Gamut Mapping Research Articles

Generic color correction for tone mapping operators in high dynamic range imaging

One of the significant challenges in tone mapping is to preserve the perceptual quality of the tone-mapped images. Traditional tone mapping operators (TMOs) compress the luminance of high dynamic range (HDR) images with little to no consideration of image color information resulting into suboptimal colors. Existing color management algorithms require either manual parameter tuning or introducing lightness and hue shifts. The current study aimed to develop a color correction model in plausible agreement with the psychophysical phenomenon of color appearance perception for optimal color reproduction of tone mapped images. The proposed model leverages CIECAM16 perceptual correlates i.e., lightness, chroma, and hue. It is adaptive and entirely automatic while preserving the lightness of the tone-mapped image and maintaining the hue from the HDR image. Moreover, it does not require any gamut mapping correction algorithm in natural color reproduction. The effectiveness of the model was validated using objective and subjective methods. The experiments revealed that the model achieved optimal color reproduction in terms of the naturalness of the colors.

Reconstructing the Colors of Underwater Images Based on the Color Mapping Strategy

Underwater imagery plays a vital role in ocean development and conservation efforts. However, underwater images often suffer from chromatic aberration and low contrast due to the attenuation and scattering of visible light in the complex medium of water. To address these issues, we propose an underwater image enhancement network called CM-Net, which utilizes color mapping techniques to remove noise and restore the natural brightness and colors of underwater images. Specifically, CM-Net consists of a three-step solution: adaptive color mapping (ACM), local enhancement (LE), and global generation (GG). Inspired by the principles of color gamut mapping, the ACM enhances the network’s adaptive response to regions with severe color attenuation. ACM enables the correction of the blue-green cast in underwater images by combining color constancy theory with the power of convolutional neural networks. To account for inconsistent attenuation in different channels and spatial regions, we designed a multi-head reinforcement module (MHR) in the LE step. The MHR enhances the network’s attention to channels and spatial regions with more pronounced attenuation, further improving contrast and saturation. Compared to the best candidate models on the EUVP and UIEB datasets, CM-Net improves PSNR by 18.1% and 6.5% and SSIM by 5.9% and 13.3%, respectively. At the same time, CIEDE2000 decreased by 25.6% and 1.3%.

Color gamut extension algorithm for various images based on laser display.

In pursuit of enhancing the display performance of gamut extension algorithms across diverse image types while minimizing image dependency, this study introduces a dynamic gamut extension algorithm. The algorithm is designed to extend the sRGB source gamut towards the wide gamut of a laser display. To evaluate its effectiveness, psychophysical experiments were conducted using four distinct image categories: complexions, scenery, objects, and color blocks and bars. The performance of the proposed algorithm was benchmarked against four established color gamut mapping algorithms. The comparative analysis revealed that our algorithm excels in handling wide color gamuts, outperforming the alternatives in terms of preference and the preservation of detail richness.

Poster Session II: XR-based personalized active aid for color deficient observers.

In a previous study, Xu et al. (Optics Express, 2022) investigated the efficacy of active aid in the form of personalized image enhancement to increase color discrimination ability in color-deficient observers (CDO). The study parameterized severity of color deficiency, the wavelength shift of cone spectral fundamentals, and the spectral distribution of display primaries. The first parameter was derived by computing the confusion index of the CDO, employing a modified version of the FM-100 test (ZJU50Hue). The second parameter was determined via evaluation of a wavelength-shifted ZJU50Hue test on color-normal observers (CNO). The three parameters were used to model the gamut mapping between CNO and CDO. In this study, extended reality (XR) based modules were developed to acquire these parameters and consequently tailor the headset display to assist CDOs. We chose to implement the Cambridge color test over the ZJU50Hue test as threshold results along the protan, deutan, and tritan lines are more informative than a single confusion index. Preliminary results on a calibrated Varjo XR-3 headset suggest a high correlation between the standard CCT and our XR-based trivector test. As the calibration, simulation and modeling processes all take place in the same HMD, we intend to model the CNO-CDO gamut mapping into a post-process graphics shader to enhance the camera input of the XR-3 and perform a paper-based Ishihara test for evaluation of real-world color discrimination efficacy.

Spectral-level assessment of light pollution from urban façade lighting

Façade lighting makes cities brighter at night and causes ecological impacts, as more wavelengths associated with species spectral sensitivity are emitted in light spectra. To assess the resulting ground-level light pollution and explore ecological lighting strategies, this study collected hyperspectral data of 74 lighting facades in Shanghai, China and analyzed lighting features related to ecological impacts. Spectral-level assessment shows that multi-spectral lighting causes higher luminance, more variable colours, and stronger eco-impacts, e.g., on human circadian rhythms, human photopic vision and plant photosynthesis than uniform-colour lighting. High ecological impacts are associated with narrow spectra of saturated blue and red colours, and cannot be effectively reduced by luminance control. A colour gamut mapping strategy is thus proposed to reduce the eco-impacts through colour restriction. This study quantitatively assesses the light pollution from façade lighting and reveals the associations between different lighting indicators and ecological impacts. The colour gamut mapping strategy provides a trade-off between visual effects and eco-impacts, which helps to balance the needs of urban development and environment. The findings provide guidance to lighting manufacturers, designers and policy makers on eco-lighting design and its integration into sustainable lighting masterplans.

P‐142: Late‐News Poster: Research on a Gamut Mapping Color Enhancement Algorithm Based on User Experience

Four gamut mapping algorithms with different color style are designed and implemented in the DCI‐P3 OLED. It is found that the most popular tuning direction was "dark+pure", while "bright+gray" was the least. The keywords "comfortable", "real", "brilliant" and "lifelike" have a high explanatory power to user preferences..

Perceptually Optimizing Color Look-up Tables.

The quality of ICC profiles with embedded look-up tables (LUTs) depends on multiple factors: 1. the accuracy of the optical printer model, 2. the exploitation of the available gamut combined with the quality of the gamut mapping approach encoded in the B2A-LUTs (backwards LUTs) and 3. the tonal smoothness as well color accuracy of the backwards LUTs. It can be shown that optimizing the smoothness of the LUTs comes at the expense of color accuracy and requires gamut reduction because of internal tonal edges. We present a method to optimize backwards LUTs of existing ICC profiles w.r.t accuracy, smoothness, gamut exploitation and mapping, which can be extended beyond color, e.g. to joint color and translucency backward LUTs. The approach is based on a perceptual difference metric that is used to optimize the LUT's tonal smoothness constrained to preserve both the accuracy of and the relationship between colors.

Constant hue loci in different color spaces for stimuli in Rec. 2020 color gamut and HDR conditions.

Hue is an important attribute for characterizing a color stimulus, which is also an output in various color spaces. The investigations on the hue linearity and constant hue loci for different color spaces were generally conducted using conventional CRT displays or surface color samples, in which the color stimuli were within small color gamuts and viewed under standard dynamic range conditions. With the development of imaging technologies, the hue linearity and constant hue loci need to be investigated for wide color gamuts and high dynamic range conditions, which is critically important for image processing (e.g., gamut mapping and tone mapping). In this study, we carefully carried out a hue matching experiment using high-power LED devices. The color stimuli almost reached Rec. 2020 color gamut with the luminance above the diffuse white luminance (i.e., a high dynamic range condition). The results suggested that the hue linearity of ICTCP color space was the best among the nine color spaces. Twenty-one constant hue loci were derived for each of these nine color spaces, which can be used for hue correction when performing image processing and to further revise the color spaces.

Upper limit of gamut volumes in multi-primary display systems.

Based on the difference between multi-primary displays (MPDs) and three-primary displays, we propose a new definition for evaluating the color gamut volume (CGV) to explore the upper limit of MPDs, which could theoretically represent all colors that MPDs can display. The proposed definition corrects the defects in the L*a*b* color space that arise when calculating the CGV of MPDs. In view of the high computational complexity of this method, we propose a simplified scheme with a small margin of error. Additionally, we verify the new definition with experiments on a six-primary projector. This method is helpful in guiding the selection of light sources and the evaluation of MPDs, and also has great reference value to calculate the target gamut for gamut mapping in MPDs.

Perceived Color Gamut in Images: From Boundary to Difference.

A larger display color gamut volume (CGV) is expected to produce higher perceived brightness and colorfulness of the images displayed. However, display control algorithms such as gamut mapping and color conversion need to be carefully controlled to fully take advantage of the higher luminance and more saturated display primaries. Using RGBW displays (RGB plus a white channel) as a special case in contrast to RGB displays, it is demonstrated that a larger RGB display gamut enclosed by the boundary did not guarantee a larger color gamut perceived in images. Five gamuts with different white channel contributions were simulated, and seven different image contents were curated and rendered on each display. Using a paired comparison experiment with 33 observers, the perceived scales of color gamut as perceived brightness and colorfulness were derived. The results show more correlation with the image-wise than display-wise CGV and can be explained with image color differences. Our findings highlight the importance of considering image contents when optimizing display gamut volume, which can be guided by such image-wise analysis.

52‐5: A Color Gamut Mapping Method Using an OLED Display Model

In this paper, we introduce how to map the native Organic Light Emitting Diode (OLED) panel's color gamut to the standard gamut (i.e., sRGB or P3). In order to achieve better accuracy and speedy mapping for the overall gamut region, we modeled OLED panel considering IR‐drop and Lateral leakage which are unique properties of OLED. In the experiment results, it is confirmed that the native mobile OLED panel was tuned to the standard gamut properly and proposed Color Gamut Mapping (CGM) result had an average distortion less than 0.4 in CIEDE2000. (It almost equals to 0.5 JNCD) In addition, whole calibration procedure can be performed within 1 minute by using proposed method, so that could be applied to individual panel calibration in a factory line.

Personalized image enhancement method for color deficient observers.

A personalized image enhancement method is proposed to improve color vision in hereditary color vision deficiency (CVD). It is divided into two stages: evaluation of CVD and gamut mapping for image enhancement. These two separate stages are connected via a psychophysical experiment, through which the deficiency test result expressed using the C-index can be further transformed into a physical parameter, namely the wavelength shift of the cone fundamental. Experiments conducted by the color-deficient observers (CDOs) validated this proposed method, and it is emphasized that the proposed method is just serving as a template for image enhancement. A more advanced simulation model, a more accurate assessment method, or a more sophisticated gamut mapping algorithm can yield a better result.

Developing a gamut mapping method for a novel inkjet printer

Gamut mapping algorithms (GMAs) map all the colors within the input image to colors reproducible with a printer. In this paper we discuss a gamut mapping algorithm we’ve developed for a novel inkjet nail printer. The algorithm we used previously for this suffers from visible desaturation since it only exploits the part of the printer gamut that overlaps with the sRGB gamut. To solve this issue, we add a step we call gamut alignment, which enables the printer to fully exploit the entire printer gamut. We show digitally simulated gamut mapped images to illustrate that the proposed GMA indeed produced better saturated gamut mapped images.

Image Enhancement for Colour Deficiency via Gamut Mapping

This paper describes a colour image enhancement method for those having colour-vision deficiencies. The proposed method can be divided into 3 stages. Firstly, a conversion relation between the wavelength shift (measured in nanometers) of a colour deficient observer (CDO) and the severity of colour deficiency was established. Secondly, the perceived colour gamut was built by applying the conversion relation. Finally, the original images were re-coloured by adopting a gamut mapping algorithm to map colours from the gamut of colour normal observer (CNO) to that of a CDO. Psychophysical experiments were then conducted to show the effectiveness of the method.

Gamut mapping based image enhancement algorithm for color deficiencies.

This study presents an approach for enhancing color images for color vision deficiencies. The proposed approach is separated into three stages. First, the type and severity of a color deficient observer (CDO) were evaluated. Following that, the perceived color gamut was assessed using a physiologically-based color deficiency simulation model. Finally, images prepared for color normal observers (CNOs) were re-colored using a gamut mapping method to map colors from the gamut of a CNO to that of a CDO. Two psychophysical experiments were carried out to validate this method, and the results suggest that it is a promising solution for the CDOs. The unique feature of the present method is to include a gamut mapping method to enhance the color discrimination by preserving the perceived hue.

Nonlinear color space coded by additive digital pulses

Additive color mixing modulated by digital pulses enables universal applications in lights and displays. Conventional methods map binarized signals to ordinary red–green–blue color spaces, loosely connected with the color perception of human eyes, causing the complexity of gamut mapping and inaccuracy of chromatic manipulations. Here we developed a complete theory that encodes and decodes digital signals directly to a perceptually nonuniform color space of the commission internationale de l’éclairage, featuring a strict bijection between the duty cycles of each binary pulse and color components of the mixed light and an analytic nonlinear gamut volume. Exemplary applications in biophotonic lighting, active full-color displaying, and metaphotonic sensing confirmed our theory. The method applies to the fields with accurate manipulations of chromaticity and luminosity of a light, opening an avenue toward the next generation of perceptual displays, cameras, and sensors.

Using ACES Look Modification Transforms (LMTs) in VFX Environments – Part 2: Gamut Mapping

In modern moving image production pipelines, it is unavoidable to move the footage through different color spaces. Unfortunately, these color spaces exhibit color gamuts of various sizes. The most common problem is converting the cameras’ widegamut color spaces to the smaller gamuts of the display devices (cinema projector, broadcast monitor, computer display). So it is necessary to scale down the scene-referred footage to the gamut of the display using tone mapping functions [34].In a cinema production pipeline, ACES is widely used as the predominant color system. The all-color compassing ACES AP0 primaries are defined inside the system in a general way. However, when implementing visual effects and performing a color grade, the more usable ACES AP1 primaries are in use. When recording highly saturated bright colors, color values are often outside the target color space. This results in negative color values, which are hard to address inside a color pipeline. "Users of ACES are experiencing problems with clipping of colors and the resulting artifacts (loss of texture, intensification of color fringes). This clipping occurs at two stages in the pipeline: <list list-type="simple"> <list-item>- Conversion from camera raw RGB or from the manufacturer’s encoding space into ACES AP0</list-item> <list-item>- Conversion from ACES AP0 into the working color space ACES AP1" [1]</list-item> </list>The ACES community established a Gamut Mapping Virtual Working Group (VWG) to address these problems. The group’s scope is to propose a suitable gamut mapping/compression algorithm. This algorithm should perform well with wide-gamut, high dynamic range, scene-referred content. Furthermore, it should also be robust and invertible. This paper tests the behavior of the published GamutCompressor when applied to in- and out-ofgamut imagery and provides suggestions for application implementation. The tests are executed in The Foundry’s Nuke [2].

Improving picture‐reproducing quality by a new color gamut mapping method

AbstractThe color gamut of input source and the display set usually are disparate; therefore, a mapping mechanism between them is necessary and makes significant impact on the picture‐reproducing quality. In this paper, a new gamut mapping algorithm is proposed, which reduces color difference significantly between the source and target, compared with that without adopting such mapping process. The gamma behavior of luminance dependence on gray scale has been little affected. Artifacts like saturation inversion and hue inconsistency are avoided.

Expanding the color gamut of inkjet textile printing during color matching

AbstractOne of the most important subjects of digital printing is evaluation of color gamut mapping. A printer can only reproduce colors that are in its color gamut. Maybe, a color tolerance between reference sample and printed sample is in agreement between manufacturer and customer. The goal of this work is to estimate the expansion of the color gamut of an inkjet textile printer by consideration of the acceptable (predetermined) color difference during color matching. For analyzing the color gamut, a standard color chart IT8.7/4‐2005 CMYK target was printed by nine different mixtures of CMYK, and then the reflectance spectra and colorimetric parameters of the printed samples were measured. The inks used were disperse water‐based inks that were printed on the polyester fabric. The maximum acceptable color difference in this study was equal to 2 CIELAB units for all colors. In order to estimate the color gamut with the maximum acceptable color difference, for all boundary points of gamut, expanded points were obtained by Euclidean distance less than = 2. The results show that when the acceptable color difference is considered in color gamut measuring, the area and volume of the color gamut are increased and so, the reproducibility of printer is increased. The maximum and minimum expansions of color gamut volumes were obtained by the mixtures with minimum and maximum color gamuts, respectively. The volume expansion of the mixture with the minimum color gamut volume was 34.20%, while the volume expansion of the mixture with the maximum color gamut volume was 28.06%.

Vision Models for Wide Color Gamut Imaging in Cinema.

Gamut mapping is the problem of transforming the colors of image or video content so as to fully exploit the color palette of the display device where the content will be shown, while preserving the artistic intent of the original content's creator. In particular, in the cinema industry, the rapid advancement in display technologies has created a pressing need to develop automatic and fast gamut mapping algorithms. In this article, we propose a novel framework that is based on vision science models, performs both gamut reduction and gamut extension, is of low computational complexity, produces results that are free from artifacts and outperforms state-of-the-art methods according to psychophysical tests. Our experiments also highlight the limitations of existing objective metrics for the gamut mapping problem.