Super-Resolved and Blurred Decoded Pictures for Improving Coding Efficiency in Inter-Frame Prediction

Inter-frame prediction in video coding can be difficult if an object is sharped or blurred between inter frames. We therefore propose a video coding method for inter-frame prediction using super-resolved and blurred decoded pictures. In the joint exploration model (JEM) of random-access mode, inter-frame prediction is performed by using previously decoded pictures. In the proposed method, super-resolved and blurred pictures of the previously decoded pictures are generated before the inter-frame prediction. The inter-frame prediction is performed by using a three-type picture, which consist of a previously decoded picture and its super-resolved and blurred pictures. A previously decoded picture with the lowest rate distortion (RD) cost is selected from these three- type pictures. In the experiment, the proposed method is implemented in the JEM 7.0. The experimental results show that the proposed method produces higher video quality than the conventional JEM 7.0 coder.