Mismatched codebooks and the role of entropy-coding in lossy data compression

TL;DR

This paper proposes a universal quantization scheme using mismatched codebooks and entropy-coding, analyzing its performance and universality, and connecting it to lattice quantizers with significant rate reductions.

Contribution

It introduces a universal quantization scheme with a single codebook and entropy-coding, providing a new analysis and demonstrating near-optimality across sources and distortion measures.

Findings

Entropy-coding can significantly reduce the rate, potentially arbitrarily large.

A universal scheme with mismatched codebooks is near-optimal for various sources.

Connection established between the scheme and entropy-coded lattice quantizers, extending the 'half-a-bit' bound.

Abstract

We introduce a universal quantization scheme based on random coding, and we analyze its performance. This scheme consists of a source-independent random codebook (typically_mismatched_ to the source distribution), followed by optimal entropy-coding that is_matched_ to the quantized codeword distribution. A single-letter formula is derived for the rate achieved by this scheme at a given distortion, in the limit of large codebook dimension. The rate reduction due to entropy-coding is quantified, and it is shown that it can be arbitrarily large. In the special case of "almost uniform" codebooks (e.g., an i.i.d. Gaussian codebook with large variance) and difference distortion measures, a novel connection is drawn between the compression achieved by the present scheme and the performance of "universal" entropy-coded dithered lattice quantizers. This connection generalizes the "half-a-bit" bound on the redundancy of dithered lattice quantizers. Moreover, it demonstrates a strong notion of universality where a single "almost uniform" codebook is near-optimal for_any_ source and_any_ difference distortion measure.