A recursive extrapolation approach to intra prediction in video coding

A novel intra prediction scheme, based on recursive extrapolation filters, is introduced. Standard intra prediction largely consists of copying boundary pixels (or linear combinations thereof) along certain directions, which reflects an overly simplistic model for the underlying spatial correlations. As an alternative, we view the image signal as a 2-D non-separable Markov model, whose corresponding correlation model better captures the nuanced directionality effects within blocks. This viewpoint motivates the design of a set of prediction modes represented by three-tap extrapolation filters, which replace the standard “pixel-copying” prediction modes, and provide efficient prediction at modest complexity. The Markov property is exploited by recursive predictions from nearest neighbors without recourse to simplistic separability assumptions, and while effectively accounting for correlation decay with distance from available boundary pixels. Coefficients for the set of mode filters are first trained by an efficient “k-modes” iterative technique designed to monotonically decrease the mean squared prediction error, and are then adjusted to directly optimize the overall rate-distortion objective. This prediction scheme complements the hybrid (cosine and sine) transform coding approach developed by our group, to achieve consistent coding gains, as shown for standard and commercial intra coders such as H.264/AVC and VP8.