Unique hue data for colour appearance models. Part III: Comparison with NCS unique hues

Application of the CIECAM02 colour appearance model to predict the effect of gamma on the colours viewed on CRT monitors

This paper reports the results of measurements performed in a test room to investigate the impact of using different wall colours and light scenes on indoor lighting quality. Eye level spectral irradiances were measured for a user located inside the room and seated at a desk. These measurements allow the use of the Irradiance Toolbox, which makes it possible to calculate equivalent illuminances for the photopigments in the human eye. Moreover, the CIECAM02 colour appearance model was used to investigate the variations in hue and chroma values of the different wall colours when changing the light scene. The results demonstrate that there are significant variations in eye level spectral irradiance distributions, photoreceptor stimulation and hue and chroma values with different combinations of wall colours and light scenes.

This paper presents a novel mechanism for automatically matting monochromatic background images and videos in real-time. The proposed mechanism simulates the process of human perception on isolating foreground elements in a given scene. The perceptual analysis is performed on optimized hue, saturation, lightness and chroma from CIECAM02 color appearance model, which has the best overall performance across the tested data sets. The foreground and background sample-pairs for alpha estimation are adaptively predicted based on the prior analysis rather than direct sampling. To achieve real-time performance, the entire procedures are optimized for parallel processing on the GPUs (Graphics Processing Units). The qualitative evaluation shows that our determined alpha mattes and foreground colors especially in large seemingly translucent areas are more acceptable by human eyes. And the quantitative comparison between our mechanism and other existing approaches also validates the advantage in speed and quality.

An end-to-end image reproduction method is proposed to render cultural heritage images under arbitrary illuminating conditions. When displayed on a characterized display, the appearance of paper-based cultural heritages is reproduced as actually observed under the target illumination, by leveraging spectral images of the artworks and spectral power distribution (SPD) of the identical illumination. Psychophysical experiments are conducted to verify that the proposed method outperforms the traditional pipelines on reproducing real scenes, including the CIECAM02 color appearance model. The method has the potential to be applied in the digitization and exhibition of paper cultural heritages.

Unique hue plays a critical role in color appearance models and uniform color spaces. Past studies investigating unique hues commonly used 40 Munsell samples with the same chroma and lightness levels to produce color stimuli, with a hue angle step of 9°. These 40 samples were always simultaneously presented to the observers. Both the larger hue angle step and the simultaneous presentation of the samples may help to reduce the variations. In this study, we reduced the hue angle step to 5° and each stimulus was individually presented to the observer, which resulted in larger inter- and intra-observer variations. The results suggested that the hue angles of the unique hues in both CIECAM02 and CIELAB should be revised, but both CIECAM02 and CIELAB had good hue uniformity at the hue angles of the four unique hues.

In meetings just prior to the 1997 AIC Congress in Kyoto, CIE TC1-37, chaired by M. Fairchild, established the CIE 1997 Interim Colour appearance Model (Simple Version), known as CIECAM97s. CIECAM97s was formally published in 1998 in CIE publication 131. CIE TC1-37 was dissolved shortly after publication of CIECAM97s at which time, a reportership, R1- 24 held by M. Fairchild, was established to monitor ongoing developments in color appearance modeling and notify CIE Division 1 if it became necessary to form a new TC to consider revision or replacement of CIECAM97s. In the four years between AIC Congresses, there has been much activity, both by individual researchers and within the CIE, aimed at furthering our understanding of color appearance models and deriving improved models for consideration. The aim of this paper is to summarize these activities, report on the current status of CIE efforts on color appearance models, and suggest what the future might hold for CIE color appearance models.

A comprehensive colour appearance model should be capable of predicting a wide range of viewing conditions including related and unrelated colours under a wide range of illuminance levels from mesopic to photopic region. In the past, colour appearance models can only predict the colour appearance for related colours in different viewing conditions. More recently, colour appearance models to predict unrelated colours have been developed and being improved constantly. This paper investigates the performance of different colour appearance models for unrelated colours. The three colour appearance modelswere compared including CAM15u, CAMFu and the latest CAM20u. An experimental dataset carefully accumulated by Fu et al. was used for evaluation. The results showed that the latest CAM20u performed best among the three models. CAMFu, as the oldest model, whose performance was much worse than other two models in predicting brightness and colourfulness, and performed well in predicting hue composition, while CAM15u gave a moderate performance in predicting brightness and colourfulness, but performed a little worse than the others for hue composition.

Tourmaline minerals are mainly produced in pegmatite, which is mainly produced in pegmatite.At present, the color evaluation of green tourmaline in China needs to be supplemented. In this paper, the CIECAM16 color appearance model is used to calculate the color appearance attribute parameters of green tourmaline samples, and the tristimulus XYZ of green tourmaline color measured by Colori5 colorimeter is used as the input data of CIECAM16 color appearance model to predict the color appearance attribute of green tourmaline under different conditions. The CIECAM16 color appearance model written by computer C++ language is used to calculate the color appearance attribute parameters under different illuminance levels under D65 light source, different environment (Average, Dim, Dark) conditions and different illumination light source color temperatures (D65, D50, A). Through analysis and calculation of the color appearance attribute parameters, it is found that that the increase of illuminance has a more intuitive effect on the visual brightness Q, while the change of ambient light and shade has a greater effect on the brightness than the change of light source. The A light source has a more obvious effect on the tourmaline of color than D65 and D50 light sources.

Some of the reasons for needing a color appearance model in imaging are given and the use of such a model in deriving a color reproduction index is described. There are three reasons why a model of color appearance is a necessary tool in color imaging systems. First, because the viewing conditions for images are often different at different stages of reproduction systems, it is necessary to allow for their visual effects; conventional colorimetry does not do this, but a model of color appearance can supply the necessary adjustments. Second, different display media typically have different color gamuts and a regime of gamut mapping is therefore often required; it is more appropriate to conduct this mapping in an appearance space. Third, it is desirable to be able to assess the quality of color reproductions in terms of a color reproduction index and a model of color appearance can provide a metric that is suitable as the basis of such an index.

Regarding the ocean background, the painted camouflage on the surface of targets effectively conceals them, providing a strategic defense against military reconnaissance. Accurately predicting the color appearance of both the target and background is crucial for assessing the camouflage’s performance. This study investigates the perceived color attributes (lightness, chroma, and hue) of marine targets against varying ocean backgrounds using memory matching assessments. Visual data reveal that background color slightly influences target hue perception through simultaneous contrast effects, while lightness and chroma remain unaffected. As the image-based color appearance model – iCAM offers a potential solution for extracting color appearance attributes from a complex RGB image, it has been adopted to predict the pixel-wise color appearance of the marine targets. Comparative validation shows iCAM substantially outperforms existing color appearance models in marine environments, establishing its superior capability for naval camouflage assessment and design. Further analysis indicates that chroma predictions are sensitive to changes in the filter kernel size of iCAM and the image size, while lightness and hue predictions remain stable. Notably, undersized kernels systematically underestimate perceived chroma, highlighting the critical importance of parameter optimization.

Color-normal subjects sometimes disagree about metameric matches involving highly structured SPDs, because their cone fundamentals differ slightly, but non-negligibly. This has significant implications for the design of light sources and displays, so it should be estimated. We propose a broadly applicable estimation method based on a simple adaptive "front-end" interface that can be used with any selected standard color appearance model. The interface accepts, as input, any set of color matching functions for the individual subject (for example, these could be that person's cone response functions) and also the associated tristimulus values for the test stimulus and also for the reference stimulus (i.e. reference white). The interface converts this data into tristimulus values of the form used by the selected color appearance model (which could, for example, be X, Y, Z), while also carrying out the needed transform, which is based on an estimate of the subject's likely previous long-term adaptations to their unique cone fundamentals. The selected standard color appearance model then provides color appearance data that is an estimate of the color appearance of the test stimulus, for that individual subject. This information has the advantage of being interpretable within that model's well-known color space. The adaptive front end is based on the fact that, for any selected input SPD and the subject's unique color matching functions, there can be many different SPDs that are metameric for that individual. Since observer-to-observer color perception differences are minimized for spectrally smooth SPDs, smooth metamers predict color appearances reasonably accurately.

Detecting colour differences in warp‐knitting fabric is essential to ensure its quality. Machine vision algorithms are commonly used for automatic colour difference detection. However, the current algorithm directly extracts RGB (red, green, blue) colour stimulus values from the image without considering the image's colour attributes, leading to a significant error in colour difference detection. To solve this issue, a colour appearance model is introduced into the field of colour difference detection for warp‐knitted fabric. Initially, a colour appearance model based on CAM16 is established, and the critical parameters are calculated to obtain the colour appearance attribute of the fabric. The colour difference calculation formula is then constructed in the uniform colour space of CAM16‐UCS. A template selection algorithm based on principal component analysis was designed to select warp‐knitted cloth images with standard colours. Subsequently, a cloth colour difference detection algorithm was developed using the cloth colour profile model. The performance of the colour difference formula based on the colour profile model was evaluated using the PF/3 method. To compare the CIELab, CMC(2:1), and CIEDE2000 colour difference formulas, standardised residual sum of squares was used. The results indicated that the colour difference formula based on the colour‐appearance model is about 5.32% different from the visual colour difference perceived by the human eye. However, it can perform as well as the CMC(2:1) colour difference formula, which is widely used in the textile industry.

The purpose of this study is to characterise the predictive performance of the CIECAM02 and CIECAM16 colour appearance models by comparing with experimental results on Hunt Effect over a wide range of illuminance levels. In our earlier experiments, red, green, yellow, blue colour matching data under higher illumination levels (6000 lx, 1000 lx, 100 lx) were collected. In this study, another experiment was conducted to collect colour matching data on Hunt Effect at lower illumination levels (300 lx, 100 lx, and 30 lx). The chromaticity coordinates of the matches and targets from both experiments were first calculated and plotted in CAM02UCS colour space. Then the CIECAM02 and CIECAM16 predictions of the colourfulness and hue angles for matches and targets were compared across different illumination levels (30 lx to 6000 lx). Slight differences (

The conventional object segmentation methods often degrade their performance due to the requirement of user interaction and/or the incomplete colour appearance models. In this Letter, the authors propose a novel design method of a colour appearance model for accurate and fully‐automatic object segmentation by using saliency maps. The authors initialise the Gaussian mixture models (GMMs) to describe the colour appearance of the foreground objects and the background, respectively, where the mean vectors, covariance matrices, and mixing coefficients are updated adaptively such that more salient pixels have larger weights to update the GMM for the foreground objects while less salient pixels have larger weights to update the GMM for the background, respectively. Experiments are performed on MSRC, iCoseg, and PASCAL datasets and we show that the proposed method outperforms the existing methods quantitatively and qualitatively.

Color appearance of images on self-luminous displays, such as LCD monitors, depends on two sources of illumination: scene illumination within the image itself and ambient illumination in the viewing environment. Color appearance models (CAMs) attempt to predict how perception changes as ambient illumination changes. At their best they provide viewers with an illumination-independent viewing experience. However, existing CAMs are complex and application-specific. Thus they are poorly suited to adaptive displays. For example, existing adaptive displays provide only achromatic adaptation, limiting the complexity of CAMs and minimizing tuning. We propose a new model based on von Kries adaptation, the theory on which most CAMs are based upon. It takes into account the mixed adaptation state produced by self-luminous displays. Compared to existing CAMs, our model is simpler and more efficient, yet shares the generic features of more complex CAMs.