We propose a framework for multiple description coding (MDC) of sources with memory. A new source coding method for lossless transmission of correlated sources, power series quantization (PSQ), was recently suggested. PSQ uses a separate linear or non-linear predictor for each quantizer region, and has shown increased performance compared toseveral common quantization schemes for sources with memory. We propose multiple description PSQ as a special case within our framework. The suggested scheme is shown to increase performance compared with previous state-of-the-art MDC methods.

In this paper, Gaussian mixture modeling is applied toerror concealment for block-based packet video. A Gaussian mixture model for video data is obtained offline and is thereafter utilized online in order to restore lost blocks from spatial and temporal surrounding information. We propose estimators on closed form for missing data in the case of varying available neighboring contexts. Our error concealment strategy increases peak signal-to-noise ratio compared to previously proposed schemes. Examples of improved subjective visual quality by means of the proposed method are also supplied.

We consider soft MIMO detection for the case of block fading. That is, the transmitted codeword spans over several independent channel realizations and several instances of the detection problem must be solved for each such realization. We develop methods that adaptively allocate the computational resources to the detection problems of each channel realization, under a total per-codeword complexity constraint. Our main results are a formulation of the problem as a mathematical optimization problem and a greedy algorithm to approximate it in a computationally feasible fashion.

In this paper, a spatio-temporal Markov random field method is proposed for block-based packet video error concealment. We suggest the combined usage of two estimators, one for lost pixels, and one for lost motion vectors. The estimator for the lost pixel field takes surrounding pixels in the same frame where the loss occurred and motion-compensated pixels from a previous frame based on a motion field estimate into account, while the optimal estimator of the motion field takes surrounding pixels in the same frame where the loss occurred, pixels from a previous frame, and the estimator function for the pixel field into account. Our method increases performance in peak signal-to-noise ratio as well as subjective visual performance compared to several other previous error concealment algorithms.

We have developed a statistical prediction technique to compensate for lost pixel blocks during real time transmission of video over packet-switched networks. The approach could as well be used for standard inter-frame coding. The method is based on the joint modeling of pixel statistics by Gaussian mixtures. Different EM variants for decreasing computational complexity and storage space are proposed. In the case of 4×4 luminance blocks, ourmethod increases performance by 2.3 dB when augmenting the number of mixture components from 1 to 64. We show that these results are statistically significant.