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arXiv · 1802.02992

Texture Segmentation Based Video Compression Using Convolutional Neural Networks

Chichen Fu·Di Chen·Edward J. Delp·Zoe Liu·Fengqing Zhu
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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.

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Chichen Fu, Di Chen, Edward J. Delp, Zoe Liu, Fengqing Zhu. 2018-02-08. Texture Segmentation Based Video Compression Using Convolutional Neural Networks. https://arxiv.org/abs/1802.02992

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