Interview Questions for Color Appearance

Spectral power distribution (SPD) and color appearance, while related, are distinct concepts. SPD is the physical description of light, specifying the amount of power emitted or reflected at each wavelength across the visible spectrum (roughly 400-700nm). It’s like a fingerprint of the light source or object. Think of a rainbow – each color represents a specific wavelength, and the SPD tells us the intensity of each color present. Color appearance, on the other hand, is the subjective perception of color by a human observer under specific viewing conditions. Two objects might have different SPDs but appear the same color to our eyes due to the complexities of our visual system. For example, a fluorescent light and an incandescent light might have very different SPDs but appear to produce a similar ‘white’ light under certain conditions.

The CIE XYZ color space is a cornerstone of colorimetry. It’s a three-dimensional space where each color is defined by three tristimulus values: X, Y, and Z. These values represent the relative amounts of three hypothetical primary colors needed to match any given color. Y is particularly important as it represents luminance (brightness). The CIE XYZ space provides a mathematically consistent way to represent and quantify colors, making it a crucial tool for color management. However, it has limitations. Firstly, it’s not perceptually uniform – equal distances in XYZ space don’t correspond to equal perceived color differences. A small change in XYZ values might lead to a noticeable color difference in one region of the space, but an insignificant difference in another. Secondly, XYZ values don’t directly reflect how humans perceive color; they are a mathematical construct. This is why other, more perceptually uniform color spaces have been developed.

Both L*a*b* and L*C*h* are derived from CIE XYZ but offer different ways to represent color. L*a*b*, also known as CIELAB, uses a three-dimensional space where L* represents lightness (0 being black, 100 being white), a* represents the green-red axis, and b* represents the blue-yellow axis. L*a*b* is approximately perceptually uniform, meaning that small changes in L*, a*, and b* values correspond to roughly equal perceived color differences. This is crucial for applications like color tolerance specifications. L*C*h*, on the other hand, uses a cylindrical coordinate system. L* remains the same, representing lightness. C* represents chroma (color saturation), and h represents hue (the pure color). L*C*h* is advantageous when dealing with color changes in saturation and hue, because it separates these attributes, making it easier to understand and manipulate color variations. Imagine you’re adjusting the color of a paint. L*a*b* is helpful for precise adjustments across the entire color spectrum, whereas L*C*h* is intuitive for controlling the vibrancy (C*) and the specific shade (h) separately.

Metamerism is a fascinating phenomenon where two colors appear identical under one lighting condition but different under another. This occurs because our eyes have only three types of cone cells, which means many different spectral power distributions can stimulate these cells in the same way, resulting in the same color perception. These two colors with different SPDs, but identical appearance, are called metamers. Metamerism significantly affects color matching because what looks perfect under one light source (e.g., daylight) might look noticeably different under another (e.g., incandescent). For example, a fabric sample might perfectly match a target color under a specific light box, but when placed in a showroom with different lighting, the match is no longer visually acceptable. This highlights the importance of considering metamerism during color reproduction and quality control processes. Careful attention to spectral matching, rather than simple visual matching, is critical in many industries such as textiles and printing.

Color constancy is our visual system’s remarkable ability to perceive the color of an object as relatively consistent despite changes in illumination. Imagine a red apple. Whether you view it under sunlight or a dim lamp, you still perceive it as red. This is because our brains automatically compensate for the changes in lighting, filtering out the effects of the illumination and focusing on the object’s inherent reflectance properties. This is a complex process involving several factors, and it’s not perfect, but it enables us to effectively interact with our environment by maintaining consistent color perception across various lighting conditions. Without color constancy, our perception of the world would be dramatically different, constantly shifting with changes in light source.

Several factors influence color appearance, including:

• Light Source: The spectral power distribution of the illuminant drastically affects color perception. Different light sources (daylight, incandescent, fluorescent) cause colors to appear differently.
• Observer Characteristics: Individual variations in vision, age, and health affect color perception.
• Surrounding Colors: Simultaneous contrast, where the perceived color of an object is influenced by the colors surrounding it, plays a vital role. A grey patch looks different surrounded by blue versus yellow.
• Viewing Conditions: Factors like viewing angle, background, and adaptation to previous colors affect the perceived color.
• Object Reflectance: The spectral reflectance of the object dictates how it interacts with light and how it appears to the observer.

Understanding these factors is crucial for accurate color reproduction and communication across different environments and viewers.

CIECAM02 and CIECAM16 are advanced color appearance models that attempt to predict how a color will be perceived under various viewing conditions. They go beyond simple color spaces by incorporating parameters like illuminant type, background adaptation, and surround conditions. CIECAM02 was a significant improvement over previous models but has been superseded by CIECAM16, which offers improved accuracy and handles a wider range of viewing conditions. These models use complex mathematical equations to predict perceptual attributes like brightness, lightness, chroma, and hue, providing a more accurate representation of the human visual experience compared to simpler models. They are essential in fields requiring precise color reproduction, like digital imaging, printing, and display technologies. They provide a more robust and realistic basis for specifying color tolerances and maintaining color consistency across different devices and environments.

Illuminant variation is a significant challenge in color measurement because the light source drastically affects how a color appears. Think of a red shirt: under incandescent light, it might appear more orange, while under daylight, it looks truer to its red hue. We account for this using two main approaches.

• Colorimetric Transformations: We use mathematical transformations like Bradford or XYZ to adjust color measurements to a standard illuminant (e.g., D65, representing average daylight). This involves converting the measured color values (often in XYZ tristimulus values) from the actual illuminant to the reference illuminant. The software takes the spectral power distribution of the illuminant into account during the transformation.

• Spectral Measurements: For the most accurate results, we use spectrophotometers that measure the spectral reflectance or transmittance of the object. This provides a complete description of how the object interacts with light across the visible spectrum. We can then calculate the color appearance under any desired illuminant using this spectral data.

For example, if a textile manufacturer is comparing fabric samples, spectral measurement ensures accurate color matching regardless of the lighting conditions in different manufacturing plants or retail stores. Simply relying on visual assessment could lead to significant discrepancies.

A color gamut is the range of colors that a particular device or system can reproduce. Think of it as the color ‘vocabulary’ of that system. For instance, a computer monitor has a limited gamut compared to a professional-grade printer. This means there are some colors that the monitor can display, but the printer can’t, and vice-versa.

This impacts color reproduction because if you design a graphic with colors outside the gamut of your output device, those colors will be clipped or substituted. For example, if you design a vibrant turquoise that’s outside the monitor’s gamut, the monitor will display a duller approximation. Similarly, colors outside the printer’s gamut might be substituted with the closest possible color, leading to a shift in the overall appearance.

Proper color management involves understanding and working within the gamuts of all devices involved in the workflow. We often use color profiles (ICC profiles) which describe the gamut of a particular device and help software map colors correctly between different devices.

Color difference quantifies the perceptual difference between two colors. It’s not simply about comparing numerical color values; it accounts for the human eye’s non-linear perception of color. Delta E (ΔE) is a common metric for measuring color difference. Different formulas for calculating ΔE exist, such as ΔE76, ΔE94, ΔE00, and ΔEab, each with its strengths and weaknesses in terms of perceptual uniformity.

Delta E values represent the magnitude of the color difference. A smaller ΔE indicates a smaller, less noticeable color difference. For example, a ΔE of 1 might be barely perceptible, while a ΔE of 5 or greater is readily apparent to the average observer. The interpretation of ΔE values is context-dependent. For example, a ΔE of 1 might be acceptable for wall paint, but unacceptable for a high-precision print.

Different industries have different acceptable ΔE tolerances. For example, the automotive industry has very strict tolerances for color matching across batches of car paint, requiring much lower ΔE values compared to, for example, the printing of a book cover.

Color appearance models, such as CIECAM02 and CIECAM16, are crucial in various industries. They go beyond simple color measurement, incorporating factors like viewing conditions (illuminant, surround, adaptation) to predict how a color will actually appear to an observer. This is crucial for:

• Textiles: Ensuring consistent color reproduction across different fabrics and lighting conditions.

• Printing: Accurately predicting how colors will appear on different substrates (paper, plastic) under various viewing conditions.

• Automotive: Maintaining consistent color across vehicle parts and minimizing the perceived difference under varying light sources.

• Food and Beverage: Managing brand consistency across packaging and marketing materials, particularly crucial for products like chocolates or sodas where color perception is critical for consumer appeal.

• Digital Media: Optimizing images and videos for different display devices and lighting situations to maintain consistent color appearance across screens.

In essence, color appearance models allow businesses to create more accurate and consistent color experiences for their customers. They translate raw color measurements into a perceptually uniform metric, reducing the chances of undesirable color discrepancies and ensuring brand consistency.

Color discrepancies between devices stem from differences in their color gamuts and color reproduction capabilities. Handling these discrepancies requires a multi-pronged approach.

• Color Management Software (CMS): Employing CMS, like Adobe Color Management Modules, helps translate colors between different color spaces and devices based on ICC profiles, creating a standardized workflow.

• ICC Profiles: These profiles characterize the color capabilities of each device (monitor, printer, scanner). A good profile ensures consistent color translation between devices.

• Soft Proofing: This technique simulates the final output on screen, allowing you to verify color accuracy before printing or rendering. Soft proofing greatly reduces costly reprints and revisions.

• Calibration and Profiling: Regular calibration of monitors and profiling of printers are critical for maintaining accurate color reproduction.

• Standardized Color Spaces: Using a common color space like sRGB or Adobe RGB ensures a certain level of consistency across different devices. Though, it is important to remember that color gamut differences still exist.

For example, if I’m designing a website, I use a CMS to ensure the colors appear consistent across various monitors and browsers. If I’m printing a high-quality poster, I use soft proofing with the printer profile to verify color accuracy before committing to the final print.

Color quality control is paramount, ensuring consistent and accurate color reproduction throughout a product’s lifecycle. Without it, inconsistencies lead to customer dissatisfaction, brand damage, and financial losses.

In color-critical industries, color quality control involves:

• Regular instrument calibration: Spectrophotometers and colorimeters need regular calibration to ensure accurate measurements.

• Color tolerance standards: Setting acceptable ΔE tolerances for different applications ensures consistency across batches and devices.

• Colorimetric analysis and reporting: Regularly analyzing color data to identify trends and deviations, enabling prompt corrective actions.

• Color standards and reference samples: Maintaining a library of color standards and reference samples for easy comparison and verification. This is essential for ensuring consistency over time and across multiple production runs.

• Visual assessments: While instruments provide objective data, visual assessment by trained personnel remains vital for detecting subtle variations and unforeseen issues.

Imagine a paint company: rigorous color quality control ensures that each can of paint, regardless of the batch, matches the advertised color, maintaining brand consistency and customer satisfaction.

My experience spans various color management software and tools, including Adobe Color Management Modules, X-Rite i1Profiler, and Datacolor SpyderX. I’m proficient in creating and utilizing ICC profiles, performing colorimetric transformations, and analyzing spectral data. I’ve used spectrophotometers (e.g., X-Rite i1iSis, Konica Minolta spectrophotometers) extensively for accurate color measurement, and I’m familiar with color management software integrated into different applications like Adobe Photoshop, Illustrator, and InDesign.

I regularly use these tools for:

• Monitor calibration: Ensuring my monitor accurately represents colors.

• Printer profiling: Creating custom ICC profiles for various printers to optimize color reproduction.

• Colorimetric analysis: Quantifying color differences and assessing color consistency.

• Spectral data analysis: Using spectral data for precise color matching and predicting color appearances under different lighting conditions.

My expertise encompasses both the practical application and theoretical understanding of these tools, allowing me to effectively address complex color management challenges in diverse projects.

Achieving consistent color across different devices and production processes is a significant challenge because each device has its own unique characteristics affecting color reproduction. This involves variations in:

• Device technology: Monitors use different technologies (LCD, OLED, etc.), each impacting how colors are displayed. Printers utilize various inks and substrates (paper types), leading to color shifts.
• Color profiles: These profiles describe a device’s color capabilities. Inconsistent or missing profiles lead to inaccurate color translation.
• Ambient lighting: The surrounding light significantly alters how colors are perceived. A color that appears accurate in one lighting condition might look different under another.
• Manufacturing variations: Even within the same production batch, slight differences in ink composition or printing pressure can result in subtle color variations.

Imagine trying to match a specific shade of blue on a monitor, a printed brochure, and a billboard – the differences can be dramatic without proper color management.

Addressing color inconsistencies in production involves a multi-step approach focusing on measurement, standardization, and control:

1. Establish a color standard: This might be a physical sample (e.g., a Pantone swatch) or a digital reference (e.g., a color profile in a specific color space like Adobe RGB or sRGB).
2. Utilize color measurement instruments: Spectrophotometers are crucial for objective color measurement. They provide spectral data, allowing for precise comparisons between the standard and the actual printed output.
3. Implement a color management system (CMS): A CMS helps ensure that color data is consistently translated across various devices and stages of the production process.
4. Regular calibration and profiling: Monitors and printers should be calibrated frequently using dedicated calibration tools and software to maintain accuracy.
5. Control the environment: Maintain consistent lighting conditions throughout the color assessment process to minimize variations due to ambient light.
6. Establish tolerance levels: Define acceptable deviations from the color standard. This prevents over-correction, which can create its own inconsistencies.

For example, in a packaging production environment, we would measure the color of printed boxes against the standard using a spectrophotometer. Any discrepancies exceeding the defined tolerance would trigger adjustments to the printing process – perhaps a slight alteration to the ink formulation or printing pressure.

My experience encompasses a wide range of spectrophotometers, including both benchtop and handheld models. I’m proficient in using instruments from various manufacturers like X-Rite, Konica Minolta, and Datacolor. Each instrument offers unique features and capabilities:

• Benchtop spectrophotometers: These provide highly accurate and repeatable measurements. They are crucial for precise color quality control in labs and production environments. I’ve utilized these for characterizing inks, evaluating textile colors, and verifying color consistency in printed materials.
• Handheld spectrophotometers: These are portable and convenient for on-site measurements. While offering less precision than benchtop models, their portability makes them ideal for spot checking colors in the field or on the production floor.
• Spectrodensitometers: These instruments combine spectrophotometry with densitometry, providing spectral data along with density readings. This is particularly useful in the printing industry for monitoring ink density and color consistency.

The choice of instrument depends heavily on the application and desired level of accuracy. For critical applications requiring high precision, I would always opt for a benchtop spectrophotometer.

Calibrating a monitor or printer for accurate color reproduction is essential. It involves using specialized hardware and software to adjust the device’s color output to match a predefined standard.

Monitor Calibration: This is typically performed using a colorimeter or spectrophotometer combined with calibration software (e.g., Datacolor SpyderX, X-Rite i1Display Studio). The process involves measuring the monitor’s current color output and then adjusting its settings (brightness, contrast, white point, and individual color channels) to match a standard profile like sRGB or Adobe RGB.

Printer Calibration: Printer calibration is more complex. It often involves creating a color profile that maps the printer’s color output to a specific color space. This is usually accomplished using a spectrophotometer and specialized software. The software measures the printed color patches and generates a profile that the computer uses to translate digital color data accurately to the printer.

Regular calibration, usually every few weeks for monitors and more frequently for printers depending on usage, is crucial to maintain color accuracy. Imagine trying to edit photos on an uncalibrated monitor – your edits will likely be inaccurate and look different on other screens.

Spectral data, which represents the reflectance or transmittance of light at different wavelengths, is fundamental to color appearance modeling. It’s the raw data that describes the physical properties of a colorant. Color appearance models like CIECAM16 use spectral data as their input to predict how a color will appear under different viewing conditions.

The spectral data acts as a bridge between the objective physical properties of a material and its subjective perception by the human visual system. Without spectral data, our models would lack the precision necessary to account for the complex interactions between light, material, and the observer’s perception.

For example, two colors might appear the same under one light source but drastically different under another. Spectral data allows us to predict these changes and design colors that are perceptually consistent across different lighting scenarios.

Color spaces are mathematical models that describe colors numerically. They define a three-dimensional space where each point represents a unique color. Examples include:

• sRGB: The standard color space for the web and most consumer devices. It has a relatively small gamut (range of reproducible colors).
• Adobe RGB: A wider gamut color space commonly used in professional photography and printing. It covers a larger range of colors than sRGB.
• CIE XYZ: A device-independent color space serving as a reference standard. It’s used to convert between different device-dependent color spaces.

Color profiles are descriptions of a particular device’s color capabilities. They map the device’s color output to a specific color space, allowing for accurate color translation between devices. Without color profiles, the same digital color would likely render differently on various devices, leading to inconsistencies. Imagine sending a professionally designed brochure to a printer without a color profile – the printed output might bear little resemblance to the digital design!

Individual differences in color vision significantly impact color appearance assessment. Color blindness, for example, affects a substantial portion of the population, causing variations in how individuals perceive colors. Some individuals might have difficulty distinguishing certain hues, while others might perceive colors with different levels of saturation or brightness.

Moreover, even among individuals with normal color vision, subtle variations in perception exist. These variations are influenced by factors such as age, gender, and individual differences in the distribution of photopigments in the retina. Consequently, color appearance assessment should ideally consider the observer’s color vision characteristics. In professional settings, this might involve using color vision tests to identify any significant color vision deficiencies within the team involved in color assessment.

This necessitates the use of standardized color assessment methods and tools that minimize the influence of individual variations as much as possible. For example, using objective color measurement instruments (spectrophotometers) provides a standardized and repeatable way to assess color, reducing reliance on subjective perception alone.

Ensuring color accuracy throughout the design and manufacturing process requires a multi-faceted approach, starting from the very beginning. It’s like baking a cake – you need precise ingredients and meticulous steps to achieve the desired outcome.

• Standardized Color Spaces: We begin by defining the color in a standardized color space like CIELAB or CIECAM16. These provide a device-independent way to represent color, avoiding variations due to different monitors or printers. For example, specifying a color as L*a*b* = 60, -20, 30 ensures consistency regardless of the output device.

• Color Management System (CMS): A CMS is crucial for managing color across different devices and stages. This involves profiling each device (scanner, monitor, printer) to understand its color reproduction capabilities. This profile allows the CMS to accurately translate colors between devices, minimizing deviations.

• Color Communication: Effective communication is key. Using standardized color communication methods such as Pantone Matching System (PMS) color numbers for inks or digital color codes (hex codes) in web design helps avoid misinterpretations. A sample book with physical color swatches can further enhance accuracy.

• Quality Control: Regular color measurements throughout the process, using spectrophotometers and colorimeters, are essential. This allows for early detection and correction of any color discrepancies.

• Process Standardization: Establishing and adhering to strict manufacturing processes is vital for consistent results. This involves controlling variables like lighting, substrate, and ink formulation.

Solving a color mismatch requires a systematic approach, much like diagnosing a medical condition. First, we need to understand the nature and extent of the mismatch.

1. Identify the Source: Is the mismatch between the digital design and the printed output? Is it between different batches of the same product? Or is it a problem with perceived color under different lighting conditions?

2. Measure and Quantify: Using a spectrophotometer, we accurately measure the color of both the target and the mismatched sample, obtaining their L*a*b* values. This provides objective data to quantify the difference.

3. Analyze the Difference: The difference between the target and actual L*a*b* values helps pinpoint the issue. A large ΔE (delta E) value indicates a significant mismatch. We analyze which color components (L*, a*, b*) are most affected to identify the root cause.

4. Investigate Potential Causes: Possible causes include incorrect color profiles, inconsistencies in printing conditions (ink density, substrate, temperature), variations in lighting, metamerism (colors that match under one light source but differ under another), or even observer variations.

5. Implement Corrective Actions: Depending on the root cause, solutions can involve recalibrating equipment, adjusting the color profile, modifying the manufacturing process, or using a different substrate or ink. For example, if the problem is metamerism, we might need to specify a color that remains consistent across the relevant lighting conditions.

6. Verification: After implementing corrective actions, we re-measure the color to verify if the issue is resolved. This iterative process continues until the acceptable tolerance is met.

Simulating color appearance under different viewing conditions presents several challenges. It’s like trying to predict how a painting will look under different types of lighting – the results can be quite different.

• Metamerism: Two colors that appear identical under one illuminant might appear different under another. This makes it difficult to ensure a consistent color appearance across varying lighting conditions.

• Observer Variation: Individual observers may perceive colors differently due to variations in their visual systems. A color rendering simulation needs to account for this inherent variability.

• Illuminant Specificity: Accurately modeling the spectral power distribution of different illuminants (daylight, incandescent, fluorescent) is critical for realistic simulations. Slight inaccuracies in the illuminant model can lead to significant errors in the simulated color appearance.

• Computational Complexity: Calculating color appearance under different conditions using models like CIECAM16 requires significant computational power. This complexity increases with the number of illuminants and observers to be considered.

• Substrate Effects: The material on which the color is applied (e.g., paper, fabric, metal) can significantly affect color appearance. Accurately incorporating these effects into simulations is crucial but difficult.

Color rendering indices (CRIs) are crucial for evaluating the quality of light sources in relation to how accurately they render colors. I have extensive experience with various CRIs, including the most common, CRI Ra (general color rendering index), and specialized indices like CRI R₉ (red color rendering) relevant for specific applications.

• CRI Ra: This is a widely used index that provides an average of how well a light source renders eight test colors. A higher CRI Ra (0-100) indicates better color rendering. However, it has limitations, particularly in representing the rendering of saturated colors.

• CRI R₉: This index specifically assesses the color rendering of red, a color often poorly rendered by some light sources. This is critical in applications where accurate red representation is vital (e.g., food display, art galleries).

• Other Specialized CRIs: There are other specialized CRI values focusing on individual colors or color ranges relevant to specific applications. Understanding which CRI to use is crucial, as a high CRI Ra doesn’t automatically imply excellent color rendering across the entire spectrum.

• Application Considerations: The choice of CRI depends on the specific application. For example, in museum lighting, high CRI Ra and R₉ values are essential, while in a general office setting, a moderately high CRI Ra might suffice.

Color tolerance and specifications are fundamental to ensuring consistent color reproduction. Think of it like a recipe: we need precise ingredient measurements to achieve the desired outcome. This involves defining acceptable variations in color from a target value.

• Delta E (ΔE): ΔE quantifies the difference between two colors in a color space like CIELAB. It represents the ‘distance’ between the target color and the actual color. Acceptable ΔE values depend on the application; tighter tolerances (smaller ΔE) are needed for critical applications (e.g., automotive paint) than for less critical ones (e.g., packaging).

• Color Tolerance Ellipsoids: These define an acceptable range of color variation around a target color, accounting for variations in L*, a*, and b*. This provides a more nuanced representation of tolerance than a single ΔE value.

• Specification Standards: Various standards, such as those from ISO, define color tolerance specifications for different industries and applications. These provide a framework for defining and communicating acceptable color variations.

• Practical Application: For example, in textile manufacturing, color tolerances ensure consistency in the dyeing process across batches, while in plastic molding, they guarantee uniform color across different product runs.

Validating the accuracy of color measurement results is essential for maintaining color consistency. Think of it like calibrating a scale before weighing ingredients – you need to ensure the instrument is reliable.

• Instrument Calibration: Regular calibration of spectrophotometers and colorimeters using certified standards is critical. This ensures the instruments are providing accurate readings.

• Standard Reference Materials: Measuring certified color standards with known values allows for verification of instrument accuracy and detection of potential drift.

• Inter-instrument Comparison: Comparing measurements from multiple instruments can help identify potential inconsistencies or systematic errors.

• Repeatability and Reproducibility Testing: Repeated measurements of the same sample under the same conditions (repeatability) and by different operators using different instruments (reproducibility) assess the precision and reliability of the measurements.

• Operator Training: Properly trained operators are essential to minimize errors during measurements. Consistency in sample preparation, instrument handling, and measurement procedures is vital.

Troubleshooting a color-related issue in a specific industry requires understanding the unique challenges of that sector. Let’s consider the automotive industry as an example.

Suppose there’s a color mismatch in a car’s paint job. My approach would be:

1. Understand the Process: I’d first learn about the automotive paint process, including the types of paints, primers, and clear coats used, the application methods, the curing process, and the quality control procedures.

2. Gather Data: I would gather data on the color specification, the measurement results from different batches, and the manufacturing process parameters (temperature, humidity, application techniques).

3. Analyze the Discrepancy: Using spectrophotometric data, I would analyze the color difference (ΔE) between the target color and the actual color. I’d investigate whether the variation is across batches, specific areas of a vehicle, or consistently across vehicles.

4. Identify Potential Causes: Potential causes could include variations in paint formulation, application inconsistencies, environmental factors during the curing process, or variations in the substrate.

5. Implement Corrective Actions: This might involve adjusting the paint formulation, improving application techniques, controlling environmental conditions during curing, or employing better quality control measures.

6. Verification and Validation: After implementing corrective actions, I would perform color measurements to verify that the issue is resolved and that the resulting color conforms to the specified tolerance.

This systematic approach, adapted to the specific industry and its processes, would ensure a thorough and effective resolution to the color issue.