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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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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 deblocking techniques. The proposed method outperforms the others significantly both objectively and subjectively.

Item Type: Article
Copyright, Publisher and Additional Information: Copyright © 2010 Elsevier Inc. This is an author produced version of the published paper. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: Trained filters, compression artifacts removal, image enhancement, block visibility metric
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield)
Depositing User: Dr. Ling Shao
Date Deposited: 05 Apr 2011 11:37
Last Modified: 15 Sep 2014 01:24
Published Version: http://dx.doi.org/10.1016/j.jvcir.2010.09.007
Status: Published
Publisher: Elsevier
Refereed: Yes
Identification Number: 10.1016/j.jvcir.2010.09.007
Related URLs:
URI: http://eprints.whiterose.ac.uk/id/eprint/42895

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