Principal Component Analysis in Multiple Description Coding of Spectral Images
Arto Kaarna, Andrey Norkin, Jaakko T. Astola · 2008
Communications in general require protection due to error-prone channels. In geoscience and remote sensing, especially coded or compressed data, and results from classifications are vulnerable to transmissions errors. Multiple descriptions of data are one way for protection of communications over unreliable channels. This study concentrates on multiple description of spectral images as a way for providing scalable coding. The principal component analysis outputs the common content, the redundant part, for the two descriptions and then the integer wavelet transform selects different contents for those descriptions. In the experiments, the goal was to find good parameterization in the transmitter for generating the two descriptions which will allow perfect reconstruction if both of them are available at the receiver. The reconstruction quality for various number of principal components is demonstrated. Integer wavelet filter 5/3 showed the best performance among the implemented filters.