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Lossy Source Coding via Markov Chain Monte Carlo

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2 Author(s)
Jalali, S. ; Dept. of Electr. Eng., Stanford Univ., Stanford, CA ; Weissman, T.

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 x circn = xn, and then at each iteration uniformly at random chooses one of the symbols of x circn, 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(log n) representing context length, and the original source sequence. At the end of this process, the encoder outputs the Lempel-Ziv description of x circn, 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.

Published in:

Communications, 2008 IEEE International Zurich Seminar on

Date of Conference:

12-14 March 2008