Wavelet image compression using IIR minimum variance filters, partition priority, and multiple distribution entropy coding

Dimitrios Tzovaras; Serafim N. Efstratiadis; Michael G. Strintzis

doi:10.1117/12.185991

16 September 1994 Wavelet image compression using IIR minimum variance filters, partition priority, and multiple distribution entropy coding

Dimitrios Tzovaras, Serafim N. Efstratiadis, Michael G. Strintzis

Author Affiliations +

Proceedings Volume 2308, Visual Communications and Image Processing '94; (1994) https://doi.org/10.1117/12.185991
Event: Visual Communications and Image Processing '94, 1994, Chicago, IL, United States

Abstract

Image compression methods for progressive transmission using optimal subband/wavelet decomposition, partition priority coding (PPC) and multiple distribution entropy coding (MDEC) are presented. In the proposed coder, hierarchical wavelet decomposition of the original image is achieved using wavelets generated by IIR minimum variance filters. The smoothed subband coefficients are coded by an efficient triple state DPCM coder and the corresponding prediction error is Lloyd-Max quantized. The detail coefficients are coded using a novel hierarchical PPC (HPPC) approach. That is, given a suitable partitioning of their absolute range, the detail coefficients are ordered based on their decomposition level and magnitude, and the address map is appropriately coded. Finally, adaptive MDEC is applied to both the DPCM and HPPC outputs by considering a division of the source of the quantized coefficients into multiple subsources and adaptive arithmetic coding based on their corresponding histograms.

Citation Download Citation

Dimitrios Tzovaras, Serafim N. Efstratiadis, and Michael G. Strintzis "Wavelet image compression using IIR minimum variance filters, partition priority, and multiple distribution entropy coding", Proc. SPIE 2308, Visual Communications and Image Processing '94, (16 September 1994); https://doi.org/10.1117/12.185991

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