LAUSR

This work demonstrates a formal connection between density estimation with a data-rate constraint and the joint objective of fixed-rate universal lossy source coding and model identification introduced by Raginsky in 2008 (IEEE TIT, 2008, 54, 3059–3077). Using an equivalent learning formulation, we derive a necessary and sufficient condition over the class of densities for the achievability of the joint objective. The learning framework used here is the skeleton estimator, a rate-constrained learning scheme that offers achievable results for the joint coding and modeling problem by optimally adapting its learning parameters to the specific conditions of the problem. The results obtained with the skeleton estimator significantly extend the context where universal lossy source coding and model identification can be achieved, allowing for applications that move from the known case of parametric collection of densities with some smoothness and learnability conditions to the rich family of non-parametric L1-totally bounded densities. In addition, in the parametric case we are able to remove one of the assumptions that constrain the applicability of the original result obtaining similar performances in terms of the distortion redundancy and per-letter rate overhead.