Entropy Coding via Parametric Source Model with Applications in Fast and Efficient Compression of Image and Video Data
In this paper a framework is proposed for efficient entropy coding of data which can be represented by a parametric distribution model. Based on the proposed framework, an entropy coder achieves coding efficiency by estimating the parameters of the statistical model (for the coded data), either via Maximum A Posteriori (MAP) or Maximum Likelihood (ML) parameter estimation techniques.