There has been a growing interest in using different approaches to improve the coding efficiency of modern video codec in recent years as demand for web-based video consumption increases. In this paper, we propose a model-based approach that uses texture analysis/synthesis to reconstruct blocks in texture regions of a video to achieve potential coding gains using the AV1 codec developed by the Alliance for Open Media (AOM). The proposed method uses convolutional neural networks to extract texture regions in a frame, which are then reconstructed using a global motion model. Our preliminary results show an increase in coding efficiency while maintaining satisfactory visual quality.
@article{arxiv.1802.02992,
title = {Texture Segmentation Based Video Compression Using Convolutional Neural Networks},
author = {Chichen Fu and Di Chen and Edward J. Delp and Zoe Liu and Fengqing Zhu},
journal= {arXiv preprint arXiv:1802.02992},
year = {2018}
}