Development of a perceptually calibrated objective metric of noise

Brian W. Keelan; Elaine W. Jin; Sergey Prokushkin

doi:10.1117/12.871633

24 January 2011 Development of a perceptually calibrated objective metric of noise

Brian W. Keelan, Elaine W. Jin, Sergey Prokushkin

Proceedings Volume 7867, Image Quality and System Performance VIII; 786707 (2011) https://doi.org/10.1117/12.871633
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States

Abstract

A system simulation model was used to create scene-dependent noise masks that reflect current performance of mobile phone cameras. Stimuli with different overall magnitudes of noise and with varying mixtures of red, green, blue, and luminance noises were included in the study. Eleven treatments in each of ten pictorial scenes were evaluated by twenty observers using the softcopy ruler method. In addition to determining the quality loss function in just noticeable differences (JNDs) for the average observer and scene, transformations for different combinations of observer sensitivity and scene susceptibility were derived. The psychophysical results were used to optimize an objective metric of isotropic noise based on system noise power spectra (NPS), which were integrated over a visual frequency weighting function to yield perceptually relevant variances and covariances in CIE L*a*b* space. Because the frequency weighting function is expressed in terms of cycles per degree at the retina, it accounts for display pixel size and viewing distance effects, so application-specific predictions can be made. Excellent results were obtained using only L* and a* variances and L*a* covariance, with relative weights of 100, 5, and 12, respectively. The positive a* weight suggests that the luminance (photopic) weighting is slightly narrow on the long wavelength side for predicting perceived noisiness. The L*a* covariance term, which is normally negative, reflects masking between L* and a* noise, as confirmed in informal evaluations. Test targets in linear sRGB and rendered L*a*b* spaces for each treatment are available at http://www.aptina.com/ImArch/ to enable other researchers to test metrics of their own design and calibrate them to JNDs of quality loss without performing additional observer experiments. Such JND-calibrated noise metrics are particularly valuable for comparing the impact of noise and other attributes, and for computing overall image quality.

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Brian W. Keelan, Elaine W. Jin, and Sergey Prokushkin "Development of a perceptually calibrated objective metric of noise", Proc. SPIE 7867, Image Quality and System Performance VIII, 786707 (24 January 2011); https://doi.org/10.1117/12.871633

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