An Extended Skip Strategy for Inter Prediction

The High Efficiency Video Coding (HEVC) standard adopts inter prediction to eliminate temporal correlation between the successive frames. However, a large amount of bits need to be explicitly signaled in the bitstream to specify the motion information. In this paper, we propose an extended skip strategy to alleviate bit consumption for motion data during the inter prediction process. Specifically, before the current frame is encoded, an additional picture generated by a deep convolutional neural network (CNN) is introduced to inter prediction. Since the additional reference picture is more similar with the current frame, most blocks of this frame can be skipped in the coding process. Consequently, to further improve the compression, an extended skip strategy is designed, i.e., the current frame can be skipped in multi-levels, including frame-level and coding tree unit level (CTU-level). Moreover, the skip-level of the current frame is decided in the sense of rate-distortion optimization (RDO). The proposed algorithm is implemented on the HM-16.6 software and an average of 4.4% BD-rate gain has been achieved in the experiments, which indicates the superiority of the proposed method.