Layered Coding of Hidden Markov Sources

TL;DR

This paper introduces a new optimal layered coding approach for hidden Markov sources, improving reconstruction quality and outperforming existing methods by leveraging state probability tracking and scalable coding techniques.

Contribution

It proposes a novel fundamental coding method for HMS based on state probability estimation and introduces scalable coding that utilizes all available information.

Findings

Significant reduction in reconstructed distortion.

Outperforms existing coding techniques.

Effective scalable coding for HMS.

Abstract

The paper studies optimal coding of hidden Markov sources (HMS), which represent a broad class of practical sources obtained through noisy acquisition processes, beside their explicit modeling use in speech processing and recognition, image understanding and sensor networks. A new fundamental source coding approach for HMS is proposed, based on tracking an estimate of the state probability distribution, and is shown to be optimal. Practical encoder and decoder schemes that leverage the main concepts are introduced. An iterative approach is developed for optimizing the system. It also focuses on a significant extension of the optimal HMS quantization paradigm. It proposes a new approach for scalable coding of HMS which accounts for all the available information while coding a given layer. Simulation results confirm that these approaches significantly reduce the reconstructed distortion and substantially outperform existing techniques.