Vector quantization and signal compression
Vector quantization and signal compression
Elements of information theory
Elements of information theory
Arithmetic coding for data compression
Communications of the ACM
Natural gradient works efficiently in learning
Neural Computation
DCC '00 Proceedings of the Conference on Data Compression
Fast algorithms for mutual information based independent component analysis
IEEE Transactions on Signal Processing - Part I
Analysis of low bit rate image transform coding
IEEE Transactions on Signal Processing
A theoretical high-rate analysis of causal versus unitary online transform coding
IEEE Transactions on Signal Processing
Transform coding with backward adaptive updates
IEEE Transactions on Information Theory
Suboptimality of the Karhunen-Loeve transform for transform coding
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
IEEE Transactions on Image Processing
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The Karhunen-Loeve Transform (KLT) is optimal for transform coding of Gaussian sources, however, it is not optimal, in general, for non-Gaussian sources. Furthermore, under the high-resolution quantization hypothesis, nearly everything is known about the performance of a transform coding system with entropy constrained scalar quantization and mean-square distortion. It is then straightforward to find a criterion that, when minimized, gives the optimal linear transform under the abovementioned conditions. However, the optimal transform computation is generally considered as a difficult task and the Gaussian assumption is then used in order to simplify the calculus. In this paper, we present the abovementioned criterion as a contrast of independent component analysis modified by an additional term which is a penalty to non-orthogonality. Then we adapt the icainf algorithm by Pham in order to compute the transform minimizing the criterion either with no constraint or with the orthogonality constraint. Finally, experimental results show that the transforms we introduced can (1) outperform the KLT on synthetic signals, (2) achieve slightly better PSNR for high-rates and better visual quality (preservation of lines and contours) for medium-to-low rates than the KLT and 2-D DCT on grayscale natural images.