1. 12/12/2003EZW Image Coding Duarte and Haupt 1 Examining The Embedded Zerotree Wavelet (EZW) Image Coding Method Marco Duarte and Jarvis Haupt ECE 533 December 12, 2003

2. 12/12/2003EZW Image Coding Duarte and Haupt 2 Overview Statement of the Problem Our Approach Results Analysis Conclusions

3. 12/12/2003EZW Image Coding Duarte and Haupt 3 The Desire The EZW Algorithm addresses a twofold goal: Optimal image quality for a given compression rate Incremental quality levels achieved by simple bitstream truncation

4. 12/12/2003EZW Image Coding Duarte and Haupt 4 How does EZW Succeed? While no claims of optimality are made, the nature of multiresolution analysis gives substantial quality for compressed images Embedding is accomplished via a series of decisions that distinguish the reconstructed image from the null image Using more of the symbol stream refines the image

5. 12/12/2003EZW Image Coding Duarte and Haupt 5 Our Approach The EZW Algorithm was implemented in Matlab using the Haar Wavelet Decomposition Reconstruction Approximations were generated and compared to the original images Error was qualified visually and quantified using the PSNR (Peak Signal to Noise Ratio)

6. 12/12/2003EZW Image Coding Duarte and Haupt 6 Results - Lena Compare the original Lena image (left) to its reconstruction using 10% of the EZW-generated symbols Notice the blurred edges, loss of detail, and blocking

7. 12/12/2003EZW Image Coding Duarte and Haupt 7 Lena Now, the original Lena (left) is compared to the reconstruction using 30% of the symbol stream Compression artifacts are almost invisible!

8. 12/12/2003EZW Image Coding Duarte and Haupt 8 Marco Now, we compare the original Marco image (left) to its 10% reconstruction

9. 12/12/2003EZW Image Coding Duarte and Haupt 9 Marco And again, compare the original (left) to the 30% reconstruction Notice the fine detail in the hair in the reconstructed image!

10. 12/12/2003EZW Image Coding Duarte and Haupt 10 Jarvis Finally, compare the original Jarvis image (left) to the 10% reconstruction. Blocking artifacts can be seen in skin color.

11. 12/12/2003EZW Image Coding Duarte and Haupt 11 Jarvis And to the 30% reconstruction. No noticeable artifacts!

12. 12/12/2003EZW Image Coding Duarte and Haupt 12 PSNR PSNR calculations were performed for each image using a variety of bits per pixel compressions For each test image, PSNR data is shown for three cases No Compression Huffman Encoding Arithmetic Coding

13. 12/12/2003EZW Image Coding Duarte and Haupt 13 PSNR vs. Compression Rate Lena Image As expected, PSNR is higher for the encoded streams for a given compression rate

14. 12/12/2003EZW Image Coding Duarte and Haupt 14 PSNR vs. Compression Rate Marco Image

15. 12/12/2003EZW Image Coding Duarte and Haupt 15 PSNR vs. Compression Rate Jarvis Image

16. 12/12/2003EZW Image Coding Duarte and Haupt 16 Algorithmic Performance The next figure illustrates how efficiently the EZW Algorithm encodes position information The best case would be a one-to-one correspondence between percent of symbols and percent of nonzero coefficients (no position information)

17. 12/12/2003EZW Image Coding Duarte and Haupt 17 Close to Theoretical Bound Note how close the correspondence for each image is to the one-to-one limit

18. 12/12/2003EZW Image Coding Duarte and Haupt 18 Conclusions The EZW Algorithm encodes significance information very compactly Coupled with the power of multiresolution analysis, the EZW Algorithm yields significant compression with little quality loss Image quality improves progressively as more symbols are used in decoding Modest extensions of the EZW Algorithm have been proposed and were examined but not implemented