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Near optimal lossy source coding and compression-based denoising via Markov chain Monte Carlo

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2 Author(s)
Shirin Jalali ; Department of Electrical Engineering, Stanford University, CA 94305, USA ; Tsachy Weissman

We propose an implementable new universal lossy source coding algorithm. The new algorithm utilizes two well- known tools from statistical physics and computer science: Gibbs sampling and simulated annealing. In order to code a source sequence xn, the encoder initializes the reconstruction block as xn = xn, and then at each iteration uniformly at random chooses one of the symbols of xn, and updates it. This updating is based on some conditional probability distribution which depends on a parameter beta representing inverse temperature, an integer parameter k = o(logn) representing context length, and the original source sequence. At the end of this process, the encoder outputs the Lempel-Ziv description of xn, which the decoder deciphers perfectly, and sets as its reconstruction. The complexity of the proposed algorithm in each iteration is linear in k and independent of n. We prove that, for any stationary ergodic source, the algorithm achieves the optimal rate-distortion performance asymptotically in the limits of large number of iterations, beta, and n. We also show how our approach carries over to such problems as universal Wyner-Ziv coding and compression-based denoising.

Published in:

Information Sciences and Systems, 2008. CISS 2008. 42nd Annual Conference on

Date of Conference:

19-21 March 2008