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A machine-learning-based post filtering method utilizing block boundary information in HEVC
Author(s): Yuya Yamaki; Yusuke Kameda; Ichiro Matsuda; Susumu Itoh
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Paper Abstract

We previously proposed a machine learning based post filtering method for reducing image artifacts caused by lossy compression. The method classifies reconstructed image samples into three categories using a support vector machine (SVM) to roughly discriminate magnitude of the reconstruction errors. Then, an optimum offset value is added to the samples belonging to each category in a similar way to the post filtering technique called sample adaptive offset (SAO) used in the H.265/HEVC standard. In this paper, two kinds of SVM classifiers are adaptively switched according to information on block boundaries of transform units (TUs) in H.265/HEVC intra-frame coding. Furthermore, samples used for a feature vector, which will be fed to the SVM classifier, are rotated at the block boundary to properly capture local characteristics of the reconstruction errors.

Paper Details

Date Published: 22 March 2019
PDF: 5 pages
Proc. SPIE 11049, International Workshop on Advanced Image Technology (IWAIT) 2019, 1104932 (22 March 2019); doi: 10.1117/12.2521534
Show Author Affiliations
Yuya Yamaki, Tokyo Univ. of Science (Japan)
Yusuke Kameda, Tokyo Univ. of Science (Japan)
Ichiro Matsuda, Tokyo Univ. of Science (Japan)
Susumu Itoh, Tokyo Univ. of Science (Japan)


Published in SPIE Proceedings Vol. 11049:
International Workshop on Advanced Image Technology (IWAIT) 2019
Qian Kemao; Kazuya Hayase; Phooi Yee Lau; Wen-Nung Lie; Yung-Lyul Lee; Sanun Srisuk; Lu Yu, Editor(s)

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