Deformable Convolution Network based Invertibility-Driven Interpolation Filter for HEVC
Qiuyang Zhang, Xiaofeng Huang, Haibing Yin, Weihong Niu · 2021
Fractional-position motion compensation has been widely utilized in video coding standard to improve the inter prediction efficiency. In this paper, we study the three key components of the state-of-the-art method––Invertibility-driven Interpolation Filter (InvIF) and improve each of them to derive an Enhanced InvIF (EInvIF). Firstly, the deformable convolution layer is introduced to make the network's filters have the ability to change its shape and parameters to adapt to the video contents. Secondly, the generative adversarial network is utilized to increase the deep learning models' ability of approximating target distribution. Finally, the motion blur images are adopted as the regularization target instead of discrete cosine transform images. The proposed EInvIF has been integrated into HM-16.7, and the experimental results show that the proposed scheme can achieve 2.5% bitrate reduction on average.