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Volume: 30 | Article ID: art00004
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Texture Segmentation Based Video Compression Using Convolutional Neural Networks
  DOI :  10.2352/ISSN.2470-1173.2018.2.VIPC-155  Published OnlineJanuary 2018
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 Delp, Zoe Liu, Fengqing Zhu, "Texture Segmentation Based Video Compression Using Convolutional Neural Networksin 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

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Electronic Imaging
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