KEYWORDS: Color difference, Edge detection, RGB color model, Visualization, Human vision and color perception, Sensors, Image processing, Molybdenum, Digital imaging, Televisions
Edge detection can be of great importance to image processing in various digital imaging applications such as digital
television and camera. Therefore, extracting more accurate edge properties are significantly demanded for achieving a
better image understanding. In vector gradient edge detection, absolute difference of RGB values between a center pixel
value, and its neighborhood values are usually used, although such a device-dependent color space does not account for
human visual characteristics well. The goal of this study is to test a variety of color difference equations and propose the
most effective model that can be used for the purpose of color edge detection. Three of synthetic images generated using
perceptibility threshold of the human visual system were used for objectively evaluate to 5 color difference equations
studied in this paper. A set of 6 complex color images was also used to testing the 5 color difference equations
psychophysically. The equations include ΔRGB, ΔE*ab, ΔECMC, CIEDE2000 (ΔE00) and CIECAM02-UCS delta E
(ΔECAM-UCS). Consequently, there were not significant performance variations observed between those 5 color difference
equations for the purpose of edge detection. However, ΔE00 and ΔECAM-UCS showed slightly higher mean opinion score
(MOS) in detected edge information.
KEYWORDS: Visualization, LCDs, Fiber optic illuminators, Data modeling, Visual process modeling, Data analysis, Physics, Manufacturing, Quantum wells, Color imaging
The purpose of this study can be divided into two descriptions. First, we investigated perceived brightness contrast to
varied surround luminance levels from dark to over-bright conditions by measuring psychophysical data using magnitude
estimation. As a result, the perceived brightness contrast increases until surround changes from dark to average, it
decreases from average to over-bright. Second, so obtained experimental results are compared with brightness contrast
estimates of CIECAM028 and MobileCAM9 and we refined a surround factor c and brightness correlate Q of CIECAM02. Consequently, the refined results appear matched to brightness contrast. A Pearson correlation between the
refined CIECAM02 prediction and the visual results was 0.95.
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