Improved entropy coding for component-based image coding

In this paper, we improve on our previous work regarding component-based image coding, a hybrid transform-based/perceptual image coding scheme based on a decomposition of the image into structure and texture characterized by a Gaussian Markov random field. The 2D Itakura distance allows us to evaluate the performance of our texture model in terms of rate vs. distortion. A minimal quantization step size for near-lossless coding of model parameters is determined. Furthermore, we show that texture contrast can be efficiently coded using transform-based techniques.