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Proceedings Paper

Near-lossless interframe image compression via wavelet transform and context modeling
Author(s): Paul Bao; Xiaolin Wu
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Paper Abstract

The near-lossless CALIC is one of the best near-lossless intraframe image coding schemes which exploits and removes the local context correlation of images. Wavelet transform localizes the frequency domain and exploits the frequency- based global correlation of images. Applying the context modeling for the wavelet transform coefficients, a state of the art intraframe near-lossless coding scheme can be obtained. In this paper, we generalize the intraframe wavelet transform CALIC to interframe coding to form a hybrid near-lossless multispectral image compression. Context modeling techniques lend themselves easily to modeling of image sequences. While wavelet transform exploits the global redundancies, the interframe context modeling can thoroughly exploit the statistical redundance is both between and within the frames. First, the image frame is wavelet transformed in the near-lossless mode to obtain a set of orthogonal subclasses of images. Then the coefficients of interframes of the image are predicted using the gradient-adjusted predictor based on both intra- and inter-frame current coefficient context. The predicted coefficients are adjusted predictor based on both intra- and inter-frame current coefficient context. The predicted coefficients are adjusted using the sample mean of prediction errors conditioned on the current context and the residues are quantized. An incremental scheme is used for the prediction errors in a moving time windows for prediction bias cancellation. All the components are distortion controlled in the minmax metric to ensure the near-lossless compression. The decompression is the inverse of the process. It is demonstrated that the near-lossless wavelet transform and context modeling interframe image compression is one of the best schemes in high-fidelity multispectral image compression and it outperforms its intraframe counterpart with 10-20 percent compression gains while keeping the high fidelity.

Paper Details

Date Published: 8 July 1998
PDF: 11 pages
Proc. SPIE 3389, Hybrid Image and Signal Processing VI, (8 July 1998); doi: 10.1117/12.316536
Show Author Affiliations
Paul Bao, Hong Kong Polytechnic Univ. (Hong Kong)
Xiaolin Wu, Univ. of Western Ontario (Canada)


Published in SPIE Proceedings Vol. 3389:
Hybrid Image and Signal Processing VI
David P. Casasent; Andrew G. Tescher, Editor(s)

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