Interview Questions for Color Scripting

CIE XYZ and sRGB are both color spaces, but they serve different purposes. CIE XYZ is a device-independent color space, meaning it’s a mathematical model representing all perceivable colors. Think of it as the universal language of color. It’s not directly displayable on a screen, but it’s the foundation upon which other color spaces are built. sRGB, on the other hand, is a device-dependent color space designed for screens and printers. It defines a specific subset of colors that can be reliably reproduced on common devices. Imagine it as a translation of the universal language into something your monitor understands.

The key difference lies in their gamut – the range of colors each can represent. CIE XYZ encompasses a far wider gamut than sRGB. sRGB is a smaller, practical subset of CIE XYZ that’s designed for common display technologies. Converting from CIE XYZ to sRGB often involves gamut mapping, where out-of-gamut colors are adjusted to the closest representable color within the sRGB space.

My experience with color profiling and calibration is extensive. I’ve worked with a variety of tools, including X-Rite i1Display Pro and Datacolor SpyderX, to create ICC profiles for monitors and printers. Calibration ensures consistency across different devices and projects. For example, I once worked on a project involving high-fidelity image reproduction for a museum exhibit. Accurate color profiling of the display screens was crucial to ensure that the images appeared true to the originals. Without proper calibration, minor color shifts could have rendered the exhibit’s images inaccurate and less impactful.

The process usually involves measuring the color output of a display or printer using a colorimeter or spectrophotometer, then generating an ICC profile that compensates for any deviations. This profile is then embedded in image files or applied as a system profile to ensure consistent color representation across software and hardware. I regularly perform calibration checks to maintain accuracy. This is often a crucial part of managing colour in workflow pipelines where accuracy is vital.

Color inconsistencies across platforms stem from different color spaces, profiles, and display capabilities. Addressing this requires a multi-pronged approach. First, I always aim for a consistent workflow using a standardized color space, preferably a wide-gamut space like Adobe RGB or ProPhoto RGB for initial creation, and converting to sRGB only for final output for web or print. Second, I carefully manage color profiles, embedding them in images and leveraging software that supports profile management. Third, I always conduct thorough testing on target devices and platforms to catch any discrepancies. For web applications, I rely on tools and frameworks that provide color management capabilities. Consider this situation: you’re designing a website with vibrant colors; it looks perfect on your calibrated monitor, but dull on a friend’s laptop. This is where color space conversion and careful profile embedding becomes crucial.

Common challenges in color space conversion include gamut mapping (handling colors outside the target space), potential loss of information during conversion, and inaccurate transformations leading to color shifts. To address these: I use high-quality conversion algorithms (e.g., those found in Adobe products or OpenColorIO), carefully select the conversion method (perceptual, relative colorimetric, absolute colorimetric), and always visually inspect the results. A common issue is converting from a wide-gamut space like Adobe RGB to sRGB – colors might appear desaturated or shifted in the final output. Addressing these requires understanding the implications of different conversion methods and using appropriate techniques like gamut compression to minimize information loss.

Gamma correction is a crucial aspect of color management that addresses the non-linear relationship between input and output light levels in displays. Essentially, human perception of brightness is logarithmic, not linear. A gamma value (typically around 2.2) adjusts the brightness levels to match our visual perception, resulting in a more accurate representation of colors. Without gamma correction, images might appear too dark or too bright, depending on the device’s characteristics. Imagine a grayscale ramp: without gamma correction, the darker shades would appear too dark, and the lighter shades too light, resulting in a compressed and inaccurate image. Therefore, proper gamma correction ensures images appear true to life across various screens and printing processes.

My preferred tools for color scripting are Python with libraries like OpenCV and scikit-image, combined with the OpenColorIO (OCIO) library for advanced color transformations. I also use specialized scripting capabilities within Adobe Photoshop and After Effects for automating color adjustments and batch processing. These tools offer flexibility and control over complex color manipulations, allowing me to automate tasks and ensure consistent color across large datasets or projects. Python’s versatility and extensibility allow creating highly customized solutions and integration with other workflows.

I have extensive experience working with color metadata, including embedding ICC profiles in images using tools like Adobe Photoshop and specialized command-line utilities. Understanding and utilizing color metadata is vital for ensuring consistent color reproduction across different devices and software. Embedding an ICC profile provides the necessary information for a display or printer to accurately interpret the colors in an image. I regularly review and manage the color profiles associated with assets. For example, in a client project involving archival images, embedding correct profiles was crucial for long-term preservation of color accuracy – ensuring the images retain their fidelity even decades later.

Troubleshooting color issues in a complex pipeline requires a systematic approach. Think of it like detective work – you need to isolate the source of the problem. I begin by carefully examining the entire pipeline, from source image acquisition to final output. This often involves checking each stage individually.

• Visual Inspection: A close visual comparison of the input and output can quickly highlight discrepancies. Look for unexpected color shifts, banding, or artifacts.
• Intermediate Checks: Insert checkpoints at various stages of the pipeline to inspect intermediate results. This helps pinpoint the exact stage where the color problem is introduced.
• Instrumentation: Use color analysis tools and colorimeters to perform objective measurements at different stages, comparing numerical values to identify deviations from the expected color space.
• Isolation: If the issue isn’t immediately apparent, try isolating sections of the pipeline. By disabling or bypassing parts of the process, you can systematically determine the culprit.
• Testing with Known Inputs: Use test images with known color characteristics. This provides a controlled environment to determine if the issue is related to specific image properties or a problem within the pipeline itself.

For instance, I once worked on a project where subtle color shifts appeared after a particular color grading step. By inserting a checkpoint just before and after this step, we discovered a small bug in the grading algorithm causing a slight skew in the color matrix. Addressing this bug solved the color issue.

Color transforms are fundamental to image processing, enabling adjustments to brightness, contrast, saturation, and hue, and conversions between color spaces. They can be broadly categorized as follows:

• Linear Transformations: These involve multiplying the color vector by a matrix. Examples include rotation of color space axes, scaling of individual color channels (adjusting brightness, contrast), and simple linear combinations of channels.
• Non-linear Transformations: These are more complex, often involving power functions or lookup tables. Examples include gamma correction (adjusting for the non-linear response of display devices), color grading curves, and tone mapping (for HDR images).
• Color Space Conversions: These transformations convert between different color spaces (e.g., RGB to XYZ, Lab to LCh). They require accurate mathematical formulas to maintain colorimetric accuracy.
• Color Matrix Transformations (CMT): These are used to compensate for differences in color reproduction between devices. They involve a 3×3 matrix that maps colors from one color space to another.

A simple example of a linear transformation is increasing brightness. In RGB, you might add a constant value to each R, G, and B channel.

Colorimetric measurements involve objectively quantifying the color characteristics of a light source, a display, or a printed sample. This contrasts with subjective visual assessment. Accurate measurements are crucial for color management and ensuring consistency across the pipeline.

Instruments used for colorimetric measurements include:

• Spectrophotometers: These instruments measure the spectral reflectance or transmittance of a sample. This data can then be used to calculate colorimetric values like CIE XYZ or Lab.
• Colorimeters: These measure the tristimulus values (X, Y, Z) directly. They are generally less expensive than spectrophotometers but provide less detailed spectral information.
• Densitometers: Primarily used in printing, these measure the optical density of inks, providing information relevant to color reproduction.

The data obtained from these instruments is typically reported using standard color spaces like CIE XYZ, Lab, or LCh, providing numerical representations of color that can be compared objectively. This ensures consistency and predictability in color reproduction across different devices and workflows.

Optimizing a color pipeline for performance requires a multi-pronged approach, focusing on both algorithm efficiency and data representation.

• Algorithmic Optimization: Efficient algorithms are crucial. Explore using optimized libraries or writing custom functions using vectorization techniques or parallel processing where applicable.
• Data Representation: Using smaller color data types (e.g., 8-bit instead of 16-bit) where the color depth allows, can drastically reduce memory footprint and processing time. Consider using compressed color formats.
• Cache Optimization: Data locality is crucial. Structure your code and data to minimize memory access times. Utilize caching mechanisms where appropriate.
• GPU Acceleration: Leverage the parallel processing power of GPUs to accelerate computationally intensive color transformations, especially for tasks involving large images or videos.
• Profiling: Before optimization, profile the pipeline to identify performance bottlenecks. This allows you to focus your efforts on the most critical areas.

For example, in a high-resolution image processing pipeline, converting from 16-bit to 8-bit color representation before applying a color grading algorithm can result in significant performance gains without a noticeable loss in visual quality.

I have extensive experience with color space transformations, particularly those involving XYZ and Lab. These transformations are essential for color management and are frequently used in image processing and color science.

XYZ to Lab: The XYZ color space is device-independent, representing colors based on human perception. The Lab color space is perceptually uniform, meaning that a small numerical difference in Lab values corresponds to a small perceived difference in color. This makes Lab useful for color difference calculations and color appearance modeling.

The transformation from XYZ to Lab involves a series of non-linear steps:

• Normalization: The XYZ values are typically normalized to a white point.
• Non-linear Transformation: Specific functions are applied to the normalized XYZ values to obtain the L*, a*, and b* components of Lab.

I’ve used these transformations numerous times for applications such as color grading, color difference calculations, and gamut mapping. A key consideration is ensuring that the transformation is performed with accurate and well-defined mathematical functions to maintain colorimetric accuracy.

Different color encodings offer trade-offs between various factors such as color accuracy, file size, computational cost, and hardware support.

• RGB: Widely used, simple to implement, but device-dependent and not perceptually uniform. The choice of RGB variant (sRGB, Adobe RGB, etc.) significantly impacts color accuracy.
• CMYK: Used primarily for printing, based on subtractive color mixing. Limited color gamut compared to RGB.
• XYZ: Device-independent, forms the basis for many other color spaces. However, it’s not perceptually uniform.
• Lab: Perceptually uniform, making it ideal for color difference calculations. However, it’s more complex to work with than RGB.
• YCbCr/YUV: Often used in video and image compression, separating luminance (Y) and chrominance (Cb, Cr) components. This allows for efficient compression by subsampling the chrominance channels.

For example, while RGB is straightforward for display, its device dependence necessitates color profiles for consistent reproduction. Lab, while more computationally demanding, provides superior color accuracy and consistency across devices when used in conjunction with color management systems.

Color gamut mapping addresses the issue of colors that are present in a source image but fall outside the reproducible gamut of a target device (e.g., a printer or monitor). It’s like trying to fit a square peg into a round hole – the solution is to modify the shape of the peg to best fit the round hole.

There are several techniques for gamut mapping:

• Clipping: The simplest method – simply clipping colors outside the target gamut to the closest in-gamut color. This can lead to noticeable color shifts and loss of information.
• Compression: Scales the gamut of the source image to fit within the target gamut. This maintains relative color relationships but may reduce overall color saturation.
• Perceptual Mapping: This more sophisticated approach attempts to minimize the perceived color difference between the original color and the mapped color. Algorithms like those based on CIECAM02 or other color appearance models are used.

Choosing the appropriate gamut mapping technique depends on the application and the importance of preserving color fidelity. For critical applications, perceptually based methods are preferable, although computationally more demanding. Simple clipping is acceptable for cases where color accuracy is less crucial.

Ensuring color accuracy in a High-Dynamic Range (HDR) workflow requires meticulous attention to detail throughout the entire pipeline. HDR images contain a far wider range of brightness levels than Standard Dynamic Range (SDR) images, making accurate representation crucial. This involves careful monitoring of several factors.

• Color Space and Encoding: Working in a wide gamut color space like Rec.2020 is essential. Using appropriate encoding methods like PQ (Perceptual Quantizer) or HLG (Hybrid Log-Gamma) correctly maps the image’s luminance and chrominance information for display. Incorrect encoding will result in washed-out or clipped highlights and shadows.

• Display Calibration: Your monitor needs to be meticulously calibrated using a colorimeter to ensure it accurately represents the HDR content. This involves setting peak brightness, black level, and white point according to HDR standards. A poorly calibrated monitor can lead to inaccurate color perception and grading decisions.

• Metadata: HDR metadata, like Dolby Vision or HDR10+, contains crucial information about the image’s dynamic range and color characteristics. It’s crucial to ensure this metadata is correctly embedded and interpreted across the entire pipeline, from capture to final output. Failing to maintain metadata integrity can result in significant color inaccuracies.

• Reference Monitors: Relying on a professional-grade HDR reference monitor is crucial for objective color assessment. These monitors are designed with accurate color reproduction and high peak brightness for faithful HDR rendering.

Think of it like painting a landscape – in SDR you have a limited palette; with HDR, you have a vastly expanded range of colors and brightness, demanding more precise control to avoid muddy colors or blown-out highlights.

My experience with color-managed workflows spans various applications, notably Photoshop and Nuke. Both require a different approach but share the underlying principle of consistent color space management.

• Photoshop: I extensively use Photoshop for image editing and retouching. I always begin by embedding the correct color profile (e.g., Adobe RGB, ProPhoto RGB) based on the source image and intended output. I meticulously manage the working space to avoid color shifts during processing. I leverage tools like adjustment layers with clipping masks to ensure non-destructive editing and precise color control.

• Nuke: In Nuke, a node-based compositing software, I work with OpenEXR files, a format that preserves high-dynamic range and color information. I leverage color spaces like ACES (Academy Color Encoding System) for pipeline consistency. Nuke’s built-in color management tools are essential, particularly color spaces transforms and OCIO (OpenColorIO) configurations, for accurate color transformations between different parts of the pipeline.

A key difference lies in the non-destructive nature of Nuke’s workflow compared to Photoshop’s potential for destructive edits. This necessitates a more structured approach in Nuke to ensure color consistency and revisability across multiple shots and sequences.

Handling color variations under different lighting conditions requires understanding the impact of light on color perception. This involves both practical and technical approaches.

• White Balance: Accurate white balance is fundamental. Different light sources (daylight, tungsten, fluorescent) emit different color temperatures. Correct white balance ensures accurate color representation across various lighting conditions. In post-production, tools for white balance adjustment are crucial to neutralize color casts.

• Color Temperature Adjustment: Subtle color adjustments might be needed to account for ambient light color shifts. Tools like color temperature controls in editing software allow compensating for cool or warm lighting casts. This helps maintain color consistency throughout a project.

• Exposure Control: Proper exposure is critical; underexposed footage might exhibit muted colors, while overexposed footage may result in blown-out highlights. This necessitates careful exposure metering during acquisition and strategic exposure adjustments during post-production.

• LUTs (Look-Up Tables): LUTs can help standardize color across different lighting conditions. A properly created LUT can transform the color of footage shot under various lighting scenarios to achieve a consistent look. For example, a LUT could transform footage from a nighttime scene to align with the color palette of scenes shot during the day.

Imagine photographing a red flower – under direct sunlight, it might appear brighter and slightly different than under shade. Managing these subtle variations is key to achieving consistent and natural-looking color.

Color grading and look development are integral parts of my workflow. It’s about more than just color correction; it’s about shaping the visual narrative and establishing a particular mood and style.

• Understanding the Story: I always begin by understanding the overall tone and style of the project. The color palette should serve the story, not the other way around.

• Reference Images: I often use reference images or film stills to establish a desired visual style. This allows me to effectively communicate the intended look to clients and collaborate on refining the color palette.

• Iterative Process: Color grading is an iterative process involving numerous adjustments, experimenting with different color palettes, and continually refining the look until the final desired outcome is achieved.

• Tools and Techniques: I use a range of tools such as curves, color wheels, and primary/secondary color correction tools. I leverage powerful grading applications such as DaVinci Resolve to achieve the necessary precision and control over the final product.

A good analogy is a painter choosing their color palette – they don’t just pick colors randomly, they select hues that evoke specific emotions and support the overall theme of the artwork. Color grading is a similar art form of crafting an emotional visual narrative.

Several color-related artifacts can negatively impact the final image quality. Understanding these artifacts and their causes is crucial for prevention.

• Color Banding: This appears as distinct horizontal or vertical bands of color, often in gradients. It’s often caused by insufficient bit depth or improper compression. Solutions include working with higher bit-depth images (e.g., 16-bit) and using lossless compression formats like OpenEXR.

• Color Clipping: Clipping occurs when pixels exceed their maximum or minimum luminance values, leading to loss of detail in highlights (blown-out whites) or shadows (crushed blacks). Proper exposure control and dynamic range management are crucial to avoid this. Utilizing tools like histograms and waveform monitors allows for careful monitoring and adjustment.

• Color Fringing (Chromatic Aberration): This manifests as colored halos around high-contrast edges, particularly noticeable in areas with strong light sources. It’s often a lens issue but can sometimes be reduced in post-production using specialized tools that detect and correct the color fringes.

• Posterization: This is a loss of subtle color gradations, creating a poster-like appearance. It typically stems from poor color quantization. Working with high bit-depth images and avoiding aggressive color compression mitigates this problem.

Think of these artifacts as imperfections in a painting – color banding is like visible brushstrokes where they shouldn’t be, clipping is like paint that’s too thick or too thin, and posterization is akin to a loss of detail and nuance.

My experience encompasses various color-related file formats, each suited to different needs and workflows.

• OpenEXR (.exr): This is my preferred format for high-dynamic-range images, particularly in VFX and compositing workflows. It supports high bit depths, multiple channels, and lossless compression, preserving color information accurately.

• TIFF (.tif/.tiff): TIFF is a versatile format suitable for both SDR and HDR images. It offers lossless compression and supports various color spaces. It’s widely compatible, making it a reliable choice.

• JPEG (.jpg/.jpeg): JPEG is a lossy compressed format commonly used for web and print applications. While convenient, it involves some color information loss, making it unsuitable for projects demanding high color accuracy.

Choosing the right format is crucial for workflow efficiency and maintaining image quality. Think of them as containers – an OpenEXR is a large, robust container preserving all the color detail, while a JPEG is a smaller, more streamlined container that sacrifices some fidelity for space efficiency.

Version control in a collaborative color workflow is crucial for maintaining consistency and enabling efficient collaboration. My typical approach involves a combination of strategies.

• Cloud-Based Storage: Storing project files on a cloud-based platform (e.g., Google Drive, Dropbox) allows multiple team members to access and work on files simultaneously. This also enables easy version tracking.

• Versioning Software: Using version control software like Git (often integrated through platforms like ShotGrid) enables tracking specific changes made to color grades, allowing for easy rollback and comparison.

• Clearly Defined Naming Conventions: Implementing a standardized file-naming convention (e.g., including date, version number, and author) keeps the project organized and easily trackable.

• Regular Backups: Regular backups of project files safeguard against data loss, a crucial safety net in any collaborative project.

• Communication and Collaboration Tools: Leveraging communication and collaboration tools such as Slack or email facilitates timely updates and feedback sharing among team members. This ensures everyone is aware of any changes and can actively participate in refining the color workflow.

Collaborating on color grading is like building a house together – proper planning, clear communication, and organized workflows are key to successfully creating a finished product that meets everyone’s expectations.

Colorimetric calculations form the foundation of color science, allowing us to quantitatively describe and manipulate colors. They involve mathematical transformations between different color spaces and models, predicting how colors will appear under varying conditions. For example, we use them to convert a color from its RGB representation (used in screens) to a CMYK representation (used in printing) or to predict how a color will appear under different illuminants.

These calculations are crucial in various applications. In image editing, they’re used for color correction and grading, ensuring consistent color appearance across different devices. In printing, accurate colorimetric calculations are essential for matching screen colors to printed output, avoiding significant discrepancies. In manufacturing, colorimetric calculations are used for quality control, ensuring that products meet specified color standards. The core principles involve understanding color spaces like CIE XYZ, Adobe RGB, and sRGB, and the transformations between them, often utilizing matrices and equations to perform the conversions.

For instance, consider converting an RGB color to XYZ. You’d use a specific 3×3 transformation matrix dependent on the RGB space’s characteristics. Similarly, you might use different equations and algorithms to calculate color differences (e.g., Delta E) to quantify the perceptual difference between two colors.

My experience in color correction encompasses a broad range of techniques, both manual and automated. I’ve extensively used tools like Adobe Photoshop, DaVinci Resolve, and Baselight for color grading and correction. My experience includes dealing with different types of color problems, such as color casts, poor white balance, and inconsistent exposure. I’m proficient in using various methods, including:

• White balance correction: Adjusting the color temperature to achieve a neutral white point.
• Color grading: Subtly adjusting color saturation, hue, and luminance to achieve a desired look and feel.
• Color matching: Aligning the colors of different shots or assets to ensure visual consistency.
• Gamma correction: Adjusting the brightness levels to match the display’s response curve.
• Using 3D Lookup Tables (LUTs): Applying pre-defined or custom LUTs for fast and consistent color transformations.

I’ve worked on projects across different mediums including film, photography, and animation. In one project, we used a combination of manual correction and LUTs to match the color of archival footage to newly shot scenes, ensuring a seamless transition between old and new material.

Ensuring color consistency across devices and screens requires a multi-pronged approach centered around color management. The key is to work within a standardized color space and profile. Instead of relying on device-dependent RGB values, we leverage a device-independent color space, like Adobe RGB or ProPhoto RGB, for editing and pre-press. These spaces offer a wider gamut than sRGB, allowing for more colors to be represented. Then, we profile each output device (printer, monitor) to create a color profile that maps the colors in the device-independent space to the device’s color capabilities.

This profile acts as a translator, ensuring that the colors are accurately rendered on each device. Soft proofing, a crucial step, allows us to preview how the final output will look on different devices using their associated profiles before the actual printing or display. Regular calibration of monitors is crucial to maintain consistent results over time. Software like Adobe Color and X-Rite i1Display Pro are valuable tools for this process. Additionally, understanding and controlling the viewing environment (lighting conditions) can further enhance color accuracy.

A Color Management System (CMS) is indispensable for maintaining color accuracy and consistency across the entire workflow. It provides a framework for managing color data from capture to output. The benefits are numerous:

• Consistent Color Across Devices: The primary advantage is the ability to produce consistent colors regardless of the device used – from camera to monitor to printer.
• Improved Accuracy: CMS helps to minimize color shifts and variations during different stages of production.
• Predictable Results: With accurate color profiles, it enables designers to more accurately predict how their designs will appear in the final output.
• Reduced Errors and Rework: By reducing color discrepancies, CMS minimizes the need for costly corrections and reprints.
• Enhanced Collaboration: Shared color profiles facilitate easier collaboration between designers, printers, and other stakeholders.

Think of a CMS as a central authority for color, providing a common language that different devices understand. Without a CMS, color variations can creep in at each stage, leading to frustration and inconsistencies.

Evaluating the quality of a color workflow involves a series of checks at different stages. This isn’t solely about the final output; it’s a holistic assessment that starts at the beginning of the process.

• Input Assessment: Checking the color accuracy and dynamic range of the input device (e.g., camera, scanner) through test charts and analysis software.
• Profile Validation: Verifying the accuracy of color profiles used for monitors and printers using tools like spectrophotometers and colorimeters.
• Soft Proofing & Simulation: Thoroughly soft-proofing designs to see how they will look on various devices before committing to final output.
• Output Evaluation: Comparing the final output against reference images or color standards using spectrophotometers for objective color measurement.
• Visual Inspection: A crucial human element, involving visual inspection for subtle color differences and anomalies that might be missed by purely numerical analysis.

A well-functioning workflow demonstrates minimal color differences between the initial intent and the final output across various devices. The use of objective color measurement tools and controlled viewing conditions are vital for reliable evaluation.

In a recent project involving the restoration of vintage film reels, we encountered a significant color instability issue. The film had faded unevenly over time, leading to wildly different color temperatures and saturation across different sections of the same reel. Manual correction proved extremely time-consuming and yielded inconsistent results.

My solution involved a multi-step approach. Firstly, I carefully analyzed the film, identifying areas of consistent color and using them as references. Next, I employed a combination of software techniques, including advanced masking and color grading tools, to gradually correct the color inconsistencies in the individual sections. I leveraged color spaces that allowed a wider gamut for correcting the faded colors. A key element was creating custom LUTs based on the reference colors to ensure consistent application of the correction across the entire footage. Finally, I implemented a quality control process involving both automated analysis and visual reviews to ensure that the restored footage had the intended visual appeal while maintaining historical accuracy. The end result was a beautifully restored film with significantly improved color consistency.

Perceptual uniformity in color spaces refers to the arrangement of colors in a way that reflects how humans perceive color differences. In a perfectly perceptually uniform space, a small numerical difference between two color coordinates translates to a small perceived difference between those colors, regardless of where in the color space they are. This is vital because our eyes don’t perceive color differences linearly. For example, a small shift in blue might look drastically different compared to the same numerical shift in red.

Several color spaces aim for perceptual uniformity, with varying degrees of success. CIELAB (L*a*b*) is a widely used color space designed to provide better perceptual uniformity than older spaces like XYZ. However, even CIELAB has some limitations, especially at the edges of the color gamut. Delta E, a metric for calculating color differences, is often used in conjunction with perceptually uniform spaces, providing a more accurate measure of the perceived difference between colors. The pursuit of better perceptual uniformity continues to be an area of active research in color science.