Title :
A new learning-based deblocking algorithm for DCT coded images
Author_Institution :
Univ. of Electron. Sci. & Technol. of China, Chengdu, China
Abstract :
A new algorithm for the reduction of blocking artifacts in images compressed using block-based discrete cosine transform (DCT) is proposed in this paper. Firstly, a Bayesian model and the Markov network assumption are adopted for our deblocking algorithm. An input blocking image is divided into observation nodes of the network. Then a simplified method is applied to find approximate optimal solutions of the underlying nodes in the network. The solutions are learned from the training set. Experimental results show that the proposed approach is able to remove some blocking artifacts, at the same time, reserve sharp edges and learn fine details.
Keywords :
Bayes methods; Markov processes; discrete cosine transforms; image coding; learning (artificial intelligence); Bayesian model; DCT coded images; Markov network assumption; block-based discrete cosine transform; blocking artifacts reduction; learning-based deblocking algorithm; Discrete cosine transforms; Image resolution; Noise; Image deblocking; Markov network; discrete cosine transform (DCT); learning;
Conference_Titel :
Intelligent Signal Processing and Communication Systems (ISPACS), 2010 International Symposium on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-7369-4
DOI :
10.1109/ISPACS.2010.5704622
