Adaptive coding of images via multiresolution ICA

Multiresolution (MR) representations have been very successful in image encoding, due to both their algorithmic performance and coding efficiency. However these transforms are fixed, suggesting that coding efficiency could be further improved if a multiresolution code could be adapted to a specific signal class. Among adaptive coding methods, independent component analysis (ICA) provides the best linear code by finding a linear transform with maximally independent coefficients, given a specific signal distribution. This technique, however, scales poorly with the dimensionality of the data, and has been ill-suited for large-scale image coding. We propose a hybrid method (multi-resolution ICA) which derives an ICA basis for each subband space produced by a given MR transform over the image class. We find that this method produces a significantly more efficient code compared to the MR transform alone. We provide both quantitative and qualitative assessments of coding performance, and illustrate improvement over standard (i.e., non-adaptive) wavelet-based representations such as that used in JPEG2000.