Multiple Description Image Coding Based on CompressionGuided Optimization In this paper, we design a new multiple description coding scheme for image signals based on our proposed compressionguided optimization. Firstly, we propose a compressionconstrained adaptive filtering method to produce two descriptions for the source image, where the proposed filtering algorithm works not only to guarantee a highquality side decoding but also make a highefficient central decoding. Secondly, we design a compressiondependent deblocking algorithm based on the transform coefficients which are decoded from both descriptions to improve the performance for the cental decoding. Experimental results demonstrate that our proposed method achieves impressive performance gains when it is applied to image signals.

A Total Variation Based Approach for Composing Better Pictures in Multiple Description Coding One of the important issues in multiple description coding (MDC) for image and video signals is to compose pictures of the best quality when more than one description is received at the decoder side. To this goal, a transformdomain overlapping technique combined with some necessary translations in the transform domain is proposed in the popular MDC schemes with staggered quantizers. However, the achievable gain is rather limited. In this paper, we formulate this enhancement problem as a total variation (TV) regularized optimization constrained by the knowledge of the quantization intervals of the DCT coefficients of each composed picture. In this TVbased approach, the transformdomain overlapping technique is used to find the more accurate quantization intervals in which the true DCT coefficients will fall while receiving more than one description. Simulation results demonstrate that such a TVbased enhancement yields a higher quality gain in both objective (e.g. PSNRbased) and subjective (i.e. visual perception) evaluations than using the transformdomain overlapping technique.