A new approach to multichannel image compression is introduced where the intra- and cross-band correlations are jointly exploited in a surprisingly simple yet very effective manner. The key component of the algorithm is a bijection mapping of the original multichannel image into a virtual two-dimensional (2-D) scalar image. By optimally mapping the multichannel image set into a 2-D array and by subsequently applying a scalar image coding algorithm, the spatial correlation and the spectral correlation of the multichannel data set are jointly exploited. Based on the statistical characteristics of the multichannel data, the bijection mapping can be optimized to minimize the distortion introduced by the compression algorithm. The optimization reduces to the maximization of a function of the second-order statistics of the multichannel data. At high compression rates, the new algorithm outperforms traditional compression algorithms whenever the cross-band correlation is high and it yields comparable performance at low compression rates.

A new approach to multispectral image compression is introduced where the intra- and cross-band correlations are jointly exploited in a surprisingly simple yet very effective manner. The proposed compression algorithm maps the multispectral image set into a virtual 2-dimensional array and applies a scalar image coding algorithm to the virtual array. Thus, the spatial correlation and the spectral correlation of the multispectral data set are jointly exploited. Based on the statistical characteristics of the multispectral data, the bijection mapping is optimized to minimize the distortion introduced by the compression algorithm. At high compression rates, the new algorithm outperforms traditional compression algorithms whenever the cross-band correlation is high and it yields comparable performance at low compression rates.