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A hybrid quantization scheme for image compression

A hybrid quantization scheme for image compression. Source : Image and Vision Computing, Vol: 22, Issue: 3, pp. 203-213, March 1, 2004 Author : Paul Shelley, Xiaobo Li, Bin Han Speaker : Chang-Chu Chen Date : 6/1/2004. Outline. Image compression based on Wavelet

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A hybrid quantization scheme for image compression

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  1. A hybrid quantization scheme for image compression Source : Image and Vision Computing, Vol: 22, Issue: 3, pp. 203-213, March 1, 2004 Author : Paul Shelley, Xiaobo Li, Bin Han Speaker : Chang-Chu Chen Date : 6/1/2004

  2. Outline • Image compression based on Wavelet • SPIHT and ModLVQ • Proposed scheme • Results • Conclusions

  3. Image compression based on Wavelet Original image Wavelet coefficients Quantization Coding 10 11 0 0 1 10 0 0 0 1 0 0 0 0 0 0 1 0 1 0 10 0 0 0 0 1010 EZW, SPIHT, ModLVQ…

  4. Subband LL3 HL3 LL3 HL3 HL2 HL2 LH3 HH3 LH3 HH3 LL1 HL1 HL1 LL1 LH2 HH2 LH2 HH2 LH1 HH1 LH1 HH1

  5. Quadtree LL2 X X LL2 HL2 X HL1 LH2 HH2 LH2 Y Y Y LH1 HH1 LH1

  6. Scan order

  7. Zerotree Tk : kth threshold DWT coefficients

  8. SPIHT and ModLVQ • Set Partitioning in Hierarchical Trees • Modified lattice vector quantization 2x2 blocks 4x4 blocks Zerotree Threshold Tk+1 = 0.5 * Tk Zerotree Threshold Tk+1 = 0.65 * Tk

  9. SPIHT and ModLVQ(cont.) Smooth image Detailed image

  10. SPIHT and ModLVQ(cont.) Smooth image Detailed image

  11. Proposed Scheme • Hy-Q (Hybrid Quantization) Smooth subimage SPIHT divide image to subimages ModLVQ Detailed subimage

  12. Proposed Scheme (cont.) Threshold 0.94

  13. Results

  14. Results (cont.)

  15. Results (cont.) • For uniformly smooth or uniformly detailed images, Hy-Q provides over 85% of the time. • For some feature quadrants are mostly smooth and the others are mostly detailed, Hy-Q provides over 77% of the time.

  16. Conclusions • Proposed a new algorithm Hy-Q for the quantization step of wavelet image compression. • Proposed scheme mixes two quantization strategies : • SPIHT is used for quantize the smooth regions. • ModLVQ is used for quantize the detailed regions.

  17. Discrete Wavelet Transform -Haar Phase 1) Horizontal: Phase 2) Vertical:

  18. Discrete Wavelet Transform -Haar(cont.) Example Phase 1) Horizontal: Level one Phase 2) Vertical: (Level one is done) Phase 1) Horizontal: Level two Phase 2) Vertical: (Level two is done)

  19. LL2 HL2 LL1 HL1 LH2 HH2 LH1 HH1 Discrete Wavelet Transform -Haar(cont.) Example 1” Wavelet 2” Wavelet

  20. SPIHT • Notationsn :H : Set of coordinates of all spatial orientation tree rootsLSP : List of significant pixelsLIP : List of insignificant pixelsLIS : List of insignificant Sets type A : Set of coordinates of all descendants of a node type B : Set of coordinates of all descendants of a node, but not include this node

  21. SPIHT algorithm Output: n= 5 // 25=32 (threshold T0 ) H=(0,0), (1,0), (0,1), (1,1) // head (roots of trees) LIP: (0,0)→(0,1)→(1,0)→(1,1) LIS: LSP:

  22. SPIHT algorithm (cont.) Output: 63 –34 –31 23 10(+) 11(-) 0 0 n= 5 // 25=32 (threshold T0 ) H=(0,0), (1,0), (0,1), (1,1) // head (roots of trees) LIP: (0,0)→(0,1)→(1,0)→(1,1) X X R R LIS: A(0,1)→A(1,0)→A(1,1) LSP: (0,0) (0,1)

  23. SPIHT algorithm (cont.) Output: 63 –34 –31 23 A(0,1) 49 10 14 –13 10(+) 11(-) 0 0 1 10 0 0 0 n= 5 // 25=32 (threshold T0 ) H=(0,0), (1,0), (0,1), (1,1) // head (roots of trees) LIP: (0,0)→(0,1)→(1,0)→(1,1) →(0,2) →(0,3) →(1,2) →(1,3) X X R R X R R R LIS: A(0,1)→A(1,0)→A(1,1) →B(0,1) X LSP: (0,0) (0,1) (0,2)

  24. SPIHT algorithm (cont.) Output: 63 –34 –31 23 A(0,1) 49 10 14 –13 A(1,0) 15 14 –9 -7 10(+) 11(-) 0 0 1 10 0 0 0 1 0 0 0 0 n= 5 // 25=32 (threshold T0 ) H=(0,0), (1,0), (0,1), (1,1) // head (roots of trees) LIP: (0,0)→(0,1)→(1,0)→(1,1) →(0,2) →(0,3) →(1,2) →(1,3) →(2,0) →(2,1) →(3,0) →(3,1) X X R R X R R R R R R R LIS: A(0,1)→A(1,0)→A(1,1) →B(0,1) →B(1,0) X X LSP: (0,0) (0,1) (0,2)

  25. SPIHT algorithm (cont.) Output: 63 –34 –31 23 A(0,1) 49 10 14 –13 A(1,0) 15 14 –9 -7 A(1,1) 10(+) 11(-) 0 0 1 10 0 0 0 1 0 0 0 0 0 n= 5 // 25=32 (threshold T0 ) H=(0,0), (1,0), (0,1), (1,1) // head (roots of trees) LIP: (0,0)→(0,1)→(1,0)→(1,1) →(0,2) →(0,3) →(1,2) →(1,3) →(2,0) →(2,1) →(3,0) →(3,1) X X R R X R R R R R R R LIS: A(0,1)→A(1,0)→A(1,1) →B(0,1) →B(1,0) X X R LSP: (0,0) (0,1) (0,2)

  26. SPIHT algorithm (cont.) Output: 63 –34 –31 23 A(0,1) 49 10 14 –13 A(1,0) 15 14 –9 -7 A(1,1) 10(+) 11(-) 0 0 1 10 0 0 0 1 0 0 0 0 0 B(0,1) 0 n= 5 // 25=32 (threshold T0 ) H=(0,0), (1,0), (0,1), (1,1) // head (roots of trees) LIP: (0,0)→(0,1)→(1,0)→(1,1)→(0,2)→(0,3)→(1,2)→(1,3)→(2,0)→(2,1)→(3,0)→(3,1) X X R R X R R R R R R R LIS: A(0,1)→A(1,0)→A(1,1)→B(0,1) X X R R LSP: (0,0) (0,1) (0,2)

  27. SPIHT algorithm (cont.) Output: 63 –34 –31 23 A(0,1) 49 10 14 –13 A(1,0) 15 14 –9 -7 A(1,1) 10(+) 11(-) 0 0 1 10 0 0 0 1 0 0 0 0 0 B(0,1) B(1,0) 0 1 n= 5 // 25=32 (threshold T0 ) H=(0,0), (1,0), (0,1), (1,1) // head (roots of trees) LIP: (0,0)→(0,1)→(1,0)→(1,1)→(0,2)→(0,3)→(1,2)→(1,3)→(2,0)→(2,1)→(3,0)→(3,1) X X R R X R R R R R R R LIS: A(0,1)→A(1,0)→A(1,1)→B(0,1)→B(1,0)→A(2,0)→A(2,1)→A(3,0)→A(3,1) X X R R R LSP: (0,0) (0,1) (0,2)

  28. SPIHT algorithm (cont.) Output: 63 –34 –31 23 A(0,1) 49 10 14 –13 A(1,0) 15 14 –9 -7 A(1,1) 10(+) 11(-) 0 0 1 10 0 0 0 1 0 0 0 0 0 B(0,1) B(1,0) A(2,0) 0 1 0 n= 5 // 25=32 (threshold T0 ) H=(0,0), (1,0), (0,1), (1,1) // head (roots of trees) LIP: (0,0)→(0,1)→(1,0)→(1,1)→(0,2)→(0,3)→(1,2)→(1,3)→(2,0)→(2,1)→(3,0)→(3,1) X X R R X R R R R R R R LIS: A(0,1)→A(1,0)→A(1,1)→B(0,1)→B(1,0)→A(2,0)→A(2,1)→A(3,0)→A(3,1) X X R R R R LSP: (0,0) (0,1) (0,2)

  29. SPIHT algorithm (cont.) Output: 63 –34 –31 23 A(0,1) 49 10 14 –13 A(1,0) 15 14 –9 -7 A(1,1) 10(+) 11(-) 0 0 1 10 0 0 0 1 0 0 0 0 0 B(0,1) B(1,0) A(2,0) A(2,1) –1 47 –3 –2 0 1 0 1 0 10 0 0 n= 5 // 25=32 (threshold T0 ) H=(0,0), (1,0), (0,1), (1,1) // head (roots of trees) LIP: (0,0)→(0,1)→(1,0)→(1,1)→(0,2)→(0,3)→(1,2)→(1,3)→(2,0)→(2,1)→(3,0)→(3,1)→ X X R R X R R R R R R R (4,2)→(4,3)→(5,2)→(5,3) R X R R LIS: A(0,1)→A(1,0)→A(1,1)→B(0,1)→B(1,0)→A(2,0)→A(2,1)→A(3,0)→A(3,1) X X R R R R X LSP: (0,0) (0,1) (0,2) (4,3)

  30. SPIHT algorithm (cont.) Output: 63 –34 –31 23 A(0,1) 49 10 14 –13 A(1,0) 15 14 –9 -7 A(1,1) 10(+) 11(-) 0 0 1 10 0 0 0 1 0 0 0 0 0 B(0,1) B(1,0) A(2,0) A(2,1) –1 47 –3 –2 A(3,0) 0 1 0 1 0 10 0 0 0 n= 5 // 25=32 (threshold T0 ) H=(0,0), (1,0), (0,1), (1,1) // head (roots of trees) LIP: (0,0)→(0,1)→(1,0)→(1,1)→(0,2)→(0,3)→(1,2)→(1,3)→(2,0)→(2,1)→(3,0)→(3,1)→ X X R R X R R R R R R R (4,2)→(4,3)→(5,2)→(5,3) R X R R LIS: A(0,1)→A(1,0)→A(1,1)→B(0,1)→B(1,0)→A(2,0)→A(2,1)→A(3,0)→A(3,1) X X R R R R X R LSP: (0,0) (0,1) (0,2) (4,3)

  31. SPIHT algorithm (cont.) Output: 63 –34 –31 23 A(0,1) 49 10 14 –13 A(1,0) 15 14 –9 -7 A(1,1) 10(+) 11(-) 0 0 1 10 0 0 0 1 0 0 0 0 0 B(0,1) B(1,0) A(2,0) A(2,1) –1 47 –3 –2 A(3,0) A(3,1) 0 1 0 1 0 10 0 0 0 0 n= 5 // 25=32 (threshold T0 ) H=(0,0), (1,0), (0,1), (1,1) // head (roots of trees) LIP: (0,0)→(0,1)→(1,0)→(1,1)→(0,2)→(0,3)→(1,2)→(1,3)→(2,0)→(2,1)→(3,0)→(3,1)→ X X R R X R R R R R R R (4,2)→(4,3)→(5,2)→(5,3) R X R R LIS: A(0,1)→A(1,0)→A(1,1)→B(0,1)→B(1,0)→A(2,0)→A(2,1)→A(3,0)→A(3,1) X X R R R R X R R LSP: (0,0) (0,1) (0,2) (4,3)

  32. SPIHT algorithm (cont.) Refine the set LSP (0,0) (0,1) (0,2) (4,3) according to the refinement of EZW and generate 1010 Output: 10 11 0 0 1 10 0 0 0 1 0 0 0 0 0 0 1 0 1 0 10 0 0 0 0 1010

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