Color Difference Metrics Research Articles

8‐1: Riemannian Color Difference Metric

We developed a new Riemannian color difference metric based on Friele's line element and the results of psychophysical color discrimination experiments. Visual adaptation effects are incorporated into the model. This new color difference metric was validated against various data sets available in the literature. We found adequate agreement with experimentally determined threshold ellipsoids/ellipses and a better threshold predictability compared with other color difference metrics. The new Riemannian color difference metric was applied for the calculation of the color gamut volume and the maximum number of mutually discernible colors.

Line element for the perceptual color space.

It is generally accepted that the perceptual color space is not Euclidean. A new line element for a 3-dimensional Riemannian color space was developed. This line element is based on the Friele line elements and psychophysical color discrimination models, and comprises both the first and second stage of color vision. The line element is expressed in a contrast space based on the MacLeod-Boynton chromaticities. New equations for the contrast thresholds along the cardinal axes and new metric tensor elements were determined. Visual adaptation effects were incorporated into the model. Color discrimination threshold ellipsoids were calculated with the new line element. Adequate agreement with experimental threshold ellipsoids reported in literature was demonstrated. From a comparison with other color difference metrics a better overall predictability of threshold ellipsoids was found with the new line element.

Image Quality Evaluation in Professional HDR/WCG Production Questions the Need for HDR Metrics.

In the quality evaluation of high dynamic range and wide color gamut (HDR/WCG) images, a number of works have concluded that native HDR metrics, such as HDR visual difference predictor (HDR-VDP), HDR video quality metric (HDR-VQM), or convolutional neural network (CNN)-based visibility metrics for HDR content, provide the best results. These metrics consider only the luminance component, but several color difference metrics have been specifically developed for, and validated with, HDR/WCG images. In this paper, we perform subjective evaluation experiments in a professional HDR/WCG production setting, under a real use case scenario. The results are quite relevant in that they show, firstly, that the performance of HDR metrics is worse than that of a classic, simple standard dynamic range (SDR) metric applied directly to the HDR content; and secondly, that the chrominance metrics specifically developed for HDR/WCG imaging have poor correlation with observer scores and are also outperformed by an SDR metric. Based on these findings, we show how a very simple framework for creating color HDR metrics, that uses only luminance SDR metrics, transfer functions, and classic color spaces, is able to consistently outperform, by a considerable margin, state-of-the-art HDR metrics on a varied set of HDR content, for both perceptual quantization (PQ) and Hybrid Log-Gamma (HLG) encoding, luminance and chroma distortions, and on different color spaces of common use.

Advanced Surface Color Quality Assessment in Paper-Based Full-Color 3D Printing.

Color 3D printing allows for 3D-printed parts to represent 3D objects more realistically, but its surface color quality evaluation lacks comprehensive objective verification considering printing materials. In this study, a unique test model was designed and printed using eco-friendly and vivid paper-based full-color 3D printing as an example. By measuring the chromaticity, roughness, glossiness, and whiteness properties of 3D-printed surfaces and by acquiring images of their main viewing surfaces, this work skillfully explores the correlation between the color representation of a paper-based 3D-printed coloring layer and its attached underneath blank layer. Quantitative analysis was performed using ΔE*ab, feature similarity index measure of color image (FSIMc), and improved color-image-difference (iCID) values. The experimental results show that a color difference on color-printed surfaces exhibits a high linear correlation trend with its FSIMc metric and iCID metric. The qualitative analysis of microscopic imaging and the quantitative analysis of the above three surface properties corroborate the prediction of the linear correlation between color difference and image-based metrics. This study can provide inspiration for the development of computational coloring materials for additive manufacturing.

Characterization of Color Differences for Color Palettes

Various color difference metrics were developed for characterizing the perceived color difference between individual color patches. Color difference between palettes containing multiple color patches, however, is critically important in product design and computer graphics. This study aimed to investigate how the perceived color difference between a pair of color palettes containing more than a single color patch is affected by the order and number of color patches in the palette. Two reference color sets were generated and each set had four color palettes containing 1, 4, 9, and 16 color patches that were arranged as 1 × 1, 2 × 2, 3 × 3, and 4 × 4 patterns. Human observers scaled the color differences between a color palette of the reference set and a color palette that had revised colors, or revised orders, or a combination of revised colors and orders compared to the reference palette. The calculated color differences between the two palettes were derived using the Minimum Color Difference Model (MICDM) algorithm proposed in a recent work with different color difference metrics, including CIELAB, CMC, CIE94, and DE2000. It was found that the perceived color differences of pairs of individual color patches were significantly larger than those containing multiple patches, when the calculated color differences were the same. The color differences metrics, except for CIE94, had similar performance when characterizing perceived color differences between color palettes.

Image quality evaluation for high dynamic range and wide color gamut applications using visual spatial processing of color differences

AbstractHigh dynamic range (HDR) and wide color gamut imagery has an established video ecosystem, spanning image capture to encoding and display. This drives the need for evaluating how image quality is affected by the multitudes of ecosystem parameters. The simplest quality metrics evaluate color differences on a pixel‐by‐pixel basis. In this article, we evaluate a series of these color difference metrics on four HDR and three standard dynamic range publicly available distortion databases consisting of natural images and subjective scores. We compare the performance of the well‐established CIE L*a*b* metrics (ΔE00, ΔE94) alongside two HDR‐specific metrics (ΔEZ [Jzazbz], ΔEITP [ICTCP]) and a spatial CIE L*a*b* extension (). We also present a novel spatial extension to ΔEITP derived by optimizing the opponent color contrast sensitivity functions. We observe that this advanced metric, , outperforms the other color difference metrics, and we quantify the improved performance with the steps of metric advancement.

ΔEITP is Now Ratified as ITU-R BT.2124—Is the Industry Ready to Move on from ΔE2000?

For many years, $\Delta E 2000$ has been considered the industry standard for estimating perceptual color differences and evaluating the color accuracy of display technologies. Recently, the International Telecommunication Union (ITU) released Recommendation ITU-R BT.2124, which defines a new metric, $\Delta E_{\mathrm {ITP}}$ , as the new standard for evaluating perceptual color differences. Given this new recommendation, it is important that we become familiar with how $\Delta E_{\mathrm {ITP}}$ performs in practice and what can be expected when using it in existing workflows. A key part in this process is having an understanding of the properties of the color space that the color difference metric is based on and how that relates to the intended application of the metric. In this paper, we explore this relationship as it pertains to the $\Delta E2000$ and $\Delta E_{\mathrm {ITP}}$ color difference metrics, specifically in the context of evaluating color reproduction on self-luminous displays. Simulated data and measured data from calibrated displays are used to compare the performance of the metrics. Given that $\Delta E 2000$ and $\Delta E_{\mathrm {ITP}}$ are based on significantly different derivation principles, there are notable differences that must be acknowledged between these two metrics, especially as we transition between them. It is highly likely that $\Delta E 2000$ has been underpredicting perceptual color differences, significantly so when used for the evaluation of high dynamic range (HDR) and wide color gamut (WCG) displays.

HDR and WCG Image Quality Assessment Using Color Difference Metrics

High dynamic range (HDR) and wide color gamut (WCG) imagery is now mainstream across content creation. Thus, having a reliable way of evaluating HDR systems is essential. One common way of measuring the quality of an HDR system is measuring the color errors introduced along the imaging pipeline. Unfortunately, development of color difference metrics has been based on test patches, as opposed to natural imagery, with evaluation mostly limited to standard dynamic range (SDR) databases. In this paper, we evaluate several color difference metrics on five publicly available HDR distortion databases consisting of natural images and subjective scores. Distortion types include lower frequency distortions such as those from tone mapping as well as higher-frequency distortions resulting from compression artifacts by the various compression schemes such as Joint Photographic Experts Group (JPEG). We analyze these databases with the well-established CIE <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{L}^\star \text{a}^\star \text{b}^\star $ </tex-math></inline-formula> metrics ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta E_{00}, \Delta E_{94}$ </tex-math></inline-formula> ) as well as two HDR-specific metrics: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta E_{z} (j_{z}a_{z}b_{z})$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta E_{ITP} (IC_{T}C_{P})$ </tex-math></inline-formula> . To quantify the performance, we use four standard performance evaluation procedures. We observe that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta E_{ITP}$ </tex-math></inline-formula> outperforms the other color difference metrics on four out of five databases. We also perform statistical analysis, which demonstrates the effectiveness of using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta E_{ITP}$ </tex-math></inline-formula> to assess the overall image quality of HDR/WCG content. Weaknesses of all metrics for one database suggest that more advanced spatio-chromatic visual models are worth pursuing.

Does background color influence visual thresholds?

ObjectiveTo evaluate the influence of neutral color backgrounds on visual thresholds using three color difference metrics: CIELAB, CIEDE2000(1:1:1) and CIEDE2000(2:1:1). MethodsSixty observers (dentists and laypersons; n = 30) from three countries participated in the study. A psychophysical experiment based on visual assessments of simulated images of teeth on a calibrated display was performed. Images of simulated upper central incisors (SUCI) were consecutively displayed in pairs (60) on 3 neutral color backgrounds (black, grey and white). Three color difference metrics (CIELAB, CIEDE2000(1:1:1), and CIEDE2000(2:1:1)) were used to calculate the visual thresholds (PT- perceptibility threshold; and AT- acceptability threshold) with 95 % confidence intervals (CI) and a Takagi-Sugeno-Kang (TSK) Fuzzy Approximation model was used as fitting procedure. Data was statistically analyzed using paired t-test (p = 0.05). ResultsThe50:50 % PT values were significantly lower over white background than over black background. The 50:50 % AT values were significantly greater over white background than over grey and black backgrounds. In most cases, the thresholds (PT and AT) values were significantly different for each color different metric over each background color (p ≤ 0.05). No difference was found between observers for PT and AT values. ConclusionsThe perceptibility and acceptability thresholds in dentistry are affected by the color difference metrics and by the background color. SignificanceDental color mismatches are more difficult to be accepted over a white background.

Effect of substrate and cement on the final color of zirconia-based all-ceramic crowns.

To evaluate the influence of the substrate and the cement on the final color of ceramic crowns. Prosthetic crowns (framework + porcelain) were fabricated using two all-ceramic systems (YZ, 3mol% yttria-stabilized tetragonal zirconia; IZ, alumina-based zirconia-reinforced glass-infiltrated ceramic) and a metal-ceramic system (MC; n=8). Metal (M) and resin (R) were used as substrate abutments. For R substrate, crowns were evaluated as seated (R) and after cementation (R-C). CIELAB color coordinates were obtained using a dental spectrophotometer with D65 light illumination. CIELAB, CIEDE2000 (1:1:1) and CIEDE2000 (2:1:1) color difference metrics were used. Data were analyzed using 95% confidence intervals and compared to perceptibility (PT) and acceptability (AT) thresholds. The , ∆E00 (1 : 1 : 1), and ∆E00 (2 : 1 : 1) values of the restorative systems evaluated using different substrates (M.R) were similar to the AT values of each metric. When non-cemented and cemented crowns were evaluated (R.R-C), MC had color difference values at PT and below AT; while YZ and IZ showed similar values to AT, for and ∆E00 (1 : 1 : 1) metrics. The substrate and cement affected the final color of zirconia-based all-ceramic crowns, but the color differences corresponded to an acceptable match. As both substrate and cement influence the final color of multi-layered ceramic prosthesis, dentists and dental technicians should understand the optical properties of dental ceramics to reproduce the natural appearance of dental structures and maximize the chance for excellent color matches.

80‐2: Spatiochromatic Model for Image‐Quality Prediction of High‐Dynamic‐Range and Wide‐Color‐Gamut Content

This paper presents an approach to predicting image quality by spatially filtering images before generating color difference maps with pixel‐based color difference metrics. The resulting difference maps can then be pooled across the whole image. This approach was originally developed for CIELAB color space under the name S‐CIELAB. We extend this approach to use the recently developed ICTCP color space to improve the prediction accuracy for high dynamic range and wide color gamut images. The filtering is based on the chromatic and achromatic contrast sensitivity function of the human visual system. Our results on four existing subjective image quality databases containing high dynamic range and wide color gamut images show substantial improvements at low computational cost, outperforming existing color difference metrics.

Color Restoration for Full-Waveform Multispectral LiDAR Data

The current full-waveform data at a single wavelength can mainly retrieve the geometric attributes of targets along the light path by detecting waveform components, resulting in the lack of spectral or color attribute information. This kind of device relies on a digital camera for acquiring the color information, however, which is inevitably limited by the lighting conditions and geometric registration errors. With the development of multispectral light detection and ranging (LiDAR) or even hyperspectral LiDAR that often utilize a supercontinuum laser source covering the whole visible light band, including red, green and blue bands, the simultaneous acquisition of color and spatial information becomes possible and makes passive imaging data no longer necessary. In this study, we propose a color restoration method for a full-waveform multispectral LiDAR (FWMSL) system. Additionally, we develop a multispectral lognormal function to fit the tailing echoes measured by FWMSL further accurately. Experimental data from our FWMSL system are used to evaluate the performance of the proposed method. The relative standard deviation, correlation coefficient (R2) and color difference ( Δ E ) metrics suggest that the color restoration for the full-waveform multispectral data is feasible.

Colorimetrical performance estimation of a reference hyperspectral microscope for color tissue slide assessment

Content created in High Dynamic Range (HDR) and Wide Color Gamut (WCG) is becoming more ubiquitous, driving the need for reliable tools for evaluating the quality across the imaging ecosystem. One of the simplest techniques to measure the quality of any video system is to measure the color errors. The traditional color difference metrics such as ΔE00 and the newer HDR specific metrics such as ΔEZ and ΔEITP compute color difference on a pixel-by-pixel basis which do not account for the spatial effects (optical) and active processing (neural) done by the human visual system. In this work, we improve upon the per-pixel ΔEITP color difference metric by performing a spatial extension similar to what was done during the design of S-CIELAB. We quantified the performance using four standard evaluation procedures on four publicly available HDR and WCG image databases and found that the proposed metric results in a marked improvement with subjective scores over existing per-pixel color difference metrics.

Edge-Based Demosaicking Method Using Uncorrelatedness With Sensors for CFA

This paper presents a multiple edge detection by computing uncorrelatedness of the neighboring pixels in four locations: up, down, left, and right. To recover missing G in R and missing G in B, we use the derived weightings and the pixel contributions from the neighboring pixel information. When demosaicking the R and B arrays, an interpolation is used for the recovery of the missing R in B and missing B in R. The experimental results show that the proposed overall system outperforms the existing works. The proposed work is especially better than the state-of-the-art method by 1.23 dB in composite peak signal-to-noise ratio (CPSNR) and by −0.15 in a Spatial Extension to the CIE L*a*b* DeltaE Color Difference Metric (S-CIELAB), the lower the better, averaged over a standard image dataset.

A 4K/60p HEVC Real-Time Encoding System With High Quality HDR Color Representations

This paper describes a 4K/60p high efficiency video coding (HEVC, the latest coding standard) real-time encoding system with high quality high dynamic range (HDR) color representations. The HDR technology provides attractive video contents with a wider dynamic range of luminance, which can make shadows and brighter details appear clearly without any blown out highlights or blocked up shadows. We propose a new rate control method for HDR video coding. The method consists of two algorithms for improving perceptual quality by reducing degradation of HDR-specific color artifacts. The first one, adaptive block size and prediction mode decision, is conducted to suppress perceptual degradation due to prediction error in high chroma areas. The second, local quantization parameter control, is carried out to improve the visual quality with appropriate bit allocation in low chroma areas. The method was implemented in an HEVC real-time encoding system and its performance was assessed by measuring the color difference metrics by 3.86% at the most degraded frame, while peak signal-to-noise ratio remained almost the same. The subjective quality of the decoded HDR images was improved by reducing color artifacts.

Color Image Modification with and without Hue Preservation

Color image modification is an essential component for several applications and the grayscale transformation is generally mapped to the color image indirectly. Although several techniques have been used for this transfer, they suffer from gamut mapping issue. In this paper, it is aimed to study the mapping of grayscale transformations to the color scale in different perspectives. Modifying the image in different color space than the original retains hue to a promising extent, but suffers from the gamut problem. A generic scheme to map grayscale changes to the color space for all kinds of spatial modification is proposed here. The hue preserving color image enhancement (HPCE) scheme discussed here is free of gamut-mapping issue and shows promising results in transferring the grayscale transformation to the color image in a simplistic manner. The proposed HPCE scheme is analysed qualitatively through visual appearance and quantitatively using color difference metrics SHAME and CID, gray image difference and EBCM measures. Different gray scale transformations such as S-type enhancement and different forms of histogram equalization techniques are applied on Berkeley dataset of 500 images to prove the efficacy of proposed algorithm.

Hitting the Mark—A New Color Difference Metric for HDR and WCG Imagery

With the emerging demand for high dynamic range (HDR) and wide color gamut (WCG) technologies, display and projector manufacturers are racing to extend their color primaries in the cinema and in the home. With these brighter and wider colors, the question is: in calibration, how close is close enough? This answer is increasingly important for both the consumer and professional display/projector market as they balance design trade-offs. With HDR/WCG technology, an increasing issue is that many of the color difference metrics in common use today, such as $\Delta E_{00}$ , substantially deviate from human perception and become unreliable for measuring color differences. This causes under and over prediction of color differences and can lead to suboptimal design decisions and difficulties during calibration. There is a large amount of perceptual color difference data in the field today; however, the majority was collected using reflective surfaces and very little reaches the boundaries of modern display capabilities. To provide a better tool for facilitating design choices, this paper will present a ground truth visible color difference data set. These visual experiments were conducted between luminance levels of 0.1 and 1000 cd/m 2 on HDR laser cinema projectors with approximate BT.2100 color primaries. We present our findings, compare against current metrics, and propose specifying color tolerances using the $\Delta $ IC T C P metric for HDR and WCG imagery.

A novel method to evaluate the closeness of two colors

In order to reproduce an image on different color reproduction apparatus, it is important to match their color appearance. Most of the color difference metrics are suggested to use when the color differences are relatively small. We would need a new metric, which can well express large color differences. We assume each color has several “corresponding colors”, which give the impression or the appearance to be identical or very close. If those colors form a trend-line, it would be possible to evaluate how far a given color is from its trend-line instead of direct comparison. We conducted an experiment to find the trend-lines, and it turned out that the behaviour showed similar trends as the constant hue loci. Then we tried to find the “closest” color on the trend-line with the reference color off trend-line. The selected colors on the trend-line would be applicable to use as an intervening color. Our results indicated that the intervening color is not always the closest point from the reference color.

Influence of Gender on Visual Shade Matching in Dentistry.

Accuracy on visual shade matching (VSM) is one of the most difficult procedures in esthetic dentistry. Some variables can influence on this process and gender is one of the most controversial. This observational study evaluated the influence of gender on accuracy of VSM. One-hundred observers (65 females and 35 males) shade matched four human upper central incisors (UCI) to shade tabs from Vita Classical (VC) shade guide. In addition, the spectral reflectance values from the four UCI and from the 16 VC shade tabs were measured using a spectroradiometer (SP). Measurements were performed over a gray background, inside a viewing booth and under D65 illuminant (diffuse/0º geometry). CIELAB coordinates (L*, a*, and b*) were calculated according to CIE D65 illuminant and CIE 2º Standard Observer. Instrumental shade matching for each UCI, based on minimum color difference, was performed using two color difference metrics (CIELAB and CIEDE2000(2:1:1)). Data was analyzed using descriptive statistics and McNemar test (α = 0.05). Irrespective of the color difference metric, the most prevalent shade performed by females showed greater success (50% for CIELAB and 100% for CIEDE2000(2:1:1)) than males (25% for CIELAB and 50% for CIEDE2000(2:1:1)). However, such difference on gender was statistically significant only when using CIEDE2000(2:1:1) color difference metric (p ≤ 0.05). The most prevalent shade option resultant from VSM performed by females using VC shade guide perfectly agreed (100% success rate) with measurements from SP and CIEDE2000(2:1:1) color difference metric. The ability to understand and distinguish color differences in VSM is critical in clinical dentistry. Variation in shade perception due to observer gender can be minimized using additional observers and/or improving shade matching ability. (J Esthet Restor Dent 29:E15-E23, 2017).

Lightness, chroma and hue differences on visual shade matching

ObjectivesTo analyze the influence of lightness, chroma and hue differences on visual shade matching performed by dental students. Methods100 dental students (DS) volunteers with normal vision participated in the study. A spectroradiometer (SP) was used to measure the spectral reflectance of 4 extracted human upper central incisors (UCI 1-4) and shade tabs from Vita Classical (VC) and Vita Toothguide 3D-Master (3D) shade guides. Measurements were performed over a gray background, inside a viewing booth and under D65 illuminant (diffuse/0° geometry). Color parameters (L*, a*, b*, C* and h°) were calculated. DS used VC and 3D to visually select the best shade match for each UCI. CIE metric differences (Δa*,Δb*,ΔL′, ΔC′ and ΔH′) and CIEDE2000(2:1:1) lightness (ΔEL), chroma (ΔEC) and hue (ΔEH) differences were obtained from each UCI and the first three shades selected by DS and the first option using CIELAB, CIEDE2000(1:1:1) and CIEDE2000(2:1:1) color difference metrics. The closest CIELAB color-discrimination ellipsoid (from RIT-DuPont visual color-difference data) to each UCI was selected for the analysis of visual shade matching. ResultsDS showed a preference for shades with lower chroma (ΔC′ and ΔEC) and/or hue (ΔH′ and ΔEH) values instead of shades with lower lightness values (ΔL′ and ΔEL). Most best visual matches were near the tolerance ellipsoid centered on tooth shade. SignificanceThis study is an attempt to partially explain the inconsistencies between visual and instrumental shade matching and the limitations of shade guides. Visual shade matching was driven by color differences with lower chroma and hue values.