<title>JAVA implemented MSE optimal bit-rate allocation applied to 3-D hyperspectral imagery using JPEG2000 compression</title>

Abstract

This paper describes an efficient algorithm and its Java implementation for a recently developed mean-squared error (MSE) rate-distortion optimal (RDO) inter-slice bit-rate allocation (BRA) scheme applicable to the JPEG2000 Part 2 (J2KP2) framework. Its performance is illustrated on hyperspectral imagery data using the J2KP2 with the KarhunenLoeve transform (KLT) for decorrelation. The results are contrasted with those obtained using the traditional logvariance based BRA method and with the original RDO algorithm. The implementation has been developed as a Java plug-in to be incorporated into our evolving multi-dimensional data compression software tool denoted CompressMD. The RDO approach to BRA uses discrete rate distortion curves (RDCs) for each slice of transform coefficients. The generation of each point on a RDC requires a full decompression of that slice, therefore, the efficient version minimizes the number of RDC points needed from each slice by using a localized coarse-to-fine approach denoted RDOEfficient. The scheme is illustrated in detail using a subset of 10 bands of hyperspectral imagery data and is contrasted to the original RDO implementation and the traditional (log-variance) method of BRA showing that better results are obtained with the RDO methods. The three schemes are also tested on two hyperspectral imagery data sets with all bands present: the Cuprite radiance data from AVIRIS and a set derived from the Hyperion satellite. The results from the RDO and RDOEfficient are very close to each other in the MSE sense indicating that the adaptive approach can find almost the same BRA solution. Surprisingly, the traditional method also performs very close to the RDO methods, indicating that it is very close to being optimal for these types of data sets.

Assessment of inter-slice bit-allocation strategies in the application of JPEG2000 to ultraspectral sounder data

“…While lossless or near-lossless compression has been a major focus due to the need to preserve all fidelity, it is worthwhile to explore lossy compression techniques to evaluate the potential rate/distortion performance on ultraspectral data. Similar work has been previously done by our group for other kinds of data such as hyper-spectral imagery, modeled meteorological data and medical multi-slice scans [1][2][3][6][7][8][9]. In a similar manner, this study presents an assessment of inter-band bit-allocation strategies applied to AIRS data using a JPEG2000 compatible compression scheme for multicomponent imagery.…”