Enhanced Bi-Prediction With Convolutional Neural Network for High-Efficiency Video Coding

In this paper, we propose an enhanced bi-prediction scheme based on the convolutional neural network (CNN) to improve the rate-distortion performance in video compression. In contrast to the traditional bi-prediction strategy which computes the linear superposition as the predictive signals with pixel-to-pixel correspondence, the proposed scheme employs CNN to directly infer the predictive signals in a data-driven manner. As such, the predicted blocks are fused in a nonlinear fashion to improve the coding performance. Moreover, the patch-to-patch inference strategy with CNN also improves the prediction accuracy since the patch-level information for the prediction of each individual pixel can be exploited. The proposed enhanced bi-prediction scheme is further incorporated into the high-efficiency video coding standard, and the experimental results exhibit a significant performance improvement under different coding configurations.