Integrating spectral preprocessing, spatial subband decomposition and linear prediction to accomplish lossy ultraspectral image compression

We propose a new architecture to accomplish lossy ultraspectral data compression where we particularly focus on AIRS (Atmospheric Infrared Sounder) images. In general AIRS images are good candidates for compression as they include more than two thousand spectral bands that account for over 40MB of data per single data cube. In our proposed compression technique the input image is first preprocessed by means of spatial subband decomposition followed by a spectral band ordering stage which is applied in order to increase the correlation between contiguous spectral bands. The resulting image is segmented on a spectral band basis in such a way that spectral bands are scanned to generate a speech-like signal that exhibits a higher spectral interband than intraband correlation and therefore can be modeled as an AR (autoregressive) process. As final step the data is processed through a compression stage involving short and long term forward linear prediction that produces an error signal that is encoded using a CELP (Code Excited Linear Prediction) scheme. The forward linear prediction filter order and the resolution of the CELP codebooks are adjusted depending on the spatial subband that originates the signal being predicted. By manipulating several parameters of both the preprocessing and compression stages different rate-distortion curves are obtained and highly efficient compression is achieved.