Efficient wavelet-based predictive Slepian-Wolf coding for hyperspectral imagery
Signal Processing - Special section: Distributed source coding
Distributed source coding techniques for lossless compression of hyperspectral images
EURASIP Journal on Applied Signal Processing
Spatial-aided low-delay Wyner-Ziv video coding
Journal on Image and Video Processing - Special issue on distributed video coding
Hi-index | 0.00 |
Hyperspectral images have correlation at the level of pixels; moreover, images from neighboring frequency bands are also closely correlated. In this paper, we propose to use distributed source coding to exploit this correlation with an eye to a more efficient hardware implementation. Slepian-Wolf and Wyner-Ziv based correlated coding theorems have quantified how much additional rate reduction can be obtained. In order to better exploit these correlations, we first propose a prediction model to align images. This model is based on linear prediction techniques and it is simple and shown to be effective for hyperspectral images. We then propose a coding scheme to exploit these correlations. A set-partitioning approach is used on wavelet transformed data to extract bitplanes. Under our correlation model, bitplanes from neighboring bands are correlated and we then use a low-density parity-check based Slepian-Wolf code to exploit this bitplane level correlation. This scheme is appealing for hardware implementation as it is easy to parallelize and it has modest memory requirements. As for coding performance, our preliminary results for high correlation spectral bands from the NASA AVIRIS dataset show, at medium to high reconstructed qualities, gains of about a factor of 3 in compression efficiency as compared to encoding the spectral bands independently using SPIHT.