Paper
29 December 2000 Lossless and near-lossless image compression with successive refinement
Author Affiliations +
Proceedings Volume 4310, Visual Communications and Image Processing 2001; (2000) https://doi.org/10.1117/12.411838
Event: Photonics West 2001 - Electronic Imaging, 2001, San Jose, CA, United States
Abstract
We present a technique that provides progressive transmission and near-lossless compression in one single framework. The proposed technique produces a bitstream that results in progressive reconstruction of the image just like what one can obtain with a reversible wavelet codec. In addition, the proposed scheme provides near-lossless reconstruction with respect to a given bound after each layer of the successively refinable bitstream is decoded. We formulate the image data compression problem as one of asking the optimal questions to determine, respectively, the value or the interval of the pixel, depending on whether one is interested in lossless or near-lossless compression. New prediction methods based on the nature of the data at a given pass are presented and links to the existing methods are explored. The trade-off between non- causal prediction and data precision is discussed within the context of successive refinement. Context selection for prediction in different passes is addressed. Finally, experimental results for both lossless and near-lossless cases are presented, which are competitive with the state-of-the-art compression schemes.
© (2000) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ismail Avcibas, Nasir D. Memon, Bulent Sankur, and Khalid Sayood "Lossless and near-lossless image compression with successive refinement", Proc. SPIE 4310, Visual Communications and Image Processing 2001, (29 December 2000); https://doi.org/10.1117/12.411838
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Cited by 5 scholarly publications.
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KEYWORDS
Image compression

Analytical research

Wavelets

Distortion

Image quality

Computer programming

Medical imaging

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