Paper
14 March 2013 Chromatic induction and contrast masking: similar models, different goals?
Sandra Jiménez, Xavier Otazu, Valero Laparra, Jesús Malo
Author Affiliations +
Proceedings Volume 8651, Human Vision and Electronic Imaging XVIII; 86511J (2013) https://doi.org/10.1117/12.2019010
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States
Abstract
Normalization of signals coming from linear sensors is an ubiquitous mechanism of neural adaptation.1 Local interaction between sensors tuned to a particular feature at certain spatial position and neighbor sensors explains a wide range of psychophysical facts including (1) masking of spatial patterns, (2) non-linearities of motion sensors, (3) adaptation of color perception, (4) brightness and chromatic induction, and (5) image quality assessment. Although the above models have formal and qualitative similarities, it does not necessarily mean that the mechanisms involved are pursuing the same statistical goal. For instance, in the case of chromatic mechanisms (disregarding spatial information), different parameters in the normalization give rise to optimal discrimination or adaptation, and different non-linearities may give rise to error minimization or component independence. In the case of spatial sensors (disregarding color information), a number of studies have pointed out the benefits of masking in statistical independence terms. However, such statistical analysis has not been performed for spatio-chromatic induction models where chromatic perception depends on spatial configuration. In this work we investigate whether successful spatio-chromatic induction models,6 increase component independence similarly as previously reported for masking models. Mutual information analysis suggests that seeking an efficient chromatic representation may explain the prevalence of induction effects in spatially simple images.
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Sandra Jiménez, Xavier Otazu, Valero Laparra, and Jesús Malo "Chromatic induction and contrast masking: similar models, different goals?", Proc. SPIE 8651, Human Vision and Electronic Imaging XVIII, 86511J (14 March 2013); https://doi.org/10.1117/12.2019010
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KEYWORDS
Colorimetry

Sensors

Wavelets

Image compression

Wavelet transforms

Image sensors

Statistical analysis

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