Advanced CNN Based Motion Compensation Fractional Interpolation

Abstract

Fractional-sample precision motion compensation has been widely adopted in a series of video coding standards to further improve compression efficiency. Usually, signal decomposition based interpolation filters are used to generate fractional samples from integer pixels. However, the coefficients of these finite impluse response filters may not be suitable for varied video contents and coding conditions because of the assumption when designing these filters. In this paper, we regard the fractional interpolation process as an image generation task, which utilizes the real interger position samples at the reference block to predict and generate fractional samples that are much closer to current coding block. We use the con-volutional neural netwok (CNN) as the generator. Moreover, to make the best of CNN’s powerful nonlinear learning ability, instead of inputting the reference block directly, we separately input the corresponding prediction and residual parts of reference block. The proposed dual-input CNN-based interpolation scheme has been incorporated into the HEVC framework and experimental results demonstrate our approach achieves average 0.9% bitrate reduction.

Publication
2019 IEEE International Conference on Image Processing (ICIP)
Han Zhang
Han Zhang
PhD Student

I received the B.S. degree in University of Electronic Science and Technology of China, in 2014. And I am currently pursuing the Ph.D. degree in Electronic Engineering with Shanghai Jiao Tong University (SJTU).

Li Song
Li Song
Professor, IEEE Senior Member

Professor, Doctoral Supervisor, the Deputy Director of the Institute of Image Communication and Network Engineering of Shanghai Jiao Tong University, the Double-Appointed Professor of the Institute of Artificial Intelligence and the Collaborative Innovation Center of Future Media Network, the Deputy Secretary-General of the China Video User Experience Alliance and head of the standards group.