1. 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. 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. 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. 4 2. Predictive coding  Practice: The value of a pixel can be accurately predicted using a simple predictor of previously observed neighbor pixels. cb ax

5. 5 3. LOCO-I (JPEG-LS)  median edge detector Example: 60105100105 5010010260 105100105 50 e = {+5, +2, -45} Original imagePredictive values LOCO-I: Low complexity lossless compression for images

6. 6 4. CALIC  gradient adjusted predictor gh cbi dax Causal template CALIC: Context-based, adaptive, lossless image coder

7. 7 4. CALIC (cont.)  gradient adjusted predictor gh cbi dax Causal template 403015 4520 25 102105100 d v -d h =105-8=97>80 d v -d h =69-29=40 >32 d v -d h =70-60=10 >8 405550 45506554 102105100 5560 1005045 5055100 Example: =(86+105)/2=96 Sharp horizontalWeak horizontalHorizontal e = -5e = +4 e = +57 =(3*39+55)/4 = 43

8. 8 5. The proposed scheme  Accurate gradient selection predictor (AGSP) fgh ecbi dax Causal template

9. 9 5. The proposed scheme (cont.) Example2: C h =55, C v =50, C + =45, C - =100 =(8*55 + 19*100)/(8+19)=87 5560 1005045 5055 100 405550 45506554 102105 100 Example1: C h =105, C v =65, C + =54, C - =50 =(10*54 + 29*105)/(10+29)=92 e = +8 e = +13 D h =10, D v =30, D + =29, D - =35 D h =19, D v =27, D + =21, D - =8

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

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

12. 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