Color and Image Appearance Models

Color Imaging ◽  
2008 ◽  
pp. 565-629
Keyword(s):  
Image Appearance ◽  
Appearance Models

Change of color appearance due to extremely high light level: corresponding colors under 100 and 3000 cd/m2

2019 ◽  
Vol 2019 (1) ◽  
pp. 320-325 ◽  
Author(s):  
Wenyu Bao ◽  
Minchen Wei
Keyword(s):  
Light Level ◽  
Limited Range ◽  
High Light ◽  
Color Preference ◽  
Color Appearance ◽  
Appearance Model ◽  
Color Matching ◽  
Light Levels ◽  
Appearance Models ◽  
Psychophysical Study

Great efforts have been made to develop color appearance models to predict color appearance of stimuli under various viewing conditions. CIECAM02, the most widely used color appearance model, and many other color appearance models were all developed based on corresponding color datasets, including LUTCHI data. Though the effect of adapting light level on color appearance, which is known as "Hunt Effect", is well known, most of the corresponding color datasets were collected within a limited range of light levels (i.e., below 700 cd/m2), which was much lower than that under daylight. A recent study investigating color preference of an artwork under various light levels from 20 to 15000 lx suggested that the existing color appearance models may not accurately characterize the color appearance of stimuli under extremely high light levels, based on the assumption that the same preference judgements were due to the same color appearance. This article reports a psychophysical study, which was designed to directly collect corresponding colors under two light levels— 100 and 3000 cd/m2 (i.e., ≈ 314 and 9420 lx). Human observers completed haploscopic color matching for four color stimuli (i.e., red, green, blue, and yellow) under the two light levels at 2700 or 6500 K. Though the Hunt Effect was supported by the results, CIECAM02 was found to have large errors under the extremely high light levels, especially when the CCT was low.

scholarly journals An international mixed methods study to develop a new preference-based measure for women with breast cancer: the BREAST-Q Utility module

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Manraj N. Kaur ◽  
Anne F. Klassen ◽  
Feng Xie ◽  
Louise Bordeleau ◽  
Toni Zhong ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Mixed Methods ◽  
Conceptual Framework ◽  
Field Test ◽  
Well Being ◽  
Economic Evaluations ◽  
Structured Interviews ◽  
Image Appearance ◽  
Test Version ◽  
Cancer Experience

Abstract Background Generic preference-based measures (PBM), though commonly used, may not be optimal for use in economic evaluations of breast cancer interventions. No breast cancer-specific PBM currently exists, and the generic PBMs fail to capture the unique concerns of women with breast cancer (e.g., body image, appearance, treatment-specific adverse effects). Hence, the objective of this study was to develop a breast cancer-specific PBM, the BREAST-Q Utility module. Methods Women diagnosed with breast cancer (stage 0–4, any treatment) were recruited from two tertiary hospitals in Canada and one in the US. The study followed an exploratory sequential mixed methods approach, whereby semi-structured interviews were conducted and at the end of the interview, participants were asked to list their top five health-related quality of life (HRQOL) concerns and to rate the importance of each item on the BREAST-Q. Interviews were audio-recorded, transcribed verbatim, and coded. Constant comparison was used to refine the codes and develop a conceptual framework. Qualitative and quantitative data were triangulated to develop the content of the Utility module  that was refined through 2 rounds of cognitive debriefing interviews with women diagnosed with breast cancer and feedback from experts. Results Interviews were conducted with 57 women aged 55 ± 10 years. A conceptual framework was developed from 3948 unique codes specific to breasts, arms, abdomen, and cancer experience. Five top-level domains were HRQOL (i.e., physical, psychological, social, and sexual well-being) and appearance. Data from the interviews, top 5 HRQOL concerns, and BREAST-Q item ratings were used to inform dimensions for inclusion in the Utility module. Feedback from women with breast cancer (N = 9) and a multidisciplinary group of experts (N = 27) was used to refine the module. The field-test version of the HSCS consists of 10 unique dimensions. Each dimension is measured with 1 or 2 candidate items that have 4–5 response levels each. Conclusion The field-test version of the BREAST-Q Utility module was derived from extensive patient and expert input. This comprehensive approach ensured that the content of the Utility module is relevant, comprehensive, and includes concerns that matter the most to women with breast cancer.

Sign in / Sign up

Export Citation Format

Share Document