Texture Segmentation Based Video Compression Using Convolutional Neural Networks

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 t...

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Bibliographic Details
Published in:Electronic Imaging 2018-01, Vol.30 (2), p.155-1-155-6
Main Authors: Fu, Chichen, Chen, Di, Delp, Edward, Liu, Zoe, Zhu, Fengqing
Format: Article
Language:English
Subjects:
Aom/av1
Coding Efficiency
Convolutional Neural Networks
Texture Segmentation
Video Coding
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Summary: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.
ISSN:2470-1173
2470-1173
DOI:10.2352/ISSN.2470-1173.2018.2.VIPC-155