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,  

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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.