Optimal parameter estimation for model-based quantization

We address optimal model estimation for model-based vector quan-tization for both the constrained resolution (CR) and constrained en-tropy (CE) cases. To this purpose we derive under high-rate (HR) theory assumptions the rate-distortion (RD) relations for these two quantization scenarios assuming a Gaussian model. Based on the RD relations we show that the maximum likelihood (ML) criterion leads to optimal performance for CE quantization, but not for CR quantization. We introduce a new model estimation criterion for CR quantization that is optimal (under HR theory assumptions) in terms of the RD relation. Our experiments confirm that the proposed cri-terion for model identification outperforms the ML criterion for a range of conditions. Index Terms — Constrained resolution, model-based quantiza-tion, model estimation, rate-distortion relation, high-rate theory