JPEG2000: New Standard for Still Image Compression

1. JPEG2000: New Standard for Still Image Compression Tao Tao Department of Computer Science University of Central Florida Some of the slides have been adopted from a presentation by Dr. T. Acharya

2. Outline • Introduction • Part I JPEG2000 Coding • Region of Interest • Bit-stream ordering • Conclusion

3. Introduction

4. 5.2 bpp Original image 1.89 bpp Why JPEG2000 ? lossless lossy bit stream Sub-region 1.84 bpp Lower resolution ROI One component

5. Why another still image compression standard? • To address a number of weakness in the existing JPEG standard. • To provide a number of new features that available in the JPEG standard. • Namely, • Allow efficient lossy and lossless compression within a single unified coding framework. • Provide superior image quality at low bit rates. • Support new features such as ROI and a more flexible file format. • Avoid excessive computational and memory complexity.

6. Why another still image compression standard (cont.)? • Larger image. JPEG does not allow for image greater than 64k by 64k without tiling • Transmission in noisy environment. JPEG image quality suffers dramatically when bit-errors are encountered. • Computer generated imagery. JPEG is optimized for natural imagery only. • Compound documents. JPEG is seldom used in the compression of compound documents because of its poor performance on bi-level (text) imagery.

19. JPEG 2000 Standard • Part I : Core Coding System • Part II : Extensions • Part III : Motion JPEG 2000 • Part IV : Conformance Testing • Part V : Reference Software • Part VI : Compound image file format • More parts are coming…

20. Part I JPEG 2000

21. Rate Control Multi component transform Discrete Image Tier-1 Tier-2 Quantization Wavelet Encoder Encoder Transform Coded Image Reconstructed Image Inverse Multi component transform Inverse Wavelet Tier-1 Tier-2 De-quantization Decoder Decoder Transform Coded Image JPEG 2000 - Part I encoder decoder

22. Code block Subbands tile Subband Subband Subband DWT Code block Image Component Tile Subband code block Context & Data Compressed data Bit stream BPC BAC Layer formation and data formatting JPEG2000 Encoder

23. Rate Control Multi component transform Discrete Image Tier-1 Tier-2 Quantization Wavelet Encoder Encoder Transform Coded Image Region of Interest Multi-Component Transform • Part I allows color transformation on first three components • Reversible Color Transform (RCT) • Irreversible Color Transform (ICT)

24. Irreversible Color Transform • The ICT is nothing more than the classic RGB-to-YCrCb color space transform.

25. Reversible Color Transform • The RCT is simply a reversible integer-to-integer approximation to the ICT

26. Rate Control Multi component transform Discrete Image Tier-1 Tier-2 Quantization Wavelet Encoder Encoder Transform Coded Image Region of Interest Discrete Wavelet Transform • DWT • Lifting Scheme

27. Rate Control Multi component transform Discrete Image Tier-1 Tier-2 Quantization Wavelet Encoder Encoder Transform Coded Image Region of Interest Quantization • Uniform scalar quantization with deadzone • No quantization in lossless mode • Quantization rule:

28. Deadzone Quantization • Small coefficients increase the quality of the image the least, and they still mess up the run-length encoding as much as big coefficients. • A deadzone quantizer blocks the small coefficients

29. Rate Control Multi component transform Discrete Image Tier-1 Tier-2 Quantization Wavelet Encoder Encoder Transform Coded Image Region of Interest Entropy Coding • Tier-1 coding • Bit Plane Coding (BPC) • Tier-2 coding • Tag Tree Coding

30. Tier-1 Coding • Tier-1 coding is performed on code blocks • For each bit plane, there are 3 coding passes, similar to those in EZW or SPIHT: • Significance Pass (a/(a+r)) • Refinement Pass (a/(a+r)) • Clean Up Pass (a) Example of scan pattern for a 10x5 code block

31. Tier-1 Coding (cont.)Sign-Magnitude Representation Samples in a code block Sign Magnitude

32. Tier-1 Coding (cont.)Significance • Each sample in the code-block has an associated binary state variable called its significance. • A sample is significant if it is larger than the current bit plane. • A sample is predicted to be significant if any of its 8-connected neighbor has been found to be significant • ‘Significances” are initialized to 0 and may become 1 during the course of the coding of the code-block. • Once the significance for a sample becomes 1, it stays 1 throughout the encoding of the code block. Example: 7 binary format: 0111

33. Tier-1 Coding (cont.)Significance Pass

34. Tier-1 Coding (cont.)Refinement Pass

35. Tier-1 Coding (cont.)Cleanup Pass

36. Example of Tier-1 Coding

37. Original image

38. 2.5 bpp with 4 Level DWT

39. 3.5 bpp with 4 Level DWT

40. Compressed at 4.9 bpp with 4 level DWT

41. Region Of Interest

42. ROI mask • DWT coefficients contributes only to a specific region. • A binary mask generated in the wavelet domain for distinction of ROI and Background

43. Original image

44. ROI Example (encoded at 5.85 bpp)

45. Difference image (d > 5)

46. Bit-stream Ordering

47. 2 level DWT with 16 Code Blocks (CB5) (CB6) (CB7) (CB8) (CB9) (CB10) (CB13) (CB14) (CB11) (CB12) (CB15) (CB16)

48. Assume maximum 4 Bit Planes for each Code Block S : Significance propagation pass M : Magnitude refinement pass C : Clean up pass

49. Bit stream with lower resolution image S : Significance propagation pass M : Magnitude refinement pass C : Clean up pass

50. Bit stream progressive in term of resolution S : Significance propagation pass M : Magnitude refinement pass C : Clean up pass