 3 2D Compression  2-dimensional coding  Images are divided into several groups of K lines  the first line of each group is encoded using CCITT Group 3 1D method  The rest of lines are encoded using some "differencial schemes"  Typically compression ratio 10 ~ 20 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 20

21. CCITT Group 3 2D Compression  The "K-factor" allows more error- free transmission  World-wide fassimile standard  The 2D scheme uses a combination of additional codes called vertical code, pass code, and horizontal code 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 21

22. CCITT Group 3 2D Compression 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 22

23. CCITT Group 3 2D Compression  Only one pass code, i.e. 0001 and one horizontal code, i.e. 001  If vertical code and horizontal code are not applied, then the horizontal code is appied  Horizontal Code + Group 3 1D Code = 001 + markup code + terminating code 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 23

24. COLOR, GRAY SCALE& STILL VIDEO IMAGE COMPRESSION 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 24 http://cs.stanford.edu/people/eroberts/courses/so co/projects/data- compression/lossy/jpeg/coeff.htm

25. INTRODUCTION  Adds a another Dimension to image.  Indicate the states  Red - ?  Green?  Adds Depth to the image – Background & Dense in Nature  Presenting Information 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 25

26. In Physics?  Visible Light – Electromagnetic Spectrum Radiation or Radiant Energy  Frequency Ranges ??????????  Radiant Energy is measured in terms Wavelength & Frequency  Relationship??????  Velocity of light c = 3 x 10 8 Meters 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 26

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28. COLOR  Primary Color  Complementary Color  Approaches  Additive  Subtrative 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 28

29. COLOR CHARACTERISTICS  Luminance or Brightness – Emitted or reflected from object  Hue – Color Appearances  Saturation – Color Intensity 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 29

30. COLOR MODELS  Chromacity Model  RGB Model  HSI Model  CMY Model  YUV or YUI Model B/W TV or COLOR IMAGE COMPOSITION 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 30

31. JOINT PHOTOGRAPHIC EXPERTS GROUP COMPRESSION  JPEG – Joint ISO & CCITT Working Committee - exclusively for Still Image  Joint Committee – MPEG – Full Motion Standards  Works with colour and greyscale images  Up to 24 bit colour images  Suitable for many applications e.g., satellite, medical, general photography... 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 31

32. 10/22/201532 JPEG Modes of Operation  Sequential DCT: the image is encoded in one left-to-right, top-to-bottom scan  Progressive DCT: the image is encoded in multiple scans (if the transmission time is long, a rough decoded image can be reproduced)  Hierarchical: encoding at multiple resolutions  Lossless : exact reproduction A.Aruna, Assistant Professor, Faculty of Information Technology

33. JPEG Standards Level  Baseline – Maintain High Compression Ratio  Special Lossless Function – No Loss of Data  Extended System – Various Encoding  Variable Length Encoding  Progressive Length Encoding  Hierarchical Encoding 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 33

34. OVERVIEW OF JPEG STANDARDS Components  Baseline Sequential Codec – DCT Coefficients, Quantization And Entropy Encoding  DCT Progressive Mode – Multiple Scans – Until Reached Picture Quality (Based on Quantization)  Predictive Lossless Encoding  Hierarchical Mode – Different Resolution 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 34

35. JPEG IMPLEMENTATION  Discrete Cosine Transformation  Reduce the level in Gray Scale and Color Image ( 2D – Amplitude & Frequency)  Reduce Series of Data  Remove The redundant data ( time to frequency Domain) 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 35

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37. DCT 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 37

38. INPUT 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 38 140144147140 155179175 144152140147140148167179 152155136167163162152172 168145156160152155136160 162148156148140136147162 147167140155 140136162 136156123167162144140147 148155136155152147 136

39. OUTPUT 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 39 186-1815-923-9-1419 21-3426-9-1111147 -10-24-26-183-20 -8-514-15-8-3 8 1081-1118 15 4-2-1888-41-7 91-34-7-2 0-8-2214-60

40. Quantization  Precision of Integer – Reduce No. of bits is used to store the values 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 40

41. 10/22/201541 Quantization Step  Reduces the amplitude of coefficients which contribute little or nothing to 0  Discards information which is not visually significant  Quantization coefficients Q(u,v) are specified by quantization tables  A set of 4 tables are specified by JPEG A.Aruna, Assistant Professor, Faculty of Information Technology

42. 10/22/201542 Quantization Tables  for (i=0; i < 64; i++) for (j=0; j < 64; j++) Q[i,j] = 1 + [ (1+i+j) quality];  quality = 1: best quality, lowest compression  quality = 25: poor quality, highest compression A.Aruna, Assistant Professor, Faculty of Information Technology

43. 10/22/201543 Entropy Encoding  Encodes sequences of quantized DCT coefficients into binary sequences  AC: (runlength, size) (amplitude)  DC: (size, amplitude)  runlength: number consecutive 0’s, up to 15  takes up to 4 bits for coding  (39,4)(12) = (15,0)(15,0)(7,4)(12)  amplitude: first non-zero value  size: number of bits to encode amplitude  0 0 0 0 0 0 476: (6,9)(476) A.Aruna, Assistant Professor, Faculty of Information Technology

44. 10/22/201544 Huffman coding  Converts each sequence into binary  First DC following with ACs  Huffman tables are specified in JPEG  Each (runlength, size) is encoded using Huffman coding  Each (amplitude) is encoded using a variable length integer code  (1,4)(12) => (11111101101100) A.Aruna, Assistant Professor, Faculty of Information Technology

45. 10/22/201545 Example of Huffman table A.Aruna, Assistant Professor, Faculty of Information Technology

46. VIDEO COMPRESSION 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 46

47. INTRODUCTION  Distribute the information to larger places  Application  Video teleconferencing  Digital Telephony 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 47

48. STANDARDS  P*64 (CCITT) – Video Conferencing  JPEG (ISO)- Still Image  MPEG (ISO) – Stored Video 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 48

49. Requirements for Full Motion Video Compression  Random Access – Indexing  VCR Paradigm – play,fast,forward,rewind,stop,search forward, etc.,  Audio & Video Synchronization  Multiplexing multiple compressed Audio and Video Bit Streams  Editability  Playback Device Flexibility 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 49

50. CCITT H.261 Video Coding Algorithm(px64)  Developed in 1990’s  Videophone and Video Conferencing  CIF (Common Interchange File Formats) & QCIF(Quarter CIF)  Hierarchical Block Structure – Encoding Data  DCT & DPCM 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 50

51. AUDIO COMPRESSION  ADAPTIVE DIFFERENCIAL PULSE CODE MODULATION 10/22/2015A.Aruna, Assistant Professor, Faculty of Information Technology 51