16 March 2015 Selecting stimuli parameters for video quality assessment studies based on perceptual similarity distances
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Abstract
This work presents a methodology to optimize the selection of multiple parameter levels of an image acquisition, degradation, or post-processing process applied to stimuli intended to be used in a subjective image or video quality assessment (QA) study. It is known that processing parameters (e.g. compression bit-rate) or technical quality measures (e.g. peak signal-to-noise ratio, PSNR) are often non-linearly related to human quality judgment, and the model of either relationship may not be known in advance. Using these approaches to select parameter levels may lead to an inaccurate estimate of the relationship between the parameter and subjective quality judgments – the system’s quality model. To overcome this, we propose a method for modeling the relationship between parameter levels and perceived quality distances using a paired comparison parameter selection procedure in which subjects judge the perceived similarity in quality. Our goal is to enable the selection of evenly sampled parameter levels within the considered quality range for use in a subjective QA study. This approach is tested on two applications: (1) selection of compression levels for laparoscopic surgery video QA study, and (2) selection of dose levels for an interventional X-ray QA study. Subjective scores, obtained from the follow-up single stimulus QA experiments conducted with expert subjects who evaluated the selected bit-rates and dose levels, were roughly equidistant in the perceptual quality space - as intended. These results suggest that a similarity judgment task can help select parameter values corresponding to desired subjective quality levels.
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Asli Kumcu, Ljiljana Platiša, Heng Chen, Amber Gislason-Lee, Andrew G. Davies, Peter Schelkens, Yves Taeymans, Wilfried Philips, "Selecting stimuli parameters for video quality assessment studies based on perceptual similarity distances", Proc. SPIE 9399, Image Processing: Algorithms and Systems XIII, 93990F (16 March 2015); doi: 10.1117/12.2083431; https://doi.org/10.1117/12.2083431
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