Adaptive VQ-based linear prediction for lossless compression of ultraspectral sounder data

Bormin Huang; Alok Ahuja; Mitchell D. Goldberg

doi:10.1117/12.683967

1 September 2006 Adaptive VQ-based linear prediction for lossless compression of ultraspectral sounder data

Bormin Huang, Alok Ahuja, Mitchell D. Goldberg

Proceedings Volume 6300, Satellite Data Compression, Communications, and Archiving II; 630002 (2006) https://doi.org/10.1117/12.683967
Event: SPIE Optics + Photonics, 2006, San Diego, California, United States

Abstract

Contemporary and future ultraspectral sounders represent a significant technical advancement for environmental and meteorological prediction and monitoring. Given their large volume of spectral observations, the use of robust data compression techniques will be beneficial to data transmission and storage. In this paper, we propose a novel Adaptive Vector Quantization (VQ)-based Linear Prediction (AVQLP) method for ultraspectral data compression. The method is compared with several state-of-the-art methods such as CALIC, JPEG-LS and JPEG2000. The compression experiments show that our AVQLP method is the first to surpass the 4 to 1 lossless compression barrier for a selected set of AIRS ultraspectral sounder test data.

Citation Download Citation

Bormin Huang, Alok Ahuja, and Mitchell D. Goldberg "Adaptive VQ-based linear prediction for lossless compression of ultraspectral sounder data", Proc. SPIE 6300, Satellite Data Compression, Communications, and Archiving II, 630002 (1 September 2006); https://doi.org/10.1117/12.683967

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