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 di erence 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 di erent 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.

Conventional mean squared error based methods for objective image quality assessment are not well correlated with human evaluation. The design of better objective measures of quality has attracted a lot of attention and several image quality metrics based explicitly on the properties of the Human Visual System ( HVS) have been proposed in recent years. However, only in a few cases has the performance of such metrics been demonstrated on real images. In accounting for visual masking, all these metrics assume that the multiple channels mediating visual perception are independent of each other. Recent neuroscience findings and psychophysical experiments have established that there is interaction across the channels and that such interactions are important for visual masking. In this work, we propose the Picture Distortion Metric (PDM) which integrates these new visual masking properties, and we evaluate its performance for image coding applications. We evaluate the performance at medium to high range of quality to predict subjective scores on a 0-10 numerical scale, and On a wide range of quality for the 1-5 CCIR impairment scale.

Pre-processing images prior to encoding can remove noise, or unimportant detail, and thus improve the overall performance of the coder. Typical objective image quality metrics are obtained by computing a single or several numbers as a function of the difference image between the original and coded images. Such metrics may not reflect the improvement in performance. In a recent paper we have presented a methodology that allows the quantitative determination of image quality when the image has been processed prior to encoding. We now present an extension of the work showing global objective quality measures that quantify the value of pre-processing for image coding using a wavelet coder. Because many options are available in wavelet coder design, we limit our study to a "best" coder obtained in previous work, and determine what further performance improvement can be achieved by image processing.

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