Lightness dependencies and the effect of texture on suprathreshold lightness tolerances

Abstract

A psychophysical experiment was performed to determine the effects of lightness dependency on suprathreshold lightness tolerances. Using a pass/fail method of constant stimuli, lightness tolerance thresholds were measured using achromatic stimuli centered at CIELAB L* ϭ 10, 20, 40, 60, 80, and 90 using 44 observers. In addition to measuring tolerance thresholds for uniform samples, lightness tolerances were measured using stimuli with a simulated texture of thread wound on a card. A texture intermediate between the wound thread and the uniform stimuli was also used. A computer-controlled CRT was used to perform the experiments. Lightness tolerances were found to increase with increasing lightness of the test stimuli. For the uniform stimuli this effect was only evident at the higher lightnesses. For the textured stimuli, this trend was more evident throughout the whole lightness range. Texture had an effect of increasing the tolerance thresholds by a factor of almost 2 as compared to the uniform stimuli. The intermediate texture had tolerance thresholds that were between those of the uniform and full-textured stimuli. Transforming the results into a plot of threshold vs. intensity produced results that were more uniform across the three conditions. This may indicate that CIELAB is not the best space in which to model these effects.

A colour assessment methodology for oak wood

“…The texture of the sample is one of the most important parametric criteria linked to the parametric factors of the equation (Kandi and Tehran 2009). Montag and Berns (2000) found that for textiles, texture increases the L* tolerance threshold by a factor of almost 2 compared to uniform stimuli, and Huertas et al (2006) showed that for random-dot textures, the tolerance on L* is increased the most, but also warned that the influence of texture on C* and h ab tolerances is in general not negligible. The latter also report that each texture or sort of texture should be studied separately in this context.…”

Computational models for fusion of texture and color: a comparative study

“…where function f͑·͒ is defined on each pixel position and performs linear interpolation between ͑u In addition to the RGB space, the CCM model can also be applied 10,11 in other color spaces such as l␣␤ and CIELCH. The l␣␤ space was first proposed by Ruderman et al based on the principle component analysis ͑PCA͒ of many natural scene images.…”

“…11 They first rotate the CIELCH color space using a 3 ϫ 3 matrix based on PCA to produce a decorrelated space, and then modified the mean color of the image, and finally inverted the color transform to generate new texture images. When the purpose is to simulate a new image using the same texture and different color, the PCA is no longer required.…”

“…When the purpose is to simulate a new image using the same texture and different color, the PCA is no longer required. 11 Therefore, similar to Eq. ͑15͒, the new color v CCM-LCH,p of pixel p in CIELCH space can be calculated as follows:…”

“…Montag and Berns presented a method to simulate textile color texture images in the CIELCH color space using singular value decomposition ͑SVD͒ technique. 11 It is noted that the methods proposed in Refs. [4][5][6][7]10, and 11 are all based on the statistical analysis of the texture and color distribution in texture images, while the texture rendering method in Ref.…”