Postprocessing of transform coded images via histogram-based edge     classification

J. D. McDonnell; Robert Noel Shorten; Anthony D. Fagan

doi:10.1117/12.251159

1 January 1997 Postprocessing of transform coded images via histogram-based edge classification

J. D. McDonnell, Robert Noel Shorten, Anthony D. Fagan

Author Affiliations +

Journal of Electronic Imaging, Vol. 6, Issue 1, (January 1997). https://doi.org/10.1117/12.251159

Abstract

Quantization noise prevalent in transform encoded images becomes increasingly objectionable as the required bit rate for the compressed image representation is reduced. The perceptual effect of this coding noise is not uniform throughout the image, however, being highly dependent on the local behavior of the signal on which it is superimposed. An adaptive, nonlinear postprocessing algorithm is described, which is shown to appreciably enhance the subjective quality of the reconstructed image. A three-component image model is adopted, according to which any image is considered to be composed of nonoverlapping strong edge, textured, and monotone components. The foundation of the postprocessing algorithm is a computationally efficient edge classifier capable of resolving an image into its three components. The classifier operates by exploiting the characteristic shape of the histogram of pixel luminance values in a strong edge region to distinguish between strong and textured edges. The postprocessing algorithm consists of a combination of adaptive, α trimmed mean filtering (where the α value and window size are determined by the output of the edge classifier) and a cosine transform domain dithering technique. The results presented confirm the efficacy of the proposed approach.

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J. D. McDonnell, Robert Noel Shorten, and Anthony D. Fagan "Postprocessing of transform coded images via histogram-based edge classification," Journal of Electronic Imaging 6(1), (1 January 1997). https://doi.org/10.1117/12.251159

Published: 1 January 1997

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