Inter Prediction Based on Low-rank Matrix Completion

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.