Image discrimination models: detection in fixed and random noise

Albert J. Ahumada Jr.; Bettina L. Beard

doi:10.1117/12.274520

3 June 1997 Image discrimination models: detection in fixed and random noise

Albert J. Ahumada Jr., Bettina L. Beard

Proceedings Volume 3016, Human Vision and Electronic Imaging II; (1997) https://doi.org/10.1117/12.274520
Event: Electronic Imaging '97, 1997, San Jose, CA, United States

Abstract

Image discrimination modes are used to predict the visibility of the difference between two images. Using a four category rating scale method, Rohaly et al. (SPIE Vol. 2411) and Ahumada & Beard (SPIE Vol. 2657) found that image discrimination models can predict target detectability when the background is kept constant, or 'fixed.' In experiment I, we use this same rating scale method and find no difference between 'fixed' and 'random' noise (where the white noise changes from trial to trial). In experiment II, we compare fixed noise and two random noise conditions. Using a two- interval forced-choice procedure, the 'random' noise was either the same or different in the two intervals. Contrary to image discrimination model predictions, the same random noise condition produced greater masking than the 'fixed' noise. This suggests that observers use less efficient target templates than image discrimination models implicitly assume. Also, performance appeared limited by internal process variability rather than external noise variability since similar masking was obtained for both random noise types.

Citation Download Citation

Albert J. Ahumada Jr. and Bettina L. Beard "Image discrimination models: detection in fixed and random noise", Proc. SPIE 3016, Human Vision and Electronic Imaging II, (3 June 1997); https://doi.org/10.1117/12.274520

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