JPEG 2000 New Standard for Still Image Compression

JPEG 2000: New Standard for Still Image Compression Tao Department of Computer Science University of Central Florida Some of the slides have been adopted from a presentation by Dr. T. Acharya 1

Outline • • • Introduction Part I JPEG 2000 Coding Region of Interest Bit-stream ordering Conclusion 2

Introduction 3

Why JPEG 2000 ? 5. 2 bpp los sy lossless Original image b it s t r e a m n regio Sub- Low er r eso 1. 84 bpp luti on RO I One component 1. 89 bpp 4

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. 5

Why another still image compression standard (cont. )? – Larger image. JPEG does not allow for image greater than 64 k by 64 k 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. 6

7

8

9

10

11

12

13

14

15

16

17

18

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… 19

Part I JPEG 2000 20

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

JPEG 2000 Encoder Subbands tile Subband DWT Subband Image Component code block BPC Subband Code block Tile Context & Data Code block BAC Subband Compressed data Bit stream Layer formation and data formatting 22

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

Irreversible Color Transform l The ICT is nothing more than the classic RGB-to-YCr. Cb color space transform. 24

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

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

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

Deadzone Quantization l l 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 28

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

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 10 x 5 code block 30

Tier-1 Coding (cont. ) Sign-Magnitude Representation Samples in a code block Sign 0 0 1 0 0 4 10 2 11 -1 13 15 4 0 9 -5 14 7 8 -15 3 4 10 2 11 Magnitude 1 4 5 8 13 15 0 9 14 7 15 3 31

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. : 7 binary format: 0111 Example 32

Tier-1 Coding (cont. ) Significance Pass 33

Tier-1 Coding (cont. ) Refinement Pass 34

Tier-1 Coding (cont. ) Cleanup Pass 35

Example of Tier-1 Coding 36

Original image 37

2. 5 bpp with 4 Level DWT 38

3. 5 bpp with 4 Level DWT 39

Compressed at 4. 9 bpp with 4 level DWT 40

Region Of Interest 41

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

Original image 43

ROI Example (encoded at 5. 85 bpp) 44

Difference image (d > 5) 45

Bit-stream Ordering 46

2 level DWT with 16 Code Blocks 2 LL 2 HL (CB 1) (CB 2) 2 LH 2 HH (CB 3) (CB 4) (CB 9) (CB 10) 1 LH (CB 11) (CB 5) (CB 6) 1 HL (CB 7) (CB 8) (CB 13) (CB 14) 1 HH (CB 12) (CB 15) (CB 16) 47

Assume maximum 4 Bit Planes for each Code Block BP 4 C C C C BP 3 S M C S M C S M C S M C BP 2 S M C S M C S M C S M C BP 1 S M C S M C S M C S M C CB 19 CB 11 CB 12 CB 13 CB 14 CB 15 CB 16 CB 1 CB 2 CB 3 CB 4 2 LL 2 HL 2 LH 2 HH CB 5 CB 6 CB 7 1 HL CB 8 CB 9 1 LH 1 HH S : Significance propagation pass M : Magnitude refinement pass C : Clean up pass 48

Bit stream with lower resolution image BP 4 C C C C BP 3 S M C S M C S M C S M C BP 2 S M C S M C S M C S M C BP 1 S M C S M C S M C S M C CB 19 CB 11 CB 12 CB 13 CB 14 CB 15 CB 16 CB 1 CB 2 CB 3 CB 4 2 LL 2 HL 2 LH 2 HH CB 5 CB 6 CB 7 CB 8 CB 9 1 HL 1 LH 1 HH S : Significance propagation pass M : Magnitude refinement pass C : Clean up pass 49

Bit stream progressive in term of resolution BP 4 C C C C BP 3 S M C S M C S M C S M C BP 2 S M C S M C S M C S M C BP 1 S M C S M C S M C S M C CB 19 CB 11 CB 12 CB 13 CB 14 CB 15 CB 16 CB 1 CB 2 CB 3 CB 4 2 LL 2 HL 2 LH 2 HH CB 5 CB 6 CB 7 CB 8 CB 9 1 HL 1 LH 1 HH S : Significance propagation pass M : Magnitude refinement pass C : Clean up pass 50

Bit stream progressive in term of quality BP 4 C C C C BP 3 S M C S M C S M C S M C BP 2 S M C S M C S M C S M C BP 1 S M C S M C S M C S M C CB 19 CB 11 CB 12 CB 13 CB 14 CB 15 CB 16 CB 1 CB 2 CB 3 CB 4 CB 5 2 LL 2 HL 2 LH 2 HH CB 6 CB 7 CB 8 CB 9 1 HL 1 LH 1 HH S : Significance propagation pass M : Magnitude refinement pass C : Clean up pass 51

Bit stream w/ highest resolution & highest quality BP 4 C C C C BP 3 S M C S M C S M C S M C BP 2 S M C S M C S M C S M C BP 1 S M C S M C S M C S M C CB 19 CB 11 CB 12 CB 13 CB 14 CB 15 CB 16 CB 1 CB 2 CB 3 CB 4 2 LL 2 HL 2 LH 2 HH CB 5 CB 6 CB 7 1 HL CB 8 CB 9 1 LH 1 HH S : Significance propagation pass M : Magnitude refinement pass C : Clean up pass 52

Bit stream w/ highest resolution & target SNR quality BP 4 C C C C BP 3 S M C S M C S M C S M C BP 2 S M C S M C S M C S M C BP 1 S M C S M C S M C S M C CB 19 CB 11 CB 12 CB 13 CB 14 CB 15 CB 16 CB 1 CB 2 CB 3 CB 4 2 LL 2 HL 2 LH 2 HH CB 5 CB 6 CB 7 1 HL CB 8 CB 9 1 LH 1 HH S : Significance propagation pass M : Magnitude refinement pass C : Clean up pass 53

Bit stream w/ highest resolution & target file size BP 4 C C C C BP 3 S M C S M C S M C S M C BP 2 S M S M S M C C C C C S M C S M C S M C S M C S M C S M C CB 19 CB 11 CB 12 CB 13 CB 14 CB 15 CB 16 BP 1 CB 2 CB 3 CB 4 2 LL 2 HL 2 LH 2 HH CB 5 CB 6 CB 7 1 HL CB 8 CB 9 1 LH 1 HH S : Significance propagation pass M : Magnitude refinement pass C : Clean up pass 54

JPEG 2000 Summary Key Features • Superior low bit-rate performance • Continuous-tone & bi-level compression • Lossless & lossy compression • Progressive transmission by resolution, pixel accuracy or both • Random code stream access & processing • Robustness to bit-errors • … Sample Applications • • • Web browsing & printing Digital Camera Satellite Imagery Medical Imagery … 55

Thank You 56