Abstract / truncated to 115 words (read the full abstract)

People of all generations are making more and more use of digital imaging systems in their daily lives. The image content rendered by these digital imaging systems largely differs in perceived quality depending on the system and its applications. To be able to optimize the experience of viewers of this content understanding and modeling perceived image quality is essential. Research on modeling image quality in a full-reference framework --- where the original content can be used as a reference --- is well established in literature. In many current applications, however, the perceived image quality needs to be modeled in a no-reference framework at real-time. As a consequence, the model needs to quantitatively predict perceived quality ... toggle 10 keywords

image quality assessment – human vision model – texture masking – luminance masking – blocking artifact – ringing artifact – objective metric – eye tracking – saliency map – visual attention

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