ERICSSON RESEARCH Media Lab. Signal Processing Laboratory Swiss Federal Institute of Technology, Lausanne
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6 Example: Progressive by quality Image: Bitrates: Woman bpp 0.25 bpp 0.5 bpp 1.0 bpp 2.0 bpp 76
7 0.125 bpp 77
8 0.25 bpp 78
9 0.5 bpp 79
10 1.0 bpp 80
11 2.0 bpp 81
12 Region Of Interest coding Allows certain parts of an image to be coded or decoded in better quality Static: The ROI is decided and coded once for all en the encoder side Dynamic: The ROI can be decided and decoded on the fly from a same bitstream 82
13 ROI: Some visual results 69:1 overall compression ratio No ROI Rectangular ROI 83
14 Regions Of Interest 84
15 Regions Of Interest 85
16 Regions Of Interest 86
17 ROI coding: mask computation 87
18 Region Of Interest coding BASIC IDEA: Calculate wavelet transform of whole image/time calculate ROI mask == set of coefficients that are needed for up to lossless ROI coding Encoding is progressive by accuracy and resolution NOTE: ROI mask need NOT be transmitted to decoder (location and shape of ROI needs however) 88
19 Creation of ROI mask The ROI masks are acquired by looking at the inverse transform Low Inverse transform of the 5-3 filter High For each pixel (X) that is in the ROI, the low and high frequency coefficients (L:s and H:s) that are needed to reconstruct the pixel, are included in the ROI mask n-1 n n+1 2n 2n+1 X:s n-1 n n+1 89
20 ROI Scaling based method Highest BG coeff value is found Coefficient values ROI Coefficients 90
21 ROI MaxShift method After shifting, all the ROI coefficients are larger than the largest BG coefficient Coefficient values ROI Coefficients 91
22 Example: ROI coding Image: Woman ROI: rectangular Scaling value: 6 Progressive type: SNR Bitrate: 4bpp 92
23 0.125 bpp 93
24 0.25 bpp 94
25 0.5 bpp 95
26 1.0 bpp 96
27 2.0 bpp 97
28 4.0 bpp 98
29 Example: ROI coding Image: Woman ROI: rectangular Scaling value: MAXSHIFT Progressive type: SNR Bitrate: 4bpp 99
30 1.0 bpp 100
31 3.0 bpp 101
32 4.0 bpp 102
33 ROI Maxshift mode: what is the gain? Support for arbitrary shaped ROI s with minimal complexity No need to send shape information No need for shape encoder and decoder No need for ROI mask at decoder side Decoder as simple as non-roi capable decoder Can decide in which subband the ROI will begin therefore it can give similar results to the general scaling method 103
34 ROI coding: what do we pay? Lossless image coding with ROI Gold: Rectangular ROI 1,02 1,015 1,01 1,005 1 No ROI S=2 S=4 Maxshift 0,995 0,99 104
35 ROI coding: what do we pay? Lossless image coding with ROI Target - approx. 25% circular ROI - Relative sizes 1,1 1,08 1,06 1,04 1,02 1 No ROI S=2 S=4 Max shift 0,98 0,96 0,94 105
36 Block transform coding Tiling Allow random access to portions of an image Single-Sample Overlap Discrete Wavelet Transform (SSODWT) Exploit overlapping in order to reduce blockiness In part II 106
37 Tiling (128x128, 0.25 bpp) 107
38 SSODWT (128x128, 0.25 bpp) 108
39 Error resilience capabilities Most still image coders use Entropy Coding Variable Length Coding is known to be prone to channel or transmission errors Loss of synchronization C Header Residua l CHANNEL D Bit errors (Noise) Burst errors (Fading) 109
40 Error resilience Error resilience is achieved at two levels: Entropy coding level Code-blocks Termination of arithmetic coding Reset of context Selective arithmetic coding bypass Packet level Short packet format Resynchronization markers 110
41 Visual Frequency Weighting Allows system designers to take advantage of visual perception Utilize knowledge of the visual system s varying sensitivity to spatial frequencies as measured in the contrast sensitivity function (CSF) CSF is determined by the visual frequency of the transform coefficients; One CSF weight per subband Design of CSF weights is an encoder issue; depends on viewing condition of decoded image 111
42 Visual Frequency Weighting (cont.) Fixed Visual Weighting (FVW) & Progressive Visual Coding (PVC) FVW: CSF are chosen according to the final viewing condition PVC: Visual weights are changed during the embedded process 112
43 Line based transforms Most acquisition devices are serial in nature Most common scanning patterns work on a line-by-line basis Traditional wavelet transforms require whole image to be buffered and filtered Filtering along a line, requires one line Filtering along a column requires whole image That is too complex! 113
44 Line based transforms A way for low memory implementation of the wavelet transform Same wavelet coefficients as full frame wavelet transform Same encoding results as in full frame wavelet transform 114
45 File Format File Format File Format extension.jp2 Possible to include XML data Possible to include vendor specific information Possible to include IPR information Possible to add URL to file format Can be used by an application to acquire more information about the associated vendor specific extensions 115
46 JPEG2000 Part I Core Coding System Schedule March 2000, FCD September 1, FDIS December 2000, IS Only editorial changes allowed File extension,.jp2 116
47 Extensions Schedule JPEG2000 Part II March 2000, WD September 2000, CD December 2000, FCD April 2001, FDIS July 2001, IS File extension.jpx 117
48 JPEG2000 Part III Motion-JPEG2000 Schedule March 2000, WD December 2000, CD March 2001, FCD July 2001, FDIS November 2001, IS Based on JPEG2000 Part I No inter-frame dependencies 118
49 JPEG2000 Part V Reference Software Schedule March 2000, ED July 2000, CD December 2000, FCD April 2001, FDIS July 2001, IS Software Java TM implementation (EPFL, Canon, Ericsson) C implementation (UBC / ImagePower) 119
50 JPEG2000 Part IV Compliance Tests Schedule July 2000, WD December 2000, CD March 2001, FCD July 2001, FDIS November 2001, IS 120
51 JPEG2000 Part V Reference software Schedule July 2000, CD December 2000, FCD March 2001, FDIS July 2001, IS 121
52 JPEG2000 Part VI Compound Image File Format Schedule August 2000, CD December 2000, FCD March 2001, FDIS July 2001, IS 122
53 JPEG2000 Part VII Technical report Schedule December 2000, PDTR March 2001, DTR July 2001, TR 123
54 Conclusions Advanced Still Image Coding System More complex than JPEG but it offers many interesting functionalities No IPR associated to Part I of the standard (free licensing) Intended to become the key standard for still image coding in the next millennium 124
55 More information JJ JPEG Web site: EUROSTILL SPEAR 125
56 Contact us for any further information Touradj Ebrahimi Charilaos Christopoulos 126
57 Acknowledgements * Mr. Joel Askelöf, Ericsson Mr. Nicolas Aspert, EPFL Dr. Eiji Atsumi, Mitsubishi, Japan Mr. Martin Boliek, Ricoh Dr. A. Chien, Eastman Kodak Company, USA Dr. Troy Chinen, Fuji Mr. Raphael Grosbois, EPFL Prof. Faouzi Kossentini, UBC Mr. Mathias Larsson, Ericsson Dr. Daniel Lee, HP Labs Dr. Eric Majani, CRF Prof. Michael Marcellin, Univ. of Arizona Prof. Andrew Perkis, NTNU Dr. Majid Rabbani, Eastman Kodak Company Mr. Diego Santa Cruz, EPFL Prof. Anasthasious Skodras, Univ. Of Padras Dr. David Taubman, HP Labs & Univ. New South Wales and many others... * In alphabetical order 127
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