Lossy Compression via Sparse Regression Codes: Generalized Construction and Finite-length Bounds
TL;DR
This work generalizes the SPARC construction, and considers the class of \emph{additive orthogonal} regression codes, of which standard SPARCs are a special case, and derives nonasymptotic bounds on the squared-error distortion by tracking the evolution of the encoding residual across stages.
Abstract
We study sparse regression codes (SPARCs) for lossy compression under simple greedy encoding rules, including both correlation-based and distance-based methods. We generalize the SPARC construction, and consider the class of \emph{additive orthogonal} regression codes, of which standard SPARCs are a special case. For this class of codes, we derive nonasymptotic bounds on the squared-error distortion by tracking the evolution of the encoding residual across stages. Our results highlight the role of power allocation in controlling the distortion, allowing us to optimize the allocation based on the parameters of the code. The optimized allocation improves the finite-length compression performance of SPARCs, and our bounds provide distortion guarantees for lower complexity variants of SPARCs, like signed SPARCs and $K$-sparse SPARCs.