Mixture Model- and Least Squares-Based Packet Video Error

A Gaussian mixture model (GMM)-based spatio-temporal error concealmentapproach has recently been proposed for packet video. The method improvespeak signal-to-noise ratio (PSNR) comparedto several famous error concealment methods, and itis asymptotically optimal when the number of mixture components goes toinfinity.There are also drawbacks however. The estimator has high onlinecomputational complexity,which implies that fewer surrounding pixels to the lost area than desiredare used forerror concealment. Moreover, GMM parameters are estimated withoutconsidering maximization of theerror concealment PSNR.In this paper we propose a mixture-based estimator and a least squaresapproach for solving the spatio-temporal error concealment problem.Compared to the GMM scheme, the new method may base error concealment onmore surrounding pixels to the loss, while maintaining low computationalcomplexity, and model parameters are foundby an algorithm that increases PSNR in each iteration.The proposed method outperforms the GMM-based scheme in terms ofcomputation-performance tradeoff