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Progressive vector quantization on a massively parallel SIMD machine with application to multispectral image data
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DOI：10.1109/83.481678.png)
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Abstract
En 中文
This correspondence discusses a progressive vector quantization (VQ) compression approach, which decomposes image data into a number of levels using full-search VQ. The final level is losslessly compressed, enabling lossless reconstruction. The computational difficulties are addressed by implementation on a massively parallel SIR ID machine, We demonstrate progressive VQ on multispectral imagery obtained from the advanced very high resolution radiometer (AVHRR) and other earth-observation image data, and investigate the tradeoffs in selecting the number of decomposition levels and codebook training method.
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