The quality of light sources

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.

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.

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.

This paper illustrates how to design a visual experiment to measure color differences in gonioapparent materials and how to assess the merits of different advanced color-difference formulas trying to predict the results of such experiment. Successful color-difference formulas are necessary for industrial quality control and artificial color-vision applications. A color- difference formula must be accurate under a wide variety of experimental conditions including the use of challenging materials like, for example, gonioapparent samples. Improving the experimental design in a previous paper [Melgosaet al., Optics Express 22, 3458-3467 (2014)], we have tested 11 advanced color-difference formulas from visual assessments performed by a panel of 11 observers with normal colorvision using a set of 56 nearly achromatic colorpairs of automotive gonioapparent samples. Best predictions of our experimental results were found for the AUDI2000 color-difference formula, followed by color-difference formulas based on the color appearance model CIECAM02. Parameters in the original weighting function for lightness in the AUDI2000 formula were optimized obtaining small improvements. However, a power function from results provided by the AUDI2000 formula considerably improved results, producing values close to the inter-observer variability in our visual experiment. Additional research is required to obtain a modified AUDI2000 color-difference formula significantly better than the current one.

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.

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.

In part I of this work [Opt. Express, 27 ,9276, (2019) 10.1364/OE.27.009276], we carried out an experiment to investigate the effects of adapting luminance and correlated color temperature (CCT) on degree of chromatic adaptation. Under the highest white luminance Lw of 900 cd/m2, an incomplete chromatic adaptation was still found under the 2700 and 3500 K adapting conditions. This motivated us to further increase the adapting luminance to investigate whether a complete chromatic adaptation cannot happen under a low adapting CCT (e.g., 2700 K). In this experiment, we investigated the degrees of chromatic adaptation under 12 adapting conditions, comprising four CCT (i.e., 2700, 3200, 4000, and 6500 K) and three white luminance levels (i.e., Lw of 1200, 2100, and 3000 cd/m2), by asking human observers to adjust the color appearance of a stimulus to the whitest. Such luminance levels of the adapting conditions were never investigated in the past and are assumed to introduce a complete chromatic adaptation. The results clearly show that an incomplete chromatic adaptation still happened under the adapting condition having a CCT of 2700 or 3200 K, though the luminance was so high. The adapting luminance and CCT were found to jointly affect the degree of chromatic adaptation, with a higher degree of adaptation under a higher adapting CCT or luminance level. When the adapting CCT was low (i.e., 2700 or 3200 K), the increase of adapting luminance was found to be able to increase the degree of adaptation more effectively. These findings suggest the necessity to revise the chromatic adaptation transforms (CAT) and color appearance models (CAM) for better characterizing the color appearance of stimulus under different adapting conditions.

Two daylight-balanced colour negative films, one professional and one amateur product, have been exposed to a standard subject using approximately Planckian light sources with a wide range of colour temperatures. Prints have been made to achieve a standard grey reproduction from each negative and have also been prepared using a commercial automatic printer calibrated with the negative, in each set, exposed using professional electronic flash. Subjective investigations of print acceptability are used to investigate the influence of the different spectral sensitivity distributions of the two negative films. Print acceptability is also related to a Colour Reproduction Index calculated from the standard subject, using the Hunt colour appearance model. Conclusions are drawn concerning the applicability of different subjective techniques in determining print acceptability, the effect of negative sensitivities and the applicability of the Hunt colour appearance model to problems of print acceptability.

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.

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.

This chapter covers the basic structure and function of the human visual system with respect to people's perception of color, and procedures to quantify those color perceptions, colorimetry, through the measurement and specification of light sources, materials, and human observers. specific topics addressed include: visual anatomy and physiology related to color perception, the spatial and temporal properties of color vision, color vision deficiencies, basic and advanced colorimetry, light sources and illuminants, materials, tristimulus values, CIE colorimetry, chromaticity coordinates, color spaces, color difference metrics, and color appearance models. A basic understanding of human color vision and color specification is important for imaging science and technology in order to develop insight into the design, engineering, and limitations of color imaging systems and further improve such systems. CIE colorimetry is also the fundamental basis of modern color management systems that enable device‐independent color imaging in digital systems. Since it is very difficult to cover all of this material in sufficient detail in a single article, interested readers should look to books devoted to color science for more detailed information. These reference marks are listed.

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.

A model for LED spectra at different drive currents is established. The simulation program of color rendering of a white-light LED cluster is developed according to the principle of additive color mixtures. The program can predict not only the spectral power distribution, chromaticity coordinates, correlated color temperature (CCT), and color rendering index (CRI), but also the drive currents of LEDs, luminous flux, input power, and luminous efficacy of white-light LED clusters. Three types of CCT tunable white-light LED clusters [warm-white/red/green/blue (WW/R/G/B), neutral-white (NW)/R/G/B, and cool-white/R/amber/G clusters] with high CRI are found by simulation analysis and realized in our laboratory. The experimental results show that the WW/R/G/B cluster can realize CCT tunable white light with high CRIs (above 90) but lower luminous efficacies (below 65 lm/W), and that the NW/R/G/B cluster can realize CCT tunable white light with high CRIs (above 86), as well as high luminous efficacies (above 64 lm/W).

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