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A Gradient Based Predictive Coding for Lossless Image Compression

A Gradient Based Predictive Coding for Lossless Image Compression. Source: IEICE Transactions on Information and Systems, Vol. E89-D, No. 7, July 2006. Authors: Haijiang Tang and Sei-ichiro Kamata Speaker: Chia-Chun Wu Date: 2006/10/19. Outline. 1. Lossless image compression

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A Gradient Based Predictive Coding for Lossless Image Compression

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  1. A Gradient Based Predictive Coding for Lossless Image Compression Source: IEICE Transactions on Information and Systems, Vol. E89-D, No. 7, July 2006. Authors: Haijiang Tang and Sei-ichiro Kamata Speaker: Chia-Chun Wu Date: 2006/10/19

  2. Outline • 1. Lossless image compression • 2. Predictive coding • 3. LOCO-I (JPEG-LS) • 4. CALIC • 5. The proposed scheme • 6. Experimental results • 7. Conclusions

  3. 1. Lossless image compression • Lossless: reconstruct the coded image identically to the original image • Applications: • Medical imaging • Remote sensing • Fax • Image archiving • Art work preserving • …

  4. 2. Predictive coding • Practice:The value of a pixel can be accurately predicted using a simple predictor of previously observed neighbor pixels.

  5. 3. LOCO-I (JPEG-LS) LOCO-I: Low complexity lossless compression for images • median edge detector Example: e = {+5, +2, -45} Original image Predictive values

  6. 4. CALIC CALIC: Context-based, adaptive, lossless image coder • gradient adjusted predictor Causal template

  7. =(86+105)/2=96 4. CALIC (cont.) • gradient adjusted predictor Causal template Sharp horizontal Horizontal Weak horizontal Example: dv-dh=69-29=40 >32 dv-dh=105-8=97>80 dv-dh=70-60=10 >8 =(3*39+55)/4 = 43 e = +57 e = -5 e = +4

  8. 5. The proposed scheme • Accurate gradient selection predictor (AGSP) Causal template

  9. 5. The proposed scheme (cont.) Example1: Dh=10, Dv=30, D+=29, D-=35 Ch=105, Cv=65, C+=54, C-=50 =(10*54 + 29*105)/(10+29)=92 e = +8 Example2: Dh=19, Dv=27, D+=21, D-=8 Ch=55, Cv=50, C+=45, C-=100 =(8*55 + 19*100)/(8+19)=87 e = +13

  10. 6. Experimental results • Test images: gray scale, 512 × 512 LOCO-I CALIC AGSP Amplitude images for prediction errors

  11. 6. Experimental results (cont.) • Compression performance

  12. 7. Conclusions • A new adaptive prediction algorithm based on accurate gradient estimation and selection • All the possible contexts are considered in context modeling • Handles complex structures more robustly • Maintain the simplicity of implementation and computation

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