Image compression with embedded wavelet coding via vector quantization

Ioannis Katsavounidis; C.-C. Jay Kuo

doi:10.1117/12.217588

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1 September 1995 Image compression with embedded wavelet coding via vector quantization

Ioannis Katsavounidis, C.-C. Jay Kuo

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Proceedings Volume 2569, Wavelet Applications in Signal and Image Processing III; (1995) https://doi.org/10.1117/12.217588
Event: SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, 1995, San Diego, CA, United States

Abstract

In this research, we improve Shapiro's EZW algorithm by performing the vector quantization (VQ) of the wavelet transform coefficients. The proposed VQ scheme uses different vector dimensions for different wavelet subbands and also different codebook sizes so that more bits are assigned to those subbands that have more energy. Another feature is that the vector codebooks used are tree-structured to maintain the embedding property. Finally, the energy of these vectors is used as a prediction parameter between different scales to improve the performance. We investigate the performance of the proposed method together with the 7 - 9 tap bi-orthogonal wavelet basis, and look into ways to incorporate loseless compression techniques.

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Ioannis Katsavounidis and C.-C. Jay Kuo "Image compression with embedded wavelet coding via vector quantization", Proc. SPIE 2569, Wavelet Applications in Signal and Image Processing III, (1 September 1995); https://doi.org/10.1117/12.217588

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