Parallel Design of JPEG2000 Image Compression

1. April 30th, 2003 Parallel Design of JPEG2000 Image Compression Xiuzhen Huang CS Department UC Santa Barbara

2. Outline • Introduction to image compression • JPEG2000 compression scheme • Parallel implementation of JPEG2000 • On distributed-memory multiprocessors • On shared-memory multiprocessors • Conclusion

3. Introduction to Image Compression Why do we need image compression? File size of a small digital photo without compression: 1280  800  3 (RGB) = 3 M bytes 800 pixels To speedup the image transmission over Internet and reduce image storage space, we need compression 1280 pixels

4. Introduction to Image Compression Original Picture 3 M bytes JPEG2000 Compression 19 K bytes • Compression Ratio: >150 times ! • No noticeable difference in picture quality

5. JPEG2000 International Standard JPEG2000: the new international standard for image compression, is much more efficient than the old JPEG international standard. For the same compression ratio / bit rate / file size, the JPEG2000 picture has much better quality. JPEG JPEG2000 Original Picture Compression ratio : 50:1 Strong blockiness

6. JPEG2000 International Standard JPEG2000 has a much Higher computational complexity than JPEG, especially for larger pictures. Need parallel implementation to reduce compression time.

7. JPEG2000 Compression Scheme Major steps of JPEG2000 image compression Wavelet Transform Blockwise Partition Coding of each block Input Binary Compressed data • Wavelet transform uses most of the image compression time (>80%) • parallel implementation should focus on wavelet transform

8. JPEG2000 Compression Scheme Brief Introduction to Wavelet Transform Step 1: Horizontal wavelet transform of an image for each row do 1-D wavelet transform; end What is 1-D wavelet transform ?

9. JPEG2000 Compression Scheme A simple example: 1-D Haar wavelet transform Low- Frequency coefficients Low-pass filter Average of neighboring pixels [1, 1] 2 First half of the output One array of image data Down-sample by 2 High- Frequency coefficients Difference of neighboring pixels [1, -1] 2 Second half of the output high-pass filter High Low Horizontal Wavelet Transform of Each Row

10. JPEG2000 Compression Scheme Wavelet Transform Step 2: Vertical transform of image for each column of the new image do 1-D wavelet transform; end

11. JPEG2000 Compression Scheme High Low Horizontal Wavelet Transform of Each Row Vertical Wavelet Transform of Each Column Low Low High Low High High Low High

12. Parallel Design of JPEG2000 Compression Two Parallel Computing Architectures Distributed-Memory Multiprocessors • Each processor has its own memory module • Processors communicate to each other over a high-speed network • Programming tool: MPI (Message Passing Interface) Shared-Memory Multiprocessors • Has a single address space. • Allow processors to communicate through variables stored in a shared address space • Programming tool: openMP

13. Parallel Implementation of JPEG2000 Compression on Distributed-Memory Multiprocessors

14. Parallel Design of JPEG2000 Compression-DMP Traditional Approach • The image is first divided into n regions on rows. • Each processor performs 1-D horizontal wavelet transform • Then, the new image is divided into n regions on columns. • Each processor performs 1-D vertical wavelet transform. This approach requires intensive data transmission among processors, has very high network communication cost.

15. Parallel Design of JPEG2000 Compression-DMP Tiling Approach P1 P2 P3 • JPEG2000 international standard supports tile-based image compression. • A large image is divided into several tiles and each image tile is compressed independently. P5 P4 P6 P8 P7 P9

16. Parallel Design of JPEG2000 Compression-DMP Choose MPI for parallel implementation of JPEG2000, because the JPEG2000 software is written in C, which supported by MPI. Basic framework is:

17. Parallel Design of JPEG2000 Compression-DMP Image: 512x512 Size: 32 Compression Time (Sec) Size: 256 Number of processors The picture shows the compression time using different tile size. For each tile size,processor number increases,compression time is reduced.The small tile need larger computation overhead.

18. Parallel Design of JPEG2000 Compression-DMP Note • There is a jump between one process and two processes. • When there is only one process, JPEG2000 compression is sequential • If there are more than two processes involved in the program, Process 1 is responsible for collecting data, while the others are responsible for processing different tiles and sending processed data back to the Process 1.

19. Parallel Implementation of JPEG2000 Compression on Shared-Memory Multiprocessors

20. Parallel Design of JPEG2000 Compression-SMP A problem with tile-based approach Images compressed by JPEG, JPEG2000, and JPEG2000 with relatively small tiles. Each tile is compressed independently, which causes discontinuity across tile edges, also called blockiness.

21. Parallel Design of JPEG2000 Compression-SMP • Another parallel architecture is shared-memory multiprocessors. • The excellent price-performance ratio of Intel-based SMPs make such systems very popular in many data processing applications. • There are also many available programming tools for shared memory processor, such as openMP and Java Threads.

22. Parallel Design of JPEG2000 Compression-SMP • In SMP, we do not need worry about data communication over network, because the data is in the shared memory. So there is no need for tile partitioning. • Therefore, we can use the traditional data partitioning approach for horizontal and vertical wavelet transforms.

23. Parallel Design of JPEG2000 Compression-SMP • JPEG2000 image compression is implemented on a 4-processor SMP system using direct openMP. • The speedup in wavelet transform is only about 1.6 times, which is supposed to be near 4 times. • Why?

24. Parallel Design of JPEG2000 Compression-SMP It is found that the vertical wavelet transform requires more than 10 times the horizontal transform. But we know that both vertical and horizontal transforms have the same number of operations. vertical horizontal

25. Parallel Design of JPEG2000 Compression-SMP Cache Miss Problem • In computer memory, the image data is stored line by line in a raster-scan order (from left to right, from top to bottom). • Each continuous block of image data is brought into the cache from memory for wavelet transform. • In horizontal wavelet transform, as the filter window is moving, the data of next transform is often available, few cache miss.

26. Parallel Design of JPEG2000 Compression-SMP Cache Miss Problem data • In vertical wavelet transform, the filtering is done in the vertical direction, however, the data is brought into cache in a horizontal way. So, there are very frequent cache miss. filtering Solution Do vertical transform of several columns at the same time to make full use of the existing data in the cache. , instead of column by column Significantly reduces cache miss.

27. Parallel Design of JPEG2000 Compression-SMP The vertical transform is speed up by about 10 times. Original Vertical transform Improved Vertical transform

28. Parallel Design of JPEG2000 Compression-SMP Using the improved vertical wavelet transform, the overall speedup times of wavelet transform is now close to the number of processors.

29. Conclusion • Give a brief review JPEG2000 image compression. • Discussed two approaches for parallel implementation of JPEG2000 image compression: distributed memory multiprocessor and shared memory multiprocessor. Question?