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Ideally, a quality assessment system would perceive and measure image or video impairments just like a human
being. But in reality, objective quality metrics do not necessarily correlate well with perceived quality [1]. Plus,
some measures assume that there exists a reference in the form of an "original" to compare to, which prevents their
usage in digital restoration field, where often there is no reference to compare to. That is why subjective evaluation
is the most used and most efficient approach up to now.
But subjective assessment is expensive, time consuming and does not respond, hence, to the economic requirements
[2,3].
Thus, reliable automatic methods for visual quality assessment are needed in the field of digital film restoration.
The ACE method, for Automatic Color Equalization [4,6], is an algorithm for digital images unsupervised
enhancement. It is based on a new computational approach that tries to model the perceptual response of our vision
system merging the Gray World and White Patch equalization mechanisms in a global and local way.
Like our vision system ACE is able to adapt to widely varying lighting conditions, and to extract visual information
from the environment efficaciously. Moreover ACE can be run in an unsupervised manner. Hence it is very useful
as a digital film restoration tool since no a priori information is available.
In this paper we deepen the investigation of using the ACE algorithm as a basis for a reference free image quality
evaluation. This new metric called DAF for Differential ACE Filtering [7] is an objective quality measure that can
be used in several image restoration and image quality assessment systems. In this paper, we compare on different
image databases, the results obtained with DAF and with some subjective image quality assessments (Mean Opinion
Score MOS as measure of perceived image quality). We study also the correlation between objective measure and
MOS.
In our experiments, we have used for the first image test set "Single Stimulus Continuous Quality Scale" (SSCQS)
method and in the second "Double Stimulus Continuous Quality Scale" (DSCQS) method. The users, which are
non-experts, were asked to identify their preferred image (between original and ACE filtered images) according to
contrast, naturalness, colorfulness, quality, chromatic diversity and overall subjective preference. Test and results
are presented.
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