Efficient Adaptive Algorithms and Minimax Bounds for Zero-Delay Lossy Source Coding.

Zero-delay lossy source coding schemes are considered for both individual sequences and random sources. Performance is measured by the distortion redundancy, which is defined as the difference between the normalized cumulative mean squared distortion of the scheme and the normalized cumulative distortion of the best scalar quantizer of the same rate that is matched to the entire sequence to be encoded. By improving and generalizing a scheme of Linder and Lugosi, Weissman and Merhav showed the existence of a randomized scheme that, for any bounded individual sequence of length n, achieves a distortion redundancy O(n-13/logn). However, both schemes have prohibitive complexity (both space and time), which makes practical implementation infeasible. In this paper, we present an algorithm that computes Weissman and Merhavʹs scheme efficiently. In particular, we introduce an algorithm with encoding complexity O(n43/) and distortion redundancy O(n-13/logn). The complexity can be made linear in the sequence length n at the price of increasing the distortion redundancy to O(n-14/√logn). We also consider the problem of minimax distortion redundancy in zero-delay lossy coding of random sources. By introducing a simplistic scheme and proving a lower bound, we show that for the class of bounded memoryless sources, the minimax expected distortion redundancy is upper and lower bounded by constant multiples of n-12/.