Repairing imperfect video enhancement algorithms using classification-based trained filters

Shao, Ling, Zhang, Hui, Wang, Liang and Wang, Lijun (2011) Repairing imperfect video enhancement algorithms using classification-based trained filters. Signal, Image and Video Processing, 5 (3). pp. 307-313. ISSN 1863-1703

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Abstract

There are numerous video processing algorithms and modules available. When the algorithms are not optimally tuned, undesired results may happen in the processed video signals, e.g. blurring, overshoots/downshoots, loss of details and aliasing. When the video processing modules are fixed, e.g. when the modules are implemented in hardware/chips, it is highly desirable to repair those unpleasant effects caused by certain imperfect algorithms. In this paper, we propose a solution based on classification and least squares trained filters to repair/patch low-quality video processing modules at the back end of a video chain. Extensive experiments show that the repairing method can significantly improve the video quality without modifying the original processing modules.

Item Type:	Article
Uncontrolled Keywords:	Trained filters, Video enhancement, Compression artefacts removal, De-blurring, Resolution up-conversion, Classification, Least squares optimisation
Subjects:	G400 Computer Science
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Paul Burns
Date Deposited:	10 Jun 2015 15:54
Last Modified:	12 Oct 2019 22:30
URI:	http://nrl.northumbria.ac.uk/id/eprint/22852

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