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A joint spatio-temporal filtering approach to efficient prediction in video compression

A novel filtering approach that naturally combines information from both intra-frame and motion compensated referencing for efficient prediction is proposed to fully exploit the spatio-temporal correlations of video signals, thereby achieving superior compression performance. Inspiration was drawn from our recent work on extrapolation filter based intra prediction, which views the spatial signal as a non-separable first-order Markov process and employs a 3-tap recursive filter to effectively capture the statistical characteristics. This work significantly extends the scope to further incorporate motion compensated reference in a filtering framework, whose coefficients were optimized via a “k-modes”-like iteration that accounts for various factors in the compression process including variation in statistics in the prediction loop, to minimize the rate-distortion cost. Experiments validate the efficacy of the proposed spatio-temporal approach, which translates into consistent coding performance gains.