Contrast-discrimination and contrast-appearance require different visual mechanisms

Jing Xing

doi:10.1117/12.473940

17 June 2003 Contrast-discrimination and contrast-appearance require different visual mechanisms

Jing Xing

Author Affiliations +

Proceedings Volume 5007, Human Vision and Electronic Imaging VIII; (2003) https://doi.org/10.1117/12.473940
Event: Electronic Imaging 2003, 2003, Santa Clara, CA, United States

Abstract

Image fidelity refers to the ability of discriminating the differences between two images, while image quality refers to the preference of one image over another image. Many image fidelity predictors adapted the results from contrast discrimination experiments. It is questionable whether these models can be used for image quality assessment. Image quality judgment mostly occurs to suprathreshold images and the judgment generally cares about the image appearance rather than the subtle differences between two images. In this report, we presented psychophysical evidence that image perception and discrimination have different mechanism in terms of spatial interactions. We built an image processing model of the primary visual system to simulate contrast perception and contrast discrimination at the suprathreshold level. By introducing different neuronal integration mechanisms (i.e., decision rules) to the same model structure, the model was able to predict the perceived contrast and contrast discrimination thresholds of a target embedded in a complex image. Our simulations showed that the contextual effect was significant in contrast perception and thus should not be ignored by the models of image quality measurement. With further calibrations, the 2-D output of the model can be used to as an image quality metric to predict perceptual differences in images.

Citation Download Citation

Jing Xing "Contrast-discrimination and contrast-appearance require different visual mechanisms", Proc. SPIE 5007, Human Vision and Electronic Imaging VIII, (17 June 2003); https://doi.org/10.1117/12.473940

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KEYWORDS

Image quality

Image processing

Visualization

Visual process modeling

Visual system

Quality measurement

Data modeling

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