The vector quantization for AVIRIS hyperspectral imagery compression with fixed low bitrate

Jing Zhang; Yunsong Li; Keyan Wang; Haiying Liu

doi:10.1117/12.929506

19 October 2012 The vector quantization for AVIRIS hyperspectral imagery compression with fixed low bitrate

Jing Zhang, Yunsong Li, Keyan Wang, Haiying Liu

Proceedings Volume 8514, Satellite Data Compression, Communications, and Processing VIII; 85140W (2012) https://doi.org/10.1117/12.929506
Event: SPIE Optical Engineering + Applications, 2012, San Diego, California, United States

Abstract

Vector quantization is an optimal compression strategy for hyperspectral imagery, but it can’t satisfy the fixed bitrate application. In this paper, we propose a vector quantization algorithm for AVIRIS hyperspectral imagery compression with fixed low bitrate. The 2D-TCE lossless compression for codebook image and index image, the codebook reordering, the remove water absorbed band algorithm are introduced to the classical vector quantization, and the bitrate distribution is replaced by choosing the appropriate codebook size algorithm. Experimental results show that the proposed vector quantization has a better performance than the traditional hyperspectral imagery lossy compression with fixed low bitrate.

Citation Download Citation

Jing Zhang, Yunsong Li, Keyan Wang, and Haiying Liu "The vector quantization for AVIRIS hyperspectral imagery compression with fixed low bitrate", Proc. SPIE 8514, Satellite Data Compression, Communications, and Processing VIII, 85140W (19 October 2012); https://doi.org/10.1117/12.929506

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