Block adaptive CNN/HEVC interframe prediction for video coding
Satoru Jimbo, Ji Wang, Yoshiyuki Yashima · International Workshop on Advanced Image Technology (IWAIT) 2019 · 2019
This paper proposes a block adaptive CNN/HEVC prediction method for bidirectional motion compensated prediction which is one of the key technologies for video coding. We have already proposed a deep convolutional neural network (CNN) model which can predict the target block to be predicted from its spatially co-located blocks existed in the temporally previous and future frames. The CNN model estimates four geometric transformation matrices, and the predicted values are produced by transforming both previous block and future block using them. We have demonstrated that the method is greatly effective for frames with complicated motion. However, the conventional CNN model often did not work well for the frames with large motion. In this paper, we introduce an adaptation method which selects the one of CNN based prediction and HEVC based prediction. Experimental results show that the prediction error using the proposed method can be reduced to 60% to 90% for many kinds of videos with complex motions compared to only HEVC prediction.