Gaussian mixture models are among the most widely accepted methods for clustering and probability density estimation. Recently it has been shown that these statistical methods are perfectly suited for learning patch-based image priors for various image restoration problems. In this paper we investigate the use of GMM's for image compression. A piecewise linear transform coding scheme based on Vector Quantization is proposed. In this scheme two different learning algorithms for GMM's are considered and compared. Experimental results demonstrate that the proposed techniques outperform JPEG, with results comparable to JPEG2000 for a broad class of images.
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