Kyungah Choi

User-preferred color temperature adjustment for smartphone display under varying illuminants

Abstract. The study aims to investigate the user-preferred color temperature adjustment for smartphone displays by observing the effect of the illuminant’s chromaticity and intensity on the optimal white points preferred by users. For visual examination, subjects evaluated 14 display stimuli presented on the Samsung Galaxy S3 under 19 ambient illuminants. The display stimuli were composed of 14 nuanced whites varying in color temperature from 2900 to 18,900 K. The illuminant conditions varied with combinations of color temperature (2600 to 20,100 K) and illuminance level (30 to 3100 lx) that simulated daily lighting experiences. The subjects were asked to assess the optimal level of the display color temperatures based on their mental representation of the ideal white point. The study observed a positive correlation between the illuminant color temperatures and the optimal display color temperatures (r=0.89, p<0.05). However, the range of the color temperature of the smartphone display was much narrower than that of the illuminants. Based on the assessments by 100 subjects, a regression formula was derived to predict the adjustment of user-preferred color temperature under changing illuminant chromaticity. The formula is as follows: [Display Tcp=6534.75 log (Illuminant Tcp)−16304.68 (R2=0.87, p<0.05)]. Moreover, supporting previous studies on color reproduction, the effect of illuminant chromaticity was relatively weaker under lower illuminance. The results of this experiment could be used as a theoretical basis for designers and manufacturers to adjust user-preferred color temperature for smartphone displays under various illuminant conditions.

Context-based presets for lighting setup in residential space

This study aims to derive context-based lighting setup presets in residential space by exploring the multilateral relationships among household activities, affects, and lighting setups. Three procedures were involved: First, sixty affective words were evaluated through which seven affect factors were extracted to facilitate the evaluation of colored illumination in the subsequent experiment. Second, in the user study, seven affect factors and thirty household activities were used to evaluate 147 lighting setups extracted from combinations of twelve hues, six illuminance levels, and three purity levels. As a result, twenty lighting setup presets were derived that were not only activity-based, but affect-based as well. Lastly, the twenty presets were prototyped as a set of testbed to further explore potentials and limitations. This study demonstrates that intuitive, context-based presets can help users explore the effects of colored illumination in creating a diverse range of user experiences.

Investigation of eye-catching colors using eye tracking

An eye tracking experiment was conducted to investigate the relationship between eye gazing movements and the color attributes to support the creation of effective communication and increase aesthetic satisfaction. With consideration to the context of smart phones, the study focused on icon arrays, and thus each stimulus set was composed of 25 color square patches arrayed in the format of a 5 by 5 grid. The experiment was divided into three parts, each examining one specific attribute of color, while controlling its other attributes. Fifteen college students were recruited, among whom all partook in all three parts. In Part I, hue difference was examined. Each stimulus set contained 25 hues under a fixed tone. It was revealed that subjects were more attentive to warm colors than to cool colors, particularly when warm colors were arranged along the horizontal and vertical axes; In Part II, the experiment dealt with tone difference. 25 tone variations for red, green and blue were provided as stimulus sets. However, the result indicated that changes in tone does not have a significant influence on subjects’ initial attention; Lastly, in Part III, combinations of colors were examined to determine whether color contrast influenced participants’ attention in a manner different from that of single colors. Among them, icons with complementary contrast gained the greatest attention. Throughout the experiments, the background was applied with either black or white; however a contrast effect between background and foreground was not noticeable.

Designing skin-dragging haptic motions for wearables

Skin-dragging is an emerging type of haptic feedback that coveys both precise spatial and temporal tactile cues through the motion of a small pin dragged across the skin. While past research focused on building skin-dragging wearable devices with different form-factors, and testing their feasibility, it is still unclear what the users perception of such haptic stimuli is, and how designers should generate dragging motion-patterns for informative feedback to be presented on a finger. In this work, we attempt to answer these questions. We therefore asked designers to create dragging motions using changes of speed, direction and length. We then tested the generated skin-dragging motions with a haptic smart-ring, classified them and extracted guidelines that can be used to convey rich and informative feedback on the fingers.

Assessment of white for displays under dark- and chromatic-adapted conditions

This study aims to investigate the white perception of mobile display devices under dark-adapted and chromatic-adapted conditions. The white perception was modeled with error ellipses and bivariate Gaussian distributions. The dark-adapted white encompassed a rather large area centered around 7300 K, slightly above the Planckian locus. The chromatic-adapted whites were highly dependent on the ambient illuminant, and were not parallel to the Planckian locus. Combined, the white region encompassing 6179 to 7479 K in correlated color temperature and -0.0038 to 0.0144 in Duv was suggested. The results of this study are expected to be the basis for enhanced white appearance on mobile display devices.

A comparative study of psychophysical judgment of color reproductions on mobile displays between Europeans and Asians

The purpose of this study is to investigate the differences in the psychophysical judgment of mobile display color appearances between Europeans and Asians. A total of 50 participants, comprising 20 Europeans (9 French, 6 Swedish, 3 Norwegians, and 2 Germans) and 30 Asians (30 Koreans) participated in this experiment. A total of 18 display stimuli with different correlated color temperatures were presented, varying from 2,470 to 18,330 K. Each stimulus was viewed under 11 illuminants ranging from 2,530 to 19,760 K, while their illuminance was consistent around 500 lux. The subjects were asked to assess the optimal level of the display stimuli under different illuminants. In general, confirming the previous studies on color reproduction, we found a positive correlation in the correlated color temperatures between the illuminants and optimal displays. However, Europeans preferred a lower color temperature compared to Asians along the entire range of the illuminants. Two regression equations were derived to predict the optimal display color temperature (y) under varying illuminants (x) as follows: y = α + β*log(x), where α = -8770.37 and β = 4279.29 for European (R2 = 0.95, p < .05), and α = -16076.35 and β = 6388.41 for Asian (R2 = 0.85, p < .05). The findings provide the theoretical basis from which manufacturers can take a cultural-sensitive approach to enhancing their products’ appeal in the global markets.

The optimal color temperature of smartphone display under various illuminant conditions

The purpose of this study is to find the optimal color temperature of the smartphone display, focusing on the relationship between the illuminant conditions and the ideal whites users perceive. The subjects viewed 16 nuanced whites under 19 illuminant conditions. The study reveals that there exists a positive correlation between the color temperature of illuminant and that of a smartphone display. The optimal color temperature of a white display ranged from 6000 K to 11000 K while the color temperature of illuminant varied from 2500 K to 20000 K. However, under lower illuminance, the correlation was relatively weaker confirming previous color reproduction theories.

Optimal color temperature adjustment for mobile devices under varying illuminants

With the wide use of mobile devices, display color reproduction has become extremely important. The purpose of this study is to investigate the optimal color temperature for mobile displays under varying illuminants. The effect of the color temperature and the illuminance of ambient lighting on user preferences were observed. For a visual examination, a total of 19 nuanced whites were examined under 20 illuminants. A total of 19 display stimuli with different color temperatures (2,500 K ~ 19,600 K) were presented on an iPad3 (New iPad). The ambient illuminants ranged in color temperature from 2,500 K to 19,800 K and from 0 lx to 3,000 lx in illuminance. Supporting previous studies of color reproduction, there was found to be a positive correlation between the color temperature of illuminants and that of optimal whites. However, the relationship was not linear. Based on assessments by 56 subjects, a regression equation was derived to predict the optimal color temperature adjustment under varying illuminants, as follows: [Display Tcp = 5138.93 log(Illuminant Tcp) – 11956.59, p<.001, R2=0.94]. Moreover, the influence of an illuminant was positively correlated with the illuminance level, confirming the findings of previous studies. It is expected that the findings of this study can be used as the theoretical basis when designing a color strategy for mobile display devices.

Skin-representative region in a face for finding real skin color.

Skin detection is used in applications in computer vision, including image correction, image–content filtering, image processing, and skin classification. In this study, we propose an accurate and effective method for detecting the most representative skin color in one’s face based on the face’s center region, which is free from nonskin-colored features, such as eyebrows, hair, and makeup. The face’s center region is defined as the region horizontally between the eyes and vertically from the middle to the tip of one’s nose. The performance of the developed algorithm was verified with a data set that includes more than 300 facial images taken under various illuminant conditions. Compared to previous works, the proposed algorithm resulted in a more accurate skin color detection with reduced computational load. Introduction With the development of information technology and mobile devices, various kinds of vision-based applications have been developed. Among those applications, many facilitate skin detection algorithms. A skin detection algorithm is a process of detecting skincolored pixels and regions in a digital image. Skin detection is considered a key step because the color of human skin is cognitively highly relevant and, accordingly, it is an effective feature for computation. The main applications of skin detection are image correction, image–content filtering, image processing, and skin classification [1]. For example, hand gestures can be classified after detection and segmentation of the skin region [2–4]. A human tracking algorithm and pornographic image filtering also uses skin detection before tracking and filtering process [5–7]. Not limited to these purposes, a number of skin detection algorithms have been developed. The approach of skin detection is largely classified by two types. The first type is a pixel-based skin detection method. Skin color mainly differs in lightness, but it does not show significant differences in hue and saturation, even across different ethnic groups. Likewise, skin colors are distributed in a narrow range of color spaces [8], so it is possible to screen pixels that should not be included in the skin color range. Because skin pixels should belong to the predefined range, the computational load could be relatively low. Many previous studies followed the skin range assumption, and the range is defined differently depending on the color spaces, such as RGB, normalized RGB, HSV, YCbCr, YUV, and CIE L*a*b* [9, 10]. However, the pixel-based skin detectors did not work properly for the color shifts caused by various illuminant conditions. In particular, there were high color shifts for facial images taken under various correlated color temperatures or under low illuminance. In addition, these methods cannot distinguish nonskin regions, which have similar colors to skin. As presented in Figure 1, brown hairs on the subject’s forehead were detected as skin due to the similar color range. Furthermore, the images captured by camera can be distorted due to the illuminant and the characteristics of each device. As mentioned, the range of skin color is relatively narrow and the distortion may lead the algorithm not detecting the skin region correctly [11]. This type of existing pixel-based skin detector may filter out the skin region if it is distorted due to extreme chroma of the illuminant. One possible solution is to correct images first, which is known as color constancy. For example, some assumptions were suggested, such as the average color of the scene being gray [12] or the brightest pixel in the scene being white [13]. However, these assumptions are not truly operational in some cases, and we are not always able to control the illuminant. Figure 1. The pixel-based skin detection often fails because it does not discriminate nonskin regions as far as hues belonging to the predefined detection boundary. In this case, the brown hairs were detected as skin. The other approach of skin detection is an adaptive skin detection algorithm. This does not use a predefined detection boundary of skin regions. This takes the spatial arrangement of pixels into account; therefore, it has more flexibility in various illuminant [7, 12, 13]. Most of these methods detect the face first to extract the skin region. After facial recognition, the algorithm detects skin regions by calculating the dominant hue in the face, so it can detect the skincolored pixels near the dominant color in the color space [14]. In this way, the adaptive skin color detection technique computes skin pixels in a device-dependent color space that works properly under various illuminant conditions. However, this method inevitably includes features that are not skin colored, such as eyebrows, hair, and makeup. These nonskin-colored regions may cause inaccurate results for further process after skin color detection. For example, an estimation of accurate skin color is rarely possible if the nonskincolored region is included. In addition, this type of algorithm has more computational load than the pixel-based skin detection. As skin detection is a primary process for many of other applications, the computational load can be an important issue. As such, an improvement in skin detection is necessary to meet users’ needs for the development of vision-based. In this regards, it is anticipated to obtain the most representative skin color in one’s face to detect the other skin regions in the image without considering the nonskin features of the face. In addition, it will be more effective if the representative skin color could be calculated instead of taking the whole face region for computation. 66 IS&T International Symposium on Electronic Imaging 2017 Computational Imaging XV https://doi.org/10.2352/ISSN.2470-1173.2017.17.COIMG-425

PicLight: User-Centered Lighting Control Interface for Residential Space

This study aims to develop PicLight, a user-centered lighting control interface based on the behavior of photo editing as an analogy for controlling light. The PicLight application allows users to takes photos of a space, then uses those photos to display simulations of lighting scenarios through filter effects, providing users with guidance for easy selection of optimal lighting conditions. We formed 20 lighting presets each of that engages user activity, affection. In order to determine whether the presets were suitable, we conveyed a validation test and validated context-based presets are effective features for designing lighting control interfaces.