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Fully Connected Network-Based Intra Prediction for Image Coding | IEEE Journals & Magazine | IEEE Xplore

Fully Connected Network-Based Intra Prediction for Image Coding


Abstract:

This paper proposes a deep learning method for intra prediction. Different from traditional methods utilizing some fixed rules, we propose using a fully connected network...Show More

Abstract:

This paper proposes a deep learning method for intra prediction. Different from traditional methods utilizing some fixed rules, we propose using a fully connected network to learn an end-to-end mapping from neighboring reconstructed pixels to the current block. In the proposed method, the network is fed by multiple reference lines. Compared with traditional single line-based methods, more contextual information of the current block is utilized. For this reason, the proposed network has the potential to generate better prediction. In addition, the proposed network has good generalization ability on different bitrate settings. The model trained from a specified bitrate setting also works well on other bitrate settings. Experimental results demonstrate the effectiveness of the proposed method. When compared with high efficiency video coding reference software HM-16.9, our network can achieve an average of 3.4% bitrate saving. In particular, the average result of 4K sequences is 4.5% bitrate saving, where the maximum one is 7.4%.
Published in: IEEE Transactions on Image Processing ( Volume: 27, Issue: 7, July 2018)
Page(s): 3236 - 3247
Date of Publication: 19 March 2018

ISSN Information:

PubMed ID: 29641403

Funding Agency:


References

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