![]() For example, several studies proposed correction methods for unbalanced lighting images ( Agarwal et al., 2007 Gijsenij et al., 2011 Akbarinia and Parraga, 2017 Wang et al., 2017). Notably, conventional studies have not constructed a model estimating the apparent color in photographs. However, no methodology relating to factors other than color temperature and illumination has been proposed to correct the discrepancy between the apparent and pixel colors. Based on psychophysical experiments in various lighting environments ( Kuriki and Uchikawa, 1996 Granzier and Gegenfurtner, 2012), a color appearance model (CAM) ( Moroney et al., 2002 Luo and Pointer, 2018) and S-CIE Lab model ( Johnson and Fairchild, 2003) have been proposed to correct these effects. The additional effects of adjacent colors are known as the simultaneous contrast ( Wong, 2010 Klauke and Wachtler, 2015) and assimilation effects ( Anderson, 1997). Color constancy is related to the ability to achieve stable color perception regardless of changes in the visual environment, such as illumination, the presence of shadows, and the biased color of lighting ( Ebner, 2012). These factors include color constancy, adjacent colors, illumination, and context. Various factors influence color perception, often resulting in the difference between pixel-wise color and the apparent color. For extracting the preferable color of the users, it is essential to consider users’ color perception. Other color picker and selector applications include map coloring ( Harrower and Brewer, 2003), architectural paint selection ( Bailey et al., 2003), cosmetics selection ( Jain et al., 2008), and color testing of chemicals ( Solmaz et al., 2018). The selected colors are reused in illustrations and graphs ( Ryokai et al., 2004 Shugrina et al., 2017). These tools are popular interfaces in the fields of design and art. The authors believe that the proposed methodology could be applied to develop user interfaces to compensate for the discrepancy between human perception and computer predictions.Ĭolor pickers and selectors are utilized to extract preferable colors from images, videos, and objects. However, the accuracy decreased for several conditions, including low and high saturation at low luminance. The evaluation experiments confirm that the estimated color was closer to the apparent color than the pixel color for an average of 70%–80% of the images. The linear regression model incorporates features that reflect multi-scale adjacent colors. Regression models were constructed based on the psychophysical dataset for given images to predict the apparent color from image features. In this paper, the authors investigate suitable model structures and features for constructing an apparent color picker, which extracts the apparent color from natural photos. However, methodologies for estimating the apparent color in photos have yet to be proposed. ![]() Apparent color in photos differs from pixel color due to complex factors, including color constancy and adjacent color.
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