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Quality Adaptive Least Squares Trained Filters for Video Compression Artifacts Removal Using a No-reference Block Visibility Metric

By Ling Shao, Jingnan Wang, Ihor Kirenko and Gerard de Haan


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

Publisher: Elsevier
Year: 2011
OAI identifier:

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