Abstract
An adaptive nonlinear scheme for the blocking effect reduction in low bit rate block coded images is presented. A method is developed to extract useful features from the compressed image and uses them as inputs to three-layer feedforward neural networks. The neural networks learn to reduce the coding errors in the block border areas. The new technique has been applied to process JPEG compressed images and results are presented which show improvements in both visual quality and peak-signal to noise ratio (PSNR).
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