Paper
22 April 1996 Detection of changes in the amplitude spectra of natural images is explained by a band-limited local-contrast model
David J. Tolhurst, Yoav Tadmor, G. Arthurs
Author Affiliations +
Proceedings Volume 2657, Human Vision and Electronic Imaging; (1996) https://doi.org/10.1117/12.238712
Event: Electronic Imaging: Science and Technology, 1996, San Jose, CA, United States
Abstract
The psychophysical task of discriminating small changes in the slopes of the amplitude spectra of complex images (such as digitized photographs of natural scenes) has been used to examine whether the human visual system is optimized for coding the information in natural images. The discrimination thresholds are highest when the test stimuli have amplitude spectra similar in form to those of truly natural images, and are lower when the spectra are steeper or shallower than 'normal.' The magnitudes of the thresholds differ markedly between stimuli derived from different photographs. We describe a model that explains the variety of threshold magnitudes; we suppose that the observer is detecting small changes in image contrast estimated within limited spatial-frequency bands of about 1 octave bandwidth. At threshold, the contrast change in only one frequency band will generally match the observer's JND for simple sinusoidal gratings. The success of this band-limited contrast model is shown further in experiments where the slopes of the amplitude spectra of the stimuli are changed within restricted frequency bands. If the slope is changed only within the limited frequency-band implicated by the contrast model, the observer's thresholds are unchanged, but they are elevated if the slope changes are mode only outside of the implicated band.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
David J. Tolhurst, Yoav Tadmor, and G. Arthurs "Detection of changes in the amplitude spectra of natural images is explained by a band-limited local-contrast model", Proc. SPIE 2657, Human Vision and Electronic Imaging, (22 April 1996); https://doi.org/10.1117/12.238712
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Cited by 4 scholarly publications.
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KEYWORDS
Photography

Spatial frequencies

Visual system

Visualization

Chlorine

Image filtering

Error analysis

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