Evaluation of human vision models for predicting human observer performance

Warren B. Jackson; Maya R. Said; David A. Jared; James O. Larimer; Jennifer Gille; Jeffrey Lubin

doi:10.1117/12.271312

16 April 1997 Evaluation of human vision models for predicting human observer performance

Warren B. Jackson, Maya R. Said, David A. Jared, James O. Larimer, Jennifer Gille, Jeffrey Lubin

Proceedings Volume 3036, Medical Imaging 1997: Image Perception; (1997) https://doi.org/10.1117/12.271312
Event: Medical Imaging 1997, 1997, Newport Beach, CA, United States

Abstract

We demonstrate that human-vision-model-based image quality metrics not only correlate strongly with subjective evaluations of image quality but also with human observer performance on visual recognition tasks. By varying amorphous silicon image system design parameters, the performance of human observers in target identification using the resulting test images was measured, and compared with the target weighted just-noticeable-difference produced by a human vision model applied to the same set of images. The detectability of model observer with the human observer was highly correlated for a wide range of image system design parameters. These results demonstrate that the human vision model can be used to produce human observer performance optimized imaging systems without the need for extensive human trials. The human vision based tumor detectors represent a generalization of channelized Hotelling models to non-linear, perceptually based models.

Citation Download Citation

Warren B. Jackson, Maya R. Said, David A. Jared, James O. Larimer, Jennifer Gille, and Jeffrey Lubin "Evaluation of human vision models for predicting human observer performance", Proc. SPIE 3036, Medical Imaging 1997: Image Perception, (16 April 1997); https://doi.org/10.1117/12.271312

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