An edge classification based approach to the post-processing of transform encoded images

Quantisation noise prevalent in transform encoded images becomes increasingly objectionable as the required bit rate is reduced. The perceptual effect of this coding noise is highly dependent on the focal behaviour of the signal upon which it is superimposed. In this paper, a computationally-efficient edge classifier, employing a histogram treatment of image sub-blocks, is proposed. The classifier forms the basis of an adaptive, non-linear postprocessing algorithm incorporating adaptive α-trimmed mean filtering (where the α-value and window size are determined by the output of the edge classifier) and a transform domain dithering technique. Subjective test results confirm the efficacy of the approach