Spandana image processing and compression techniques (7840228)

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Spandana image processing and compression techniques (7840228)

  1. 1. IMAGE PROCESSING AND COMPRESSION TECHNIQUES By T. Spandana 094D1A0426 E.C.E SSIET,Vadiyampeta. 1
  2. 2. Objective The objective of image processing is to sharpen, minimize theeffect of degradation, reduce the amount of memory to store the imageinformation (image compression). 2
  3. 3. IntroductionImage processing pertains to the alteration and analysisof pictorial information. Common case of image processing is the adjustmentof brightness and contrast controls on a television set bydoing this we enhance the image until its subjectiveappearing to us is most appealing. 3
  4. 4. TerminologyWhat is the Digital Image Processing? Digital: Operating by the use of discrete signals to represent data in the form of numbers. Image: An image (from Latin imago) or picture is an artefact, usually two-dimensional. Processing: To perform operations on data according to programmed instructions. 4
  5. 5. Definition Thus the definition of the digital image processing may be given as: “Digital image processing is the use of computer algorithms to perform image processing on digital images ” 5
  6. 6. Digital image: An image may be defined as a two- dimensional function, f(x, y). A digital image is composed of a finite number of elements. These elements are referred to as picture elements, image elements, pels, and pixels. 6
  7. 7. Digital image processing sequence 7
  8. 8. Key Stages in Digital Image Processing Image Morphological Restoration Processing Image Segmentation Enhancement Image Object Acquisition Recognition RepresentationProblem Domain & Description Colour Image Image Processing Compression 8
  9. 9. Key Stages in Digital Image Processing:Image acquisition Image Morphological Restoration Processing Image Segmentation Enhancement Image Object Acquisition Recognition RepresentationProblem Domain & Description Colour Image Image Processing Compression 9
  10. 10. Key Stages in Digital Image Processing:Image Enhancement Image Morphological Restoration Processing Image Segmentation Enhancement Image Object Acquisition Recognition RepresentationProblem Domain & Description Colour Image Image Processing Compression 10
  11. 11. Key Stages in Digital Image Processing:Image Restoration Image Morphological Restoration Processing Image Segmentation Enhancement Image Object Acquisition Recognition RepresentationProblem Domain & Description Colour Image Image Processing Compression 11
  12. 12. Key Stages in Digital Image Processing:Morphological Processing Image Morphological Restoration Processing Image Segmentation Enhancement Image Object Acquisition Recognition RepresentationProblem Domain & Description Colour Image Image Processing Compression 12
  13. 13. Key Stages in Digital Image Processing:Segmentation Image Morphological Restoration Processing Image Segmentation Enhancement Image Object Acquisition Recognition RepresentationProblem Domain & Description Colour Image Image Processing Compression 13
  14. 14. Key Stages in Digital Image Processing:Object Recognition Image Morphological Restoration Processing Image Segmentation Enhancement Image Object Acquisition Recognition RepresentationProblem Domain & Description Colour Image Image Processing Compression 14
  15. 15. Key Stages in Digital Image Processing:Representation & Description Image Morphological Restoration Processing Image Segmentation Enhancement Image Object Acquisition Recognition RepresentationProblem Domain & Description Colour Image Image Processing Compression 15
  16. 16. Key Stages in Digital Image Processing:Image Compression Image Morphological Restoration Processing Image Segmentation Enhancement Image Object Acquisition Recognition RepresentationProblem Domain & Description Colour Image Image Processing Compression 16
  17. 17. Key Stages in Digital Image Processing:Colour Image Processing Image Morphological Restoration Processing Image Segmentation Enhancement Image Object Acquisition Recognition RepresentationProblem Domain & Description Colour Image Image Processing Compression 17
  18. 18. Image Compression Image compression addresses the problem of reducing the amount of data required to represent a digital image. It is the sub areas of image processing. The underlying basis of the reduction process is the removal of the redundant data. 18
  19. 19. Goal of Image Compression Digital images require huge amounts of space for storage and large bandwidths for transmission. The goal of image compression is to reduce the amount of data required to represent a digital image. 19
  20. 20. The Flow of Image Compression To store the image into bit-stream as compact as possible and to display the decoded image in the monitor as exact as possible Original Image Decoded Image Bitstream Encoder 0101100111... Decoder Figure: Flow of compression 20
  21. 21. Different Compression Techniques Mainly two types of data Compression techniques are there.  Loss less Compression.  Lossy Compression. 21
  22. 22. Figure : Data compression methods 22
  23. 23. 23
  24. 24. Lossless Compression 24
  25. 25. 25
  26. 26. Run-length:•Simplest method of compression.• It can be used to compress data made of any combination of symbols.•It does not need to know the frequency of occurrence of symbols andcan be very efficient if data is represented as 0s and 1s.•The general idea behind this method is to replace consecutiverepeating occurrences of a symbol by one occurrence of the symbolfollowed by the number of occurrences. 26
  27. 27. For instance, Figure : Run-length encoding example 27
  28. 28. 28
  29. 29. Huffman codingHuffman coding assigns shorter codes to symbols that occur morefrequently and longer codes to those that occur less frequently. 29
  30. 30. Figure Huffman coding 30
  31. 31. A character’s code is found by starting at the root and following thebranches that lead to that character. The code itself is the bit value of eachbranch on the path, taken in sequence. Figure Final tree and code 31
  32. 32. EncodingLet us see how to encode text using the code for our fivecharacters. Figure shows the original and the encoded text. Figure Huffman encoding 32
  33. 33. DecodingThe recipient has a very easy job in decoding the data it receives.Figure shows how decoding takes place. Figure : Huffman decoding 33
  34. 34. 34
  35. 35. Lempel Ziv encodingLempel Ziv (LZ) encoding is an example of a category of algorithmscalled dictionary-based encoding.The idea is to create a dictionary (a table) of strings used during thecommunication session. 35
  36. 36. The LZW Algorithm (Compression)Flow Chart START W= NULL YES IS EOF STOP ? N O K=NEXT INPUT YES IS WK W=WK FOUND? N O OUTPUT INDEX OF W ADD WK TO DICTIONARY W=K 36
  37. 37. The LZW Algorithm (Compression)Example Input string is a b d c a d a c The Initial Dictionary contains symbols like a, b, c, d with their index values as 1, 2, 3, 4 respectively. a 1 Now the input string b 2 is read from left to c 3 right. Starting from a. d 4 37
  38. 38. The LZW Algorithm (Compression)Example W = Null a b d c a d a c K=a WK = a KIn the dictionary. a 1 b 2 c 3 d 4 38
  39. 39. The LZW Algorithm (Compression)Example K = b. a b d c a d a c WK = abis not in the dictionary. K Add WK to dictionary 1 Output code for a. a 1 ab 5 Set W = b b 2 c 3 d 4 39
  40. 40. The LZW Algorithm (Compression)Example K=d a b d c a d a c WK = bdNot in the dictionary. KAdd bd to dictionary. Output code b 1 2 Set W = d a 1 ab 5 b 2 bd 6 c 3 d 4 40
  41. 41. The LZW Algorithm (Compression)Example K=a a b d a b d a c WK = danot in the dictionary. K Add it to dictionary. Output code d 1 2 4 Set W = a a 1 ab 5 b 2 bd 6 c 3 da 7 d 4 41
  42. 42. The LZW Algorithm (Compression)Example K=b a b d a b d a c WK = abIt is in the dictionary. K 1 2 4 a 1 ab 5 b 2 bd 6 c 3 da 7 d 4 42
  43. 43. The LZW Algorithm (Compression)Example K=d a b d a b d a c WK = abdNot in the dictionary. K Add W to the dictionary. 1 2 4 5 Output code for W. a 1 ab 5 Set W = d b 2 bd 6 c 3 da 7 d 4 abd 8 43
  44. 44. The LZW Algorithm (Compression)Example• K=a a b d a b d a c• WK = daIn the dictionary. K 1 2 4 5 a 1 ab 5 b 2 bd 6 c 3 da 7 d 4 abd 8 44
  45. 45. The LZW Algorithm (Compression) Example• K=c a b d a b d a c• WK = dacNot in the dictionary. K• Add WK to the dictionary. 1 2 4 5 7• Output code for W. a 1 ab 5 dac 9• Set W = c b 2 bd 6• No input left so output code for W. c 3 da 7 d 4 abd 8 45
  46. 46. The LZW Algorithm (Compression)Example• The final output a b d a b d a c string is 124573 K• Stop. 1 2 4 5 7 3 a 1 ab 5 dac 9 b 2 bd 6 c 3 da 7 d 4 abd 8 46
  47. 47. LZW Decompression Algorithm Flow Chart START K=INPUT Output K W=K YES IS EOF STOP ? NO K=NEXT INPUT ENTRY=DICTIONARY INDEX (K) Output ENTRY ADD W+ENTRY[0] TO DICTIONARY W=ENTRY 47
  48. 48. The LZW Algorithm (Decompression) Example 1 2 4 5 7 3• K=1• Out put K (i.e. a) K• W=K a a 1 b 2 c 3 d 4 48
  49. 49. The LZW Algorithm (Decompression) Example 1 2 4 5 7 3• K=2• entry = b K• Output entry• Add W + entry[0] to a b dictionary• W = entry[0] (i.e. b) a 1 ab 5 b 2 c 3 d 4 49
  50. 50. The LZW Algorithm (Decompression) Example 1 2 4 5 7 3• K=4• entry = d K• Output entry• Add W + entry[0] to a b d dictionary• W = entry[0] (i.e. d) a 1 ab 5 b 2 bd 6 c 3 d 4 50
  51. 51. The LZW Algorithm (Decompression) Example 1 2 4 5 7 3• K=5• entry = ab K• Output entry• Add W + entry[0] to a b d a b dictionary• W = entry[0] (i.e. a) a 1 ab 5 b 2 bd 6 c 3 da 7 d 4 51
  52. 52. The LZW Algorithm (Decompression) Example 1 2 4 5 7 3• K=7• entry = da K• Output entry• Add W + entry[0] to a b d a b d a dictionary• W = entry[0] (i.e. d) a 1 ab 5 b 2 bd 6 c 3 da 7 d 4 abd 8 52
  53. 53. The LZW Algorithm (Decompression) Example 1 2 4 5 7 3• K=3• entry = c K• Output entry• Add W + entry[0] to a b d a b d a c dictionary• W = entry[0] (i.e. c) a 1 ab 5 dac 9 b 2 bd 6 c 3 da 7 d 4 abd 8 53
  54. 54. 54
  55. 55. LOSSY COMPRESSIONMETHODS Information loss is tolerable. Many-to-1 mapping in compression eg. Quantization 55
  56. 56. LOSSY COMPRESSIONMETHODSSeveral methods have been developed using lossy compressiontechniques.JPEG (Joint Photographic Experts Group) encoding is used tocompress pictures and graphics. MPEG (Moving Picture Experts Group) encoding is used to compressvideo.MP3 (MPEG audio layer 3) for audio compression. 56
  57. 57. 57
  58. 58. JPEG Compression image, ~150KB JPEG compressed, ~14KB 58
  59. 59. Image compression – JPEG encodingJPEG encoding is done in four steps:1. Image preparation2. Discrete Cosine Transform (DCT)3. Quantization4. Entropy Encoding 59
  60. 60. Figure : JPEG grayscale example, 640 × 480 pixels 60
  61. 61. Block diagram for JPEG encoder. The JPEG compression process 61
  62. 62. Discrete cosine transform (DCT)In this step, each block of 64 pixels goes through a transformationcalled the discrete cosine transform (DCT).The transformation changes the 64 values so that the relativerelationships between pixels are kept but the redundancies arerevealed.P(x, y) defines one value in the block, while T(m, n) defines thevalue in the transformed block. 62
  63. 63. 63
  64. 64. QuantizationAfter the T table is created, the values are quantized to reduce thenumber of bits needed for encoding. Quantization divides the number of bits by a constant and then dropsthe fraction. This reduces the required number of bits even more.. 64
  65. 65. Zig zag recording Reading the table 65
  66. 66. Block diagram for JPEG Decoder. 66
  67. 67. Examples of varying JPEG compression ratios500KB image, minimum 40KB image, half 11KB image, maxcompression compression compression 67
  68. 68. 68
  69. 69. Video compression – MPEG encodingThe Moving Picture Experts Group (MPEG) method is used tocompress video.Principle, a motion picture is a rapid sequence of a set of frames inwhich each frame is a picture.Compressing video, then, means spatially compressing each frameand temporally compressing a set of frames. 69
  70. 70. Spatial compressionThe spatial compression of each frame is done with JPEG, or amodification of it. Each frame is a picture that can be independentlycompressed.Temporal compressionIn temporal compression, redundant frames are removed. When wewatch television, for example, we receive 30 frames per second.However, most of the consecutive frames are almost the same. 70
  71. 71. Figure MPEG frames 71
  72. 72. 72
  73. 73. Audio compressionAudio compression can be used for speech or music. For speechwe need to compress a 64 kHz digitized signal, while for music weneed to compress a 1.411 MHz signal.Two categories of techniques are used for audio compression:predictive encoding and perceptual encoding. 73
  74. 74. Predictive encodingIn predictive encoding, the differences between samples areencoded instead of encoding all the sampled values. This type of compression is normally used for speech. Severalstandards have been defined such as GSM (13 kbps), G.729 (8 kbps),and G.723.3 (6.4 or 5.3 kbps). 74
  75. 75. Perceptual encoding: MP3The most common compression technique used to create CD-quality audio is based on the perceptual encoding technique.This type of audio needs at least 1.411 Mbps, which cannot be sentover the Internet without compression. MP3 (MPEG audio layer 3)uses this technique. 75
  76. 76. Advantages: In medicine Vision Systems are flexible, inexpensive, powerful tools that can be used with ease. In Space Exploration the robots play vital role which in turn use the image processing techniques Astronomical Observations. Used in Remote Sensing, Geological Surveys for detecting mineral resources etc. Also used for character recognizing techniques, inspection for abnormalities in industries. 76
  77. 77. Disadvantages: A Person needs knowledge in many fields to develop an application / or part of an application using image processing. Calculations and computations are difficult and complicated so needs an expert in the field related. Hence it’s unsuitable and unbeneficial to ordinary programmers with mediocre knowledge 77
  78. 78. ApplicationsOne of the most common uses of DIP techniques: improve quality,remove noise etc 78
  79. 79. The Hubble TelescopeLaunched in 1990 the Hubbletelescope can take images ofvery distant objectsHowever, an incorrect mirrormade many of Hubble’simages uselessImage processingtechniques wereused to fix this 79
  80. 80. MedicineTake slice from MRI scan of canine heart, and find boundariesbetween types of tissues Original MRI Image of a Dog Heart Edge Detection Image 80
  81. 81. GIS Geographic Information Systems  Digital image processing techniques are used extensively to manipulate satellite imagery  Terrain classification  Meteorology 81
  82. 82. PCB Inspection Printed Circuit Board (PCB) inspection  Machine inspection is used to determine that all components are present and that all solder joints are acceptable  Both conventional imaging and x-ray imaging are used 82
  83. 83. HCITry to make human computer interfaces more natural  Face recognition  Gesture recognition 83
  84. 84. Inserting Artificial Objects into a Scene 84
  85. 85. Human Activity Recognition 85
  86. 86. CONCLUSION: Image processing plays a vital role in many applications suchas Fingerprint Identification System Medicine Geographic Information Systems Printed Circuit Board (PCB) inspection human computer interfaces Inserting Artificial Objects into a Scene Human Activity Recognition soon……. 86
  87. 87. Future scope The digital Image Processing is now finding wide range of uses in different modern applications. Few of them (in which researcher are trying developments)include: Expert Systems Parallel Processing Neural Networks 87
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