What is the best luminance contrast weighting-function for image quality optimization? Traditionally measured contrast
sensitivity functions (CSFs), have been often used as weighting-functions in image quality and difference metrics. Such
weightings have been shown to result in increased sharpness and perceived quality of test images. We suggest contextual
CSFs (cCSFs) and contextual discrimination functions (cVPFs) should provide bases for further improvement, since
these are directly measured from pictorial scenes, modeling threshold and suprathreshold sensitivities within the context
of complex masking information. Image quality assessment is understood to require detection and discrimination of
masked signals, making contextual sensitivity and discrimination functions directly relevant.
In this investigation, test images are weighted with a traditional CSF, cCSF, cVPF and a constant function. Controlled
mutations of these functions are also applied as weighting-functions, seeking the optimal spatial frequency band
weighting for quality optimization. Image quality, sharpness and naturalness are then assessed in two-alternative forced-choice
psychophysical tests. We show that maximal quality for our test images, results from cCSFs and cVPFs, mutated
to boost contrast in the higher visible frequencies.