Computational Image Quality
Images are a powerful, efficient means for communicating information, spurring advances in technologies underlying image capture, transfer, storage and display. This text asks the fundamental question: what is image quality? The answer requires that we think of images not as signals, but as carriers of visual information, and the visuo-cognitive processing of images as information processing. This processing is an essential stage in human interaction with the environment; the perceived adequacy of an image is based on comparisons to our memory standards of what is natural and correct. With this in mind, the author presents partially flexible metrics and methods for predicting the usefulness and naturalness of reproductions, including the measurement of discriminability and identifiability. Algorithms that summarize these concepts complete this analysis.
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Approaches to Image Quality
Image Quality Semantics 1
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algorithm angular frequency approach assumed attribute strength distribution averaged over subjects Blommaert calculated Celsius Chapter chroma CIELAB CIELUV color space color constancy color reproductions color temperature defined degree of flexibility degree of match discriminability and identifiability discriminable items discriminative power ds(x dynamic range S/a example experimentally obtained judgments Figure gain factor histogram equalization identifiable items image quality images of natural input internal noise internal representation internally quantified luminance manipulated measure memory standards momentary distribution natural scenes naturalness judgments noise level number of discriminable number of identifiable number of topological observed optimal metric overall discriminability overall probability partially flexible metric Perr Photo CD predictions probability density function PSNR quality judgments reference white result rigid s-shaped transform scale value difference scale value distribution semantic level signal sky areas solid lines subjects and scenes topological error versus visibility of detail visual metric visual perception visual processing visuo-cognitive processing z-scored