Generalized competitive learning of Gaussian mixture models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Mixture model-and least squares-based packet video error concealment
IEEE Transactions on Image Processing
On predictive coding for erasure channels using a Kalman framework
IEEE Transactions on Signal Processing
Gaussian mixture learning via robust competitive agglomeration
Pattern Recognition Letters
XOR-based frame loss recovery scheme for video streaming
Computer Communications
Pattern Recognition
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In this paper, Gaussian mixture modeling is applied to error 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.