Quality adaptive least squares trained filters for video compression artifacts removal using a no-reference block visibility metric

Shao, Ling, Wang, Jingnan, Kirenko, Ihor and de Haan, Gerard (2011) Quality adaptive least squares trained filters for video compression artifacts removal using a no-reference block visibility metric. Journal of Visual Communication and Image Representation, 22 (1). pp. 23-32. ISSN 1047-3203

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Official URL: http://dx.doi.org/10.1016/j.jvcir.2010.09.007

Abstract

Compression artifacts removal is a challenging problem because videos can be compressed at different qualities. In this paper, a least squares approach that is self-adaptive to the visual quality of the input sequence is proposed. For compression artifacts, the visual quality of an image is measured by a no-reference block visibility metric. According to the blockiness visibility of an input image, an appropriate set of filter coefficients that are trained beforehand is selected for optimally removing coding artifacts and reconstructing object details. The performance of the proposed algorithm is evaluated on a variety of sequences compressed at different qualities in comparison to several other de-blocking techniques. The proposed method outperforms the others significantly both objectively and subjectively.

Item Type: Article
Uncontrolled Keywords: Compression artifacts removal; Adaptive filtering; Least squares filter; No-reference quality metric; Noise reduction; Image enhancement; Blocking artifact reduction; Picture quality improvement
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer Science and Digital Technologies
Depositing User: Paul Burns
Date Deposited: 15 Jun 2015 11:52
Last Modified: 03 Nov 2016 11:45
URI: http://nrl.northumbria.ac.uk/id/eprint/22894

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