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.
Chichen Fu, Di Chen, Edward Delp, Zoe Liu, Fengqing Zhu, "Texture Segmentation Based Video Compression Using Convolutional Neural Networks" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Visual Information Processing and Communication IX, 2018, pp 155-1 - 155-6, https://doi.org/10.2352/ISSN.2470-1173.2018.2.VIPC-155