Adaptive Methods for Color Vision Impaired Users

Color plays a key role in the understanding of the information in computer environments. It happens that about 5% of the world population is affected by color vision deficiency (CVD), also called color blindness. This visual impairment hampers the color perception, ending up by limiting the overall perception that CVD people have about the surrounding environment, no matter it is real or virtual. In fact, a CVD individual may not distinguish between two different colors, what often originates confusion or a biased understanding of the reality, including web environments, whose web pages are plenty of media elements like text, still images, video, sprites, and so on. Aware of the difficulties that color-blind people may face in interpreting colored contents, a significant number of recoloring algorithms have been proposed in the literature with the purpose of improving the visual perception of those people somehow. However, most of those algorithms lack a systematic study of subjective assessment, what undermines their validity, not to say usefulness. Thus, in the sequel of the research work behind this Ph.D. thesis, the central question that needs to be answered is whether recoloring algorithms are of any usefulness and help for colorblind people or not. With this in mind, we conceived a few preliminary recoloring algorithms that were published in conference proceedings elsewhere. Except the algorithm detailed in Chapter 3, these conference algorithms are not described in this thesis, though they have been important to engender those presented here. The first algorithm (Chapter 3) was designed and implemented for people with dichromacy to improve their color perception. The idea is to project the reddish hues onto other hues that are perceived more regularly by dichromat people. The second algorithm (Chapter 4) is also intended for people with dichromacy to improve their perception of color, but its applicability covers the adaptation of text and image, in HTML5- compliant web environments. This enhancement of color contrast of text and imaging in web pages is done while keeping the naturalness of color as much as possible. Also, to the best of our knowledge, this is the first web recoloring approach targeted to dichromat people that takes into consideration both text and image recoloring in an integrated manner. The third algorithm (Chapter 5) primarily focuses on the enhancement of some of the object contours in still images, instead of recoloring the pixels of the regions bounded by such contours. Enhancing contours is particularly suited to increase contrast in images, where we find adjacent regions that are color indistinguishable from dichromat’s point of view. To our best knowledge, this is one of the first algorithms that take advantage of image analysis and processing techniques for region contours. After accurate subjective assessment studies for color-blind people, we concluded that the CVD adaptation methods are useful in general. Nevertheless, each method is not efficient enough to adapt all sorts of images, that is, the adequacy of each method depends on the type of image (photo-images, graphical representations, etc.). Furthermore, we noted that the experience-based perceptual learning of colorblind people throughout their lives determines their visual perception. That is, color adaptation algorithms must satisfy requirements such as color naturalness and consistency, to ensure that dichromat people improve their visual perception without artifacts. On the other hand, CVD adaptation algorithms should be object-oriented, instead of pixel-oriented (as typically done), to select judiciously pixels that should be adapted. This perspective opens an opportunity window for future research in color accessibility in the field of in human-computer interaction (HCI).A cor desempenha um papel fundamental na compreensão da informação em ambientes computacionais. Porém, cerca de 5% da população mundial é afetada pela deficiência de visão de cor (ou Color Vision Deficiency (CVD), do Inglês), correntemente designada por daltonismo. Esta insuficiência visual dificulta a perceção das cores, o que limita a perceção geral que os indivíduos têm sobre o meio, seja real ou virtual. Efetivamente, um indivíduo com CVD vê como iguais cores que são diferentes, o que origina confusão ou uma compreensão distorcida da realidade, assim como dos ambientes web, onde existe uma abundância de conteúdos média coloridos, como texto, imagens fixas e vídeo, entre outros. Com o intuito de mitigar as dificuldades que as pessoas com CVD enfrentam na interpretação de conteúdos coloridos, tem sido proposto na literatura um número significativo de algoritmos de recoloração, que têm como o objetivo melhorar, de alguma forma, a perceção visual de pessoas com CVD. Porém, a maioria desses trabalhos carece de um estudo sistemático de avaliação subjetiva, o que põe em causa a sua validação, se não mesmo a sua utilidade. Assim, a principal questão à qual se pretende responder, como resultado do trabalho de investigação subjacente a esta tese de doutoramento, é se os algoritmos de recoloração têm ou não uma real utilidade, constituindo assim uma ajuda efetiva às pessoas com daltonismo. Tendo em mente esta questão, concebemos alguns algoritmos de recoloração preliminares que foram publicados em atas de conferências. Com exceção do algoritmo descrito no Capítulo 3, esses algoritmos não são descritos nesta tese, não obstante a sua importância na conceção daqueles descritos nesta dissertação. O primeiro algoritmo (Capítulo 3) foi projetado e implementado para pessoas com dicromacia, a fim de melhorar a sua perceção da cor. A ideia consiste em projetar as cores de matiz avermelhada em matizes que são melhor percebidos pelas pessoas com os tipos de daltonismo em causa. O segundo algoritmo (Capítulo 4) também se destina a melhorar a perceção da cor por parte de pessoas com dicromacia, porém a sua aplicabilidade abrange a adaptação de texto e imagem, em ambientes web compatíveis com HTML5. Isto é conseguido através do realce do contraste de cores em blocos de texto e em imagens, em páginas da web, mantendo a naturalidade da cor tanto quanto possível. Além disso, tanto quanto sabemos, esta é a primeira abordagem de recoloração em ambiente web para pessoas com dicromacia, que trata o texto e a imagem de forma integrada. O terceiro algoritmo (Capítulo 5) centra-se principalmente na melhoria de alguns dos contornos de objetos em imagens, em vez de aplicar a recoloração aos pixels das regiões delimitadas por esses contornos. Esta abordagem é particularmente adequada para aumentar o contraste em imagens, quando existem regiões adjacentes que são de cor indistinguível sob a perspetiva dos observadores com dicromacia. Também neste caso, e tanto quanto é do nosso conhecimento, este é um dos primeiros algoritmos em que se recorre a técnicas de análise e processamento de contornos de regiões. Após rigorosos estudos de avaliação subjetiva com pessoas com daltonismo, concluiu-se que os métodos de adaptação CVD são úteis em geral. No entanto, cada método não é suficientemente eficiente para todos os tipo de imagens, isto é, o desempenho de cada método depende do tipo de imagem (fotografias, representações gráficas, etc.). Além disso, notámos que a aprendizagem perceptual baseada na experiência das pessoas daltónicas ao longo de suas vidas é determinante para perceber aquilo que vêem. Isto significa que os algoritmos de adaptação de cor devem satisfazer requisitos tais como a naturalidade e a consistência da cor, de modo a não pôr em causa aquilo que os destinatários consideram razoável ver no mundo real. Por outro lado, a abordagem seguida na adaptação CVD deve ser orientada aos objetos, em vez de ser orientada aos pixéis (como tem sido feito até ao momento), de forma a possibilitar uma seleção mais criteriosa dos pixéis que deverão ser sujeitos ao processo de adaptação. Esta perspectiva abre uma janela de oportunidade para futura investigação em acessibilidade da cor no domínio da interacção humano-computador (HCI)

Contour Enhancement Algorithm for Improving Visual Perception of Deutan and Protan Dichromats

A variety of recoloring methods has been proposed in the literature to remedy the problem of confusing red-green colors faced by dichromat people (as well by other color-blinded people). The common strategy to mitigate this problem is to remap colors to other colors. But, it is clear this does not guarantee neither the necessary contrast to distinguish the elements of an image, nor the naturalness of colors learnt from past experience of each individual. In other words, the individual’s perceptual learning may not hold under color remapping. With this in mind, we introduce the first algorithm primarily focused on the enhancement of object contours in still images, instead of recoloring the pixels of the regions bounded by such contours. This is particularly adequate to increase contrast in images where we find adjacent regions that are color-indistinguishable from the dichromacy’s point of view

Contour Enhancement Algorithm for Improving Visual Perception of Deutan and Protan Dichromats

A variety of recoloring methods has been proposed in the literature to remedy the problem of confusing red-green colors faced by dichromat people (as well by other color-blinded people). The common strategy to mitigate this problem is to remap colors to other colors. But, it is clear this does not guarantee neither the necessary contrast to distinguish the elements of an image, nor the naturalness of colors learnt from past experience of each individual. In other words, the individual’s perceptual learning may not hold under color remapping. With this in mind, we introduce the first algorithm primarily focused on the enhancement of object contours in still images, instead of recoloring the pixels of the regions bounded by such contours. This is particularly adequate to increase contrast in images where we find adjacent regions that are color-indistinguishable from the dichromacy’s point of view.info:eu-repo/semantics/publishedVersio

CVD-MET: an image difference metric designed for analysis of color vision deficiency aids

Color vision deficiency (CVD) has gained in relevance in the last decade, with a surge of proposals for aid systems that aim to improve the color discrimination capabilities of CVD subjects. This paper focuses on the proposal of a new metric called CVD-MET, that can evaluate the efficiency and naturalness of these systems through a set of images using a simulation of the subject’s vision. In the simulation, the effect of chromatic adaptation is introduced via CIECAM02, which is relevant for the evaluation of passive aids (color filters). To demonstrate the potential of the CVD-MET, an evaluation of a representative set of passive and active aids is carried out both with conventional image quality metrics and with CVD-MET. The results suggest that the active aids (recoloration algorithms) are in general more efficient and produce more natural images, although the changes that are introduced do not shift the CVD’s perception of the scene towards the normal observer’s perception.Junta de Andalucia A-TIC-050-UGR18Spanish Government FIS2017-89258-PMinisterio de Ciencia, Innovación y Universidades RTI2018-094738-B-I0

Personalized Color Vision Deficiency Friendly Image Generation

Approximately, 350 million people, a proportion of 8%, suffer from color vision deficiency (CVD). While image generation algorithms have been highly successful in synthesizing high-quality images, CVD populations are unintentionally excluded from target users and have difficulties understanding the generated images as normal viewers do. Although a straightforward baseline can be formed by combining generation models and recolor compensation methods as the post-processing, the CVD friendliness of the result images is still limited since the input image content of recolor methods is not CVD-oriented and will be fixed during the recolor compensation process. Besides, the CVD populations can not be fully served since the varying degrees of CVD are often neglected in recoloring methods. To address these issues, we introduce a personalized CVD-friendly image generation algorithm distinguished by two key features: (i) the ability to produce CVD-oriented images that align with the needs of CVD populations, and (ii) the capacity to generate continuous personalized images for people with various CVD degrees through disentangling the color representation based on a triple-latent structure. Quantitative and qualitative experiments affirm the effectiveness of our proposed image generation model, demonstrating its practicality and superior performance compared to standard generation models and combination baselines across multiple datasets

An Interactive App for Color Deficient Viewers

Color deficient individuals have trouble seeing color contrasts that could be very apparent to individuals with normal color vision. For example, for some color deficient individuals, red and green apples do not have the striking contrast they have for those with normal color vision, or the abundance of red cherries in a tree is not immediately clear due to a lack of perceived contrast. We present a smartphone app that enables color deficient users to visualize such problematic color contrasts in order to help them with daily tasks. The user interacts with the app through the touchscreen. As the user traces a path around the touchscreen, the colors in the image change continuously via a transform that enhances contrasts that are weak or imperceptible for the user under native viewing conditions. Specifically, we propose a transform that shears the data along lines parallel to the dimension corresponding to the affected cone sensitivity of the user. The amount and direction of shear are controlled by the user'sfinger movement over the touchscreen allowing them to visualize these contrasts. Using the GPU, this simple transformation, consisting of a linear shear and translation, is performed efficiently on each pixel and in real-time with the changing position of the user's finger. The user can use the app to aid daily tasks such as distinguishing between red and green apples or picking out ripe bananas

Individualized Models of Colour Differentiation through Situation-Specific Modelling

In digital environments, colour is used for many purposes: for example, to encode information in charts, signify missing field information on websites, and identify active windows and menus. However, many people have inherited, acquired, or situationally-induced Colour Vision Deficiency (CVD), and therefore have difficulties differentiating many colours. Recolouring tools have been developed that modify interface colours to make them more differentiable for people with CVD, but these tools rely on models of colour differentiation that do not represent the majority of people with CVD. As a result, existing recolouring tools do not help most people with CVD. To solve this problem, I developed Situation-Specific Modelling (SSM), and applied it to colour differentiation to develop the Individualized model of Colour Differentiation (ICD). SSM utilizes an in-situ calibration procedure to measure a particular user’s abilities within a particular situation, and a modelling component to extend the calibration measurements into a full representation of the user’s abilities. ICD applies in-situ calibration to measuring a user’s unique colour differentiation abilities, and contains a modelling component that is capable of representing the colour differentiation abilities of almost any individual with CVD. This dissertation presents four versions of the ICD and one application of the ICD to recolouring. First, I describe the development and evaluation of a feasibility implementation of the ICD that tests the viability of the SSM approach. Second, I present revised calibration and modelling components of the ICD that reduce the calibration time from 32 minutes to two minutes. Next, I describe the third and fourth ICD versions that improve the applicability of the ICD to recolouring tools by reducing the colour differentiation prediction time and increasing the power of each prediction. Finally, I present a new recolouring tool (ICDRecolour) that uses the ICD model to steer the recolouring process. In a comparative evaluation, ICDRecolour achieved 90% colour matching accuracy for participants – 20% better than existing recolouring tools – for a wide range of CVDs. By modelling the colour differentiation abilities of a particular user in a particular environment, the ICD enables the extension of recolouring tools to helping most people with CVD, thereby reducing the difficulties that people with CVD experience when using colour in digital environments

Flexible Technique to Enhance Color-image Quality for Color-deficient Observers

Color-normal observers (CNOs) and color-deficient observers (CDOs) have different preferences and emotions for color images. A color-image quality-enhancement algorithm for a CDO is developed to easily adjust images according to each observer`s preference or image quality factors. The color-perception differences between CDO and CNO are analyzed and modeled in terms of the YCbCr chroma ratio and hue difference; then the color-shift method is designed to control the degree of color difference

Live Video and Image Recolouring for Colour Vision Deficient Patients

Colour Vision Deficiency (CVD) is an important issue for a significant population across the globe. There are several types of CVD\u27s, such as monochromacy, dichromacy, trichromacy, and anomalous trichromacy. Each of these categories contain specific other subtypes. The aim of this research is to device a scheme to address CVD by using variations in pixel plotting of colours to capture colour disparities and perform colour compensation. The proposed scheme recolours the video and images by colour contrast variation of each colour for CVD patients, and depending on the type of deficiency, it is able to provide live results. Different types of CVD’s can be identified and cured by changing the particular colour related to it and based upon the type of diseases, it performs RGB (Red, Green, and Blue) to LMS (Long, Medium, and Short) transformation. This helps in colour identification and also adjustments of colour contrasts. The processing and rendering of recoloured video and images, allows the affected patients with CVD to see perfect shades in the recoloured frames of video or images and other modes of files. In this thesis, we propose an efficient recolouring algorithm with a strong focus on real-time applications that is capable of providing different recoloured outputs based on specific types of CVD