Deringing cartoons by image analogies
ACM Transactions on Graphics (TOG)
Information Sciences: an International Journal
Application of feedforward neural network for the deblocking of low bit rate coded images
AIS'04 Proceedings of the 13th international conference on AI, Simulation, and Planning in High Autonomy Systems
Hybrid deblocking algorithm for block-based low bit rate coded images
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part I
A training-based method for reducing ringing artifact in BDCT-encoded images
EGMM'04 Proceedings of the Seventh Eurographics conference on Multimedia
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A new technique based on the multilayer perceptron (MLP) neural network is proposed for blocking-artifact removal in block-coded images. The new method is based on the concept of learning-by-examples. The compressed image and its original uncompressed version are used to train the neural networks. In the developed scheme, inter-block slopes of the compressed image are used as input, the difference between the original uncompressed and the compressed image is used as desired output for training the networks. Blocking-artifact removal is realized by adding the neural network's outputs to the compressed image. The new technique has been applied to process JPEG compressed images. Experimental results show significant improvements in both visual quality and peak signal-to-noise ratio. It is also shown the present method is comparable to other state of the art techniques for quality enhancement in block-coded images