iCNN: A Convolutional Neural Network for Fractional Interpolation in Video Coding

EasyChair Preprint 1398, version 1

Abstract

Motion compensated prediction has significantly contributed to the temporal redundancy in video coding by predicting the current frame from the list of previously reconstructed frames. Later video coding standard HEVC uses DCTIF to interpolate fractional pixels for more accurate motion compensated prediction. Although the fixed interpolation filters have been improved, they are not able to adapt to the diversity of video content. Inspired by super-resolution, we design the interpolation Convolutional Neural Network for fractional interpolation in video coding. Our work also solves two main problems in applying Convolutional Neural Network to fractional interpolation in video coding: there is no training set for fractional interpolation and integer pixels change after processing. As a result, this work achieves a 2.6% BD-rate reduction compared to the baseline HEVC.

Keyphrases: Fractional Interpolation, Motion Compensated Prediction., Motion compensated prediction, deep learning, video coding

@booklet{EasyChair:1398,
  author    = {Chi Do-Kim Pham and Jinjia Zhou},
  title     = {iCNN: A Convolutional Neural Network for Fractional Interpolation in Video Coding},
  howpublished = {EasyChair Preprint 1398},
  year      = {EasyChair, 2019}}