Sorting and searching operations used for the selection of test images strongly affect the results of image quality
investigations and require a high level of versatility. This paper describes the way that inherent image properties, which
are known to have a visual impact on the observer, can be used to provide support and an innovative answer to image
selection and classification. The selected image properties are intended to be comprehensive and to correlate with our
perception. Results from this work aim to lead to the definition of a set of universal scales of perceived image properties
that are relevant to image quality assessments.
The initial prototype built towards these objectives relies on global analysis of low-level image features. A
multidimensional system is built, based upon the global image features of: lightness, contrast, colorfulness, color
contrast, dominant hue(s) and busyness. The resulting feature metric values are compared against outcomes from
relevant psychophysical investigations to evaluate the success of the employed algorithms in deriving image features that
affect the perceived impression of the images.
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