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Open Access Publications from the University of California

Quantifying Coding Performance for Preprocessed Images


Typical objective methods for quantifying image quality, as part of evaluating coder performance, are obtained by computing a single or several numbers as a function of the difference image between the original and coded images. Pre-processing images prior to encoding can remove noise, or unimportant detail, and thus improve the overall performance of the coder. However, the error image obtained with the pre-processed image as a reference is substantially different than the one obtained if the original image is used. In particular, adaptive noise removal, that generally improves the image quality, could be interpreted as introducing noise with respect to the original. This paper addresses the issue of combining the changes in the image due to pre-processing and the degradation due to encoding. The objective is to obtain global quality measures that quantify the value of pre-processing for image coding.

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