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

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.