David R. Flatla

Calibration games: making calibration tasks enjoyable by adding motivating game elements

Interactive systems often require calibration to ensure that input and output are optimally configured. Without calibration, user performance can degrade (e.g., if an input device is not adjusted for the users abilities), errors can increase (e.g., if color spaces are not matched), and some interactions may not be possible (e.g., use of an eye tracker). The value of calibration is often lost, however, because many calibration processes are tedious and unenjoyable, and many users avoid them altogether. To address this problem, we propose calibration games that gather calibration data in an engaging and entertaining manner. To facilitate the creation of calibration games, we present design guidelines that map common types of calibration to core tasks, and then to well-known game mechanics. To evaluate the approach, we developed three calibration games and compared them to standard procedures. Users found the game versions significantly more enjoyable than regular calibration procedures, without compromising the quality of the data. Calibration games are a novel way to motivate users to carry out calibrations, thereby improving the performance and accuracy of many human-computer systems.

Color correction for optical see-through displays using display color profiles

In optical see-through displays, light coming from background objects mixes with the light originating from the display, causing what is known as the color blending problem. Color blending negatively affects the usability of such displays as it impacts the legibility and color encodings of digital content. Color correction aims at reducing the impact of color blending by finding an alternative display color which, once mixed with the background, results in the color originally intended. In this paper we model color blending based on two distortions induced by the optical see-through display. The render distortion explains how the display renders colors. The material distortion explains how background colors are changed by the display material. We show the render distortion has a higher impact on color blending and propose binned-profiles (BP) - descriptors of how a display renders colors - to address it. Results show that color blending predictions using BP have a low error rate - within nine just noticeable differences (JND) in the worst case. We introduce a color correction algorithm based on predictions using BP and measure its correction capacity. Results show light display colors can be better corrected for all backgrounds. For high intensity backgrounds light colors in the neutral and CyanBlue regions perform better. Finally, we elaborate on the applicability, design and hardware implications of our approach.

So that's what you see: building understanding with personalized simulations of colour vision deficiency

Colour vision deficiencies (CVD) affect the everyday lives of a large number of people, but it is difficult for others - even friends and family members - to understand the experience of having CVD. Simulation tools can help provide this experience; however, current simulations are based on general models that have several limitations, and therefore cannot accurately reflect the perceptual capabilities of most individuals with reduced colour vision. To address this problem, we have developed a new simulation approach that is based on a specific empirical model of the actual colour perception abilities of a person with CVD. The resulting simulation is therefore a more exact representation of what a particular person with CVD actually sees. We tested the new approach in two ways. First, we compared its accuracy with that of the existing models, and found that the personalized simulations were significantly more accurate than the old method. Second, we asked pairs of participants (one with CVD, and one close friend or family member without CVD) to discuss images of everyday scenes that had been simulated with the CVD persons particular model. We found that the personalized simulations provided new insights into the details of the CVD persons experience. The personalized-simulation approach shows great promise for improving understanding of CVD (and potentially other conditions) for people with ordinary perceptual abilities.

Improving calibration time and accuracy for situation-specific models of color differentiation

Color vision deficiencies (CVDs) cause problems in situations where people need to differentiate the colors used in digital displays. Recoloring tools exist to reduce the problem, but these tools need a model of the users color-differentiation ability in order to work. Situation-specific models are a recent approach that accounts for all of the factors affecting a persons CVD (including genetic, acquired, and environmental causes) by using calibration data to form the model. This approach works well, but requires repeated calibration - and the best available calibration procedure takes more than 30 minutes. To address this limitation, we have developed a new situation-specific model of human color differentiation (called ICD-2) that needs far fewer calibration trials. The new model uses a color space that better matches human color vision compared to the RGB space of the old model, and can therefore extract more meaning from each calibration test. In an empirical comparison, we found that ICD-2 is 24 times faster than the old approach, and had small but significant gains in accuracy. The efficiency of ICD-2 makes it feasible for situation-specific models of individual color differentiation to be used in the real world.

Individual models of color differentiation to improve interpretability of information visualization

Color is commonly used to represent categories and values in many computer applications, but differentiating these colors can be difficult in many situations (e.g., for users with color vision deficiency (CVD), or in bright light). Current solutions to this problem can adapt colors based on standard simulations of CVD, but these models cover only a fraction of the ways in which color perception can vary. To improve the specificity and accuracy of these approaches, we have developed the first ever individualized model of color differentiation (ICD). The model is based on a short calibration performed by a particular user for a particular display, and so automatically covers all aspects of the users ability to see and differentiate colors in an environment. In this paper we introduce the new model and the manner in which differentiability limits are predicted. We gathered empirical data from 16 users to assess the models accuracy and robustness. We found that the model is highly effective at capturing individual differentiation abilities, works for users with and without CVD, can be tuned to balance accuracy and color availability, and can serve as the basis for improved color adaptation schemes.

Oh that's what you meant!: reducing emoji misunderstanding

Emoji provide a way to express nonverbal conversational cues in computer-mediated communication. However, people need to share the same understanding of what each emoji symbolises, otherwise communication can breakdown. We surveyed 436 people about their use of emoji and ran an interactive study using a two-dimensional emotion space to investigate (1) the variation in peoples interpretation of emoji and (2) their interpretation of corresponding Android and iOS emoji. Our results show variations between peoples ratings within and across platforms. We outline our solution to reduce misunderstandings that arise from different interpretations of emoji.

Enabling Designers to Foresee Which Colors Users Cannot See

Users frequently experience situations in which their ability to differentiate screen colors is affected by a diversity of situations, such as when bright sunlight causes glare, or when monitors are dimly lit. However, designers currently have no way of choosing colors that will be differentiable by users of various demographic backgrounds and abilities and in the wide range of situations where their designs may be viewed. Our goal is to provide designers with insight into the effect of real-world situational lighting conditions on peoples ability to differentiate colors in applications and imagery. We therefore developed an online color differentiation test that includes a survey of situational lighting conditions, verified our test in a lab study, and deployed it in an online environment where we collected data from around 30,000 participants. We then created ColorCheck, an image-processing tool that shows designers the proportion of the population they include (or exclude) by their color choices.

Situation-Specific Models of Color Differentiation

Color is commonly used to represent categories and values in computer applications, but users with Color-Vision Deficiencies (CVD) often have difficulty differentiating these colors. Recoloring tools have been developed to address the problem, but current recolorers are limited in that they work from a model of only one type of congenital CVD (i.e., dichromatism). This model does not adequately describe many other forms of CVD (e.g., more common congenital deficiencies such as anomalous trichromacy, acquired deficiencies such as cataracts or age-related yellowing of the lens, or temporary deficiencies such as wearing tinted glasses or working in bright sunlight), and so standard recolorers work poorly in many situations. In this article we describe an alternate approach that can address these limitations. The new approach, called Situation-Specific Modeling (SSM), constructs a model of a specific user’s color differentiation abilities in a specific situation, and uses that model as the basis for recoloring digital presentations. As a result, SSM can inherently handle all types of CVD, whether congenital, acquired, or environmental. In this article we describe and evaluate several models that are based on the SSM approach. Our first model of individual color differentiation (called ICD-1) works in RGB color space, and a user study showed it to be accurate and robust (both for users with and without congenital CVD). However, three aspects of ICD-1 were identified as needing improvement: the calibration step needed to build the situation-specific model, and the prediction steps used in recoloring were too slow for real-world use; and the results of the model’s predictions were too coarse for some uses. We therefore developed three further techniques: ICD-2 reduces the time needed to calibrate the model; ICD-3 reduces the time needed to make predictions with the model; and ICD-4 provides additional information about the degree of differentiability in a prediction. Our final result is a model of the user’s color perception that handles any type of CVD, can be calibrated in two minutes, and can find replacement colors in near-real time (~1 second for a 64-color image). The ICD models provide a tool that can greatly improve the perceptibility of digital color for many different types of CVD users, and also demonstrates situation-specific modeling as a new approach that can broaden the applicability of assistive technology.

SSMRecolor: improving recoloring tools with situation-specific models of color differentiation

Color is commonly used to convey information in digital environments, but colors can be difficult to distinguish for many users -- either because of a congenital color vision deficiency (CVD), or because of situation-induced CVDs such as wearing colored glasses or working in sunlight. Tools intended to improve color differentiability (recoloring tools) exist, but these all use abstract models of only a few types of congenital CVD; if the users color problems have a different cause, existing recolorers can perform poorly. We have developed a recoloring tool (SSMRecolor) based on the idea of situation-specific modeling -- in which we build a performance-based model of a particular user in their specific environment, and use that model to drive the recoloring process. SSMRecolor covers a much wider range of CVDs, including acquired and situational deficiencies. We evaluated SSMRecolor and two existing tools in a controlled study of peoples color-matching performance in several environmental conditions. The study included participants with and without congenital CVD. Our results show both accuracy and response time in color-matching tasks were significantly better with SSMRecolor. This work demonstrates the value of a situation-specific approach to recoloring, and shows that this technique can substantially improve the usability of color displays for users of all types.

ACE: A Colour Palette Design Tool for Balancing Aesthetics and Accessibility

Colour can convey a mood or elicit a particular emotion and, in terms of web design, colour can influence attitudes, perceptions, and behaviours. However, many websites demonstrate inaccessible colour choices. Numerous online colour palette design tools only focus on assisting designers with either the aesthetics or accessibility of colours. With a user-centered design approach, we developed the Accessible Colour Evaluator (ACE, daprlab.com/ace) which enhances web developers’ and designers’ ability to balance aesthetic and accessibility constraints. We distributed an online questionnaire to 28 web developers and designers to understand their attitudes and utilisation of accessibility guidelines, as well as to gather initial design requirements for ACE. With this information, we created three low-fidelity paper prototypes that were used to create two high-fidelity prototypes. The high-fidelity prototypes were discussed with 4 web developers and designers during a design workshop, and their feedback was used to develop the final version of ACE. A comparative evaluation of ACE and three existing alternative tools was conducted with 10 new web developers and designers. All participants were able to complete a colour palette design task when using ACE and identified ACE as their most preferred tool. The mean scores for the six TLX measures show ACE as providing the best performance and causing the lowest frustration. Finally, we conducted a small focus group with 3 web developers and designers to gather qualitative feedback about ACE. Participants identified a number of ACE’s strengths and made suggestions for future extensions and improvements.