Sparsity-based deartifacting filtering in video compression

Authors:
Jun Xu;Yunfei Zheng;Peng Yin;Joel Sole;Cristina Gomila;Dapeng Wu
Affiliations:
Thomson Corporate Research, Princeton, New Jersey and Dept. of Electrical & Computer Engineering, University of Florida, Gainesville, Florida;Thomson Corporate Research, Princeton, New Jersey;Thomson Corporate Research, Princeton, New Jersey;Thomson Corporate Research, Princeton, New Jersey;Thomson Corporate Research, Princeton, New Jersey;Dept. of Electrical & Computer Engineering, University of Florida, Gainesville, Florida
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 3
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Abstract

In the last years, many sparsity based denoising approaches for image/video denoising have been proposed. Most of them exploit the image/video sparsity model under certain overcomplete basis. In this paper, we unify three sparsity-based denoising techniques and apply them to the problem of video compression artifacts removal. We compare and analyze the three techniques from the aspects of operation atom, transform dimensionality, and quantization impact. Based on the provided analysis, the paper may serve as a guideline to apply sparsity-based denoising techniques to related problems.