Inter Prediction Based on Low-rank Matrix Completion

Abstract

This paper proposes a new method of inter prediction based on low-rank matrix completion. By collection and rearrangement, image regions with high correlations can be used to generate a low-rank or approximately low-rank matrix. We view prediction values as the missing part in an incomplete low-rank matrix, and obtain the prediction by recovering the generated low-rank matrix. Taking advantage of exact recovery of incomplete matrix, the low-rank based prediction can exploit temporal correlation better. Our proposed prediction has the advantage of higher accuracy and less extra information, as the motion vector doesn't need to be encoded. Simulation results show that the bit-rate saving of the proposed scheme can reach up to 9.91% compared with H.264/AVC. Our scheme also outperforms the counterpart of the Template Matching Averaging (TMA) prediction by 8.06% at most.