Reduced-Reference Image Quality
Assessment
Objective image quality assessment models typically require
the access to a reference image that is assumed to have perfect quality. In
practice, such full-reference (FR) methods may not be applicable because the
reference image is often not available. On the other hand, no-reference (NR) or
“blind” image quality assessment is an extremely difficult task (especially
general-purpose metrics that are applicable to a wide variety of image distortion
types). Reduced-reference (RR) image quality metrics provide a solution that
lies between FR and NR models. They are designed to predict the perceptual
quality of distorted images with only partial information about the reference
images.
This page provides an implementation of the RR method proposed
in the following paper:
Z. Wang and
E. P. Simoncelli,
"Reduced-reference
image quality assessment using a wavelet-domain natural image statistic model,"
Human Vision
and Electronic Imaging X, Proc. SPIE, vol. 5666, San Jose, CA, Jan.
2005.
You can download the software for free, change it as you like
and use it anywhere, but please refer to its original source.
Download Software
(rriqa.zip, 668K)
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