1/13/04: Zhou Wang,  NYU

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 Stimulus Synthesis for Efficient Evaluation and Refinement of Perceptual Image Quality Metrics
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We propose a methodology for comparing and refining perceptual image
quality metrics based on synthetic images that are optimized to best
differentiate two candidate quality metrics. We start from an initial
distorted image and iteratively search for the best/worst images in
terms of one metric while constraining the value of the other to
remain fixed. We then repeat this, reversing the roles of the two
metrics. Subjective test on the quality of pairs of these images
generated at different initial distortion levels provides a strong
indication of the relative strength and weaknesses of the metrics
being compared. This methodology also provides an efficient way to
further refine the definition of an image quality metric.
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<A HREF="http://www.cns.nyu.edu/~eero/ABSTRACTS/wang04a-abstract.html">Conference paper</A>