Paper
19 January 2009 Constructing a metrics for blur perception with blur discrimination experiments
Chien-Chung Chen, Kuei-Po Chen, Chia-Huei Tseng, Sheng-Tzung Kuo, Kuei-Neng Wu
Author Affiliations +
Proceedings Volume 7242, Image Quality and System Performance VI; 724219 (2009) https://doi.org/10.1117/12.806107
Event: IS&T/SPIE Electronic Imaging, 2009, San Jose, California, United States
Abstract
In this study, we measured blur discrimination threshold at different blur levels. We found that the discrimination threshold first decreased and then increased again as reference edge width blur increased. This dipper shape of the blur discrimination threshold vs. reference width functions (TvW) functions can be explained by a divisive inhibition model. The first stage of the model contains a linear operator whose excitation is the inner product of the image and the sensitivity profile of the operator. The response of the blur discrimination mechanism is the power function of the excitation of the linear operator divided by the sum of the divisive inhibition and an additive factor. Changing mean luminance of the edge has little effect on blur discrimination except at very low luminance. When luminance is low, the blur discrimination was higher at small reference blur than those measured at medium to high luminance. This difference diminished at large reference blur. Such luminance effect can be explained by a change in the additive factor in the model. Reducing contrast of the edge shifted the whole TvW function up vertically. This effect can be explained by the decrease of gain factors in the linear operator. With these results, we constructed a metric for blur perception from the divisive inhibition we proposed and tested in this study.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chien-Chung Chen, Kuei-Po Chen, Chia-Huei Tseng, Sheng-Tzung Kuo, and Kuei-Neng Wu "Constructing a metrics for blur perception with blur discrimination experiments", Proc. SPIE 7242, Image Quality and System Performance VI, 724219 (19 January 2009); https://doi.org/10.1117/12.806107
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KEYWORDS
Data modeling

Visualization

Spatial frequencies

Visual process modeling

Motion measurement

Silicon

Image quality

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