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Image Sequence Coding by Split and Merge

Image Sequence Coding by Split and Merge. Patrice Willemin, Todd R. Reed and Murat Kunt. Presented by: Idan Shatz. Outline. Split and Merge Coding For Still Images Split and Merge Coding For Image Sequence Conclusions. Image Coding. First Generation Pels coding (prediction or transform)

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Image Sequence Coding by Split and Merge

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  1. Image Sequence Codingby Split and Merge Patrice Willemin, Todd R. Reed and Murat Kunt Presented by: Idan Shatz

  2. Outline • Split and Merge Coding • For Still Images • Split and Merge Coding • For Image Sequence • Conclusions

  3. Image Coding • First Generation • Pels coding (prediction or transform) • Second Generation • Object Based coding • Region based coding • Split and Merge Method

  4. Split & Merge • Split… • Recursive algorithm • Approximate a region by a 2D polynomial • If (approximation error > threshold) • Split the region into 4 sub-regions • Approximate each sub-region

  5. Split Example Original Image Split, depth=1

  6. Split Example Original Image Split, depth=2

  7. Split Example Original Image Split, depth=3

  8. Split Example Original Image Split, depth=4

  9. Split Example Original Image Split, depth=5

  10. Split Example Original Image Split, depth=6

  11. Split Example Original Image Split, depth=7

  12. Split Example Original Image Split, Final results Threshold=100,000

  13. The threshold 100,000 50,000 20,000 10,000 5,000 2,000 1,000 500 200 100

  14. 5,000 100,000 The threshold

  15. Split & Merge • An improvement of the Split algorithm • At each step decide between x-split and y-split • produces fewer regions

  16. Split Example (2) Original Image Split, depth=1

  17. Split Example (2) Original Image Split, depth=2

  18. Split Example (2) Original Image Split, depth=3

  19. Split Example (2) Original Image Split, depth=4

  20. Split Example (2) Original Image Split, depth=5

  21. Split Example (2) Original Image Split, depth=6

  22. Split Example (2) Original Image Split, depth=7

  23. Split Example (2) Original Image Split, depth=8

  24. Split Example (2) Original Image Split, depth=9

  25. Split Example (2) Original Image Split, depth=10

  26. Split Example (2) Original Image Split, depth=11

  27. Split Example (2) Original Image Split, depth=12

  28. Split Example (2) Original Image Split, depth=13

  29. Split Example (2) Original Image Split, Final results Threshold = 100,000

  30. Split Example (Comparison) Split – 4 sub regions Threshold = 100,000 # regions = 613 Split – 2 sub regions Threshold = 100,000 # regions = 387

  31. Comparison

  32. Image Coding • Each region is codes by: • The Border • 2D polynomial coefficients

  33. The borders of the regions • BSP-Tree • Binary space partition tree • Tree Nodes: • “X” – mark an X-split • “Y” – mark an Y-split • “0” – mark an Region

  34. Regions Borders • Example of BSP-Tree Y X 0 0 Y X 0 0 0

  35. Storing a BSP Tree • N regions  2N-1 BSP-Nodes • Storing the tree in preorder Y X 0 Y X 0 Y X 0 0 0 0 0 Y X 0 0 0

  36. Image Coding • For N regions • Border (BSP) – N*3[bit] • 2D polynomial coefficients – N*24[bit] • 8[bit] x 3 – gray level. • 8[bit] x 3 x 3 - for RGB.

  37. Image Coding • Without Compression • 196,608[Byte] • With Compression • # regions = 387 • 3628[Byte] • Compression Raito: • 1 to 54 Split – 2 sub regions

  38. Split & Merge • Merge… • For each region: • 3[bit] border << 54[bit] Coefficients • Reduce the number of regions • Merging neighbor regions with similar coefficients.

  39. Merge Example (Comparison) Split – 4 sub regions Threshold = 100,000 # regions = 613 After Merge # region = 411 Split – 2 sub regions Threshold = 100,000 # regions = 387 After Merge # regions = 348

  40. Comparison

  41. The borders of the regions • The borders are more complex • Simple BSP Tree is not enough • Therefore • BSP Tree • Regions labels

  42. Regions Labeling • How many bits are needed for labeling? • ~1000 regions  ~10[bit] • Non-neighbor regions can have the same labeling • ~3[bit] for labeling • Does not depend on the number of regions

  43. Image Coding • For N regions made from M BSP-regions • Border (BSP) – M*3[bit] • Labeling – M*3[bit] • 2D polynomial coefficients – N*24[bit] • 8[bit] x 3 – gray level. • 8[bit] x 3 x 3 - for RGB.

  44. Image Coding • Without Compression • 196,608[Byte] • With Compression • # BSP regions = 387 • # regions = 348 • 3446[Byte] • Compression Raito: • 1 to 57 (>54) Split – 2 sub regions And merging

  45. y t x Image Sequence Coding • Same Idea. • Still Image  (x,y) • Video  (x,y,t) • Split Stage • Decide between x-split, y-split and t-split. • Merge Stage • Same

  46. Real Time Transmission • Compress all movie at once • Achieving best compression. • However • can’t be transmitted on-line • Very high complexity • Deal with chunks of 5~25 frames • Compression ratio decreases • Can be transmitted on-line

  47. Conclusions • Still Images compression • Achieves good results with good compression ratio. • 2 major defects due to region boundaries: • Disappearance of important boundaries • Border artifacts between regions. • Merge Regions • using the same threshold has little effect on performance • adaptive threshold for achieving a giving compression ratio

  48. Conclusions • Image Sequence compression • Achieves good results • At the article: 1 to 200 comp. ratio. • But, compression ratio decreases on high movement scenes. • compression ratio  still images. • Merging regions can be used to balance the buffer size. • Hard to decode at real-time. • Must be coded at frame chunks. • Decoding process is hard to implement in real time environment.

  49. Go To… • Still Images • Split • to 4 sub-regions • to 2 sub-regions • Threshold(2) • Compression • Merge • Threshold • Compression • Image Sequence • Conclusions

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