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

Authors:
Ling Shao;Jingnan Wang;Ihor Kirenko;Gerard de Haan
Affiliations:
Department of Electronic & Electrical Engineering, The University of Sheffield, UK;EECS Department, Northwestern University, USA;Philips Research Laboratories, Eindhoven, The Netherlands;Philips Research Laboratories, Eindhoven, The Netherlands
Venue:
Journal of Visual Communication and Image Representation
Year:
2011

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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 de-blocking techniques. The proposed method outperforms the others significantly both objectively and subjectively.