Development of a perceptually calibrated objective metric of noise

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.

Development of the I3A CPIQ spatial metrics

“…The visual noise objective metric is the base 10 logarithm of the weighted sum of the variance and covariance values for the spatially filtered CIELAB image. This is the same as the equation proposed by Keelan et al 18 The assumption is that the perception of noise above threshold is approximately logarithmic: 19…”

“…1 and the IHIF. 18 The RMS errror of in terms of Quality Loss JND values was used to judge the goodness of the fit for the Levenberg-Marquardt regression algorithm. This produced the following expression for the objective visual noise metric:…”

Development of a Perceptually Calibrated Objective Metric for Exposure Quality

“…A softcopy quality ruler method, as depicted in ISO 20462 Part 3, was used in the subjective evaluation task [10][11][12][13][14]. The softcopy quality ruler package was developed for the IEEE P1858 CPIQ standard, and it has been used in developing objective metrics for numerous CPIQ image quality metrics [15][16][17]. The use of the softcopy ruler method allows an objective metric for an image attribute to be calibrated in the quality JND space, and hence it can be combined with other image quality metrics in predicting the overall image quality via multivariate formulism [18].…”