Independent component analysis: algorithms and applications
Neural Networks
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We investigate the implications of a unified spatio-chromatic basis for image compression and reconstruction. Different adaptive and general methods (principal component analysis, PCA, independent component analysis, ICA, and discrete cosine transform, DCT) are applied to generate bases. While typically such bases with spatial extent are investigated in terms of their correspondence to human visual perception, we are interested in their applicability to multimedia encoding. The performance of the extracted spatio-chromatic spatial patch bases is evaluated in terms of quality of reconstruction with respect to their potential for data compression. Since ICA is not as widely used as it should be, compared to the other decorrelation methods applied here in a new domain, we also provide a review of ICA. The results discussed here are intended to provide another path towards perceptually based encoding of visual data. This leads to a deeper understanding of the role played by chromatic features in data reduction.