Transform-domain temporal prediction in video coding with spatially adaptive spectral correlations
Temporal prediction in standard video coding is performed in the spatial domain, where each pixel block is predicted from a motion-compensated pixel block in a previously reconstructed frame. Such prediction treats each pixel independently and ignores underlying spatial correlations. In contrast, this paper proposes a paradigm for motion-compensated prediction in the transform domain, that eliminates much of the spatial correlation before individual frequency components along a motion trajectory are independently predicted. The proposed scheme exploits the true temporal correlations, that emerge only after signal decomposition, and vary considerably from low to high frequency. The scheme spatially and temporally adapts to the evolving source statistics via a recursive procedure to obtain the cross-correlation between transform coefficients on the same motion trajectory. This recursion involves already reconstructed data and precludes the need for any additional side-information in the bit-stream. Experiments demonstrate substantial performance gains in comparison with the standard codec that employs conventional pixel domain motion-compensated prediction.