Reduced-rank condensed filter dictionaries for inter-picture prediction

Abstract

We consider the motion-compensated temporal prediction loop at the heart of modern video coders. Rather than using motion-compensated reference frame blocks directly as predictors, we incorporate their spatially-filtered versions into the prediction loop. We design adaptive filters that are geared toward successful prediction over sophisticated temporal evolutions involving lighting changes, focus changes, structured noise, and so on. The spatially and temporally varying nature of such video evolutions requires the learning and transmission of many filters, necessitating parameter reduction for compression and related applications. Unlike earlier work that tries to limit parameters by using a small set of general filters, or by restricting to symmetric filters, etc., we propose a novel parametrization of filters in terms of a set of base-filter kernels and modulation weights. Given a filter dictionary of K-tap filters, our work can be seen as providing a reduced-rank, prediction-optimal approximation of this dictionary that represents its filters with K' ≪ K parameters.