Texture Segmentation Based Video Compression Using Convolutional Neural Networks

TL;DR

This paper introduces a CNN-based texture analysis and synthesis method to enhance video compression efficiency with AV1, by reconstructing texture regions and maintaining visual quality.

Contribution

It presents a novel model-based approach leveraging CNNs for texture region detection and reconstruction to improve video coding efficiency.

Findings

Increased coding efficiency demonstrated

Maintained visual quality in reconstructed videos

Effective texture region extraction using CNNs

Abstract

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.