Summary
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Blocking Artifact Reduction in Block-Coded Image Using Block Classification and Feedforward Neural Network Kee-Koo KWON Suk-Hwan LEE Seong-Geun KWON Kyung-Nam PARK Kuhn-Il LEE Publication IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences Vol.E85-A No.7 pp.1742-1745 Publication Date: 2002/07/01 Online ISSN: DOI: Print ISSN: 0916-8508 Type of Manuscript: LETTER Category: Digital Signal Processing Keyword: blocking artifact, neural network, block classification, adaptive filtering, Full Text: PDF(612KB)>>
Summary: A new blocking artifact reduction algorithm is proposed that uses block classification and feedforward neural network filters in the spatial domain. At first, the existence of blocking artifact is determined using statistical characteristics of neighborhood block, which is then used to classify the block boundaries into one of four classes. Thereafter, adaptive inter-block filtering is only performed in two classes of block boundaries that include blocking artifact. That is, in smooth regions with blocking artifact, a two-layer feedforward neural network filters trained by an error back-propagation algorithm is used, while in complex regions with blocking artifact, a linear interpolation method is used to preserve the image details. Experimental results show that the proposed algorithm produces better results than the conventional algorithms. | ||||||||||
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