The world of color is a complex and fascinating realm, and the most effective means we have for defining it lies in the application of mathematics. This involves intricate mathematical models known as color spaces, which utilize geometric principles to assign colors to fixed points that we can reference. This ensures a shared experience of color; for instance, the blue that I perceive is the same shade of blue that you see. However, as someone who identifies more with creative pursuits than with intricate calculations, the sheer volume of math involved can be quite overwhelming.

Fortunately, advancements in computing software take on these complicated calculations, allowing us to rely on our visual instincts to select colors that we find appealing. Yet, there’s a caveat: we are faced with an extensive array of color spaces, each optimized for different tasks such as web design, photography, video editing, and physical printing. Choosing the wrong color space during any stage of creation, editing, or viewing can lead to significant discrepancies in color representation, affecting the final output.

This information can seem quite daunting. Thankfully, the majority of us only need a basic understanding of these concepts, which can be beneficial to everyone, not just creative professionals. Familiarizing oneself with color models and spaces can enhance your purchasing decisions when acquiring new devices like phones, TVs, laptops, or computer monitors, ultimately improving your viewing experience.

To begin, it is essential to grasp the distinctions between color models, color spaces, and gamuts. A color model refers to the entire system used to define how colors are represented. For instance, consider the following examples:

• CMYK (Cyan, Magenta, Yellow, Key): This color model operates on principles akin to the color wheel theory often encountered in art education. By mixing these colors—where “Key” generally represents black—in varying quantities, we can produce a broader spectrum of colors visible to the average human eye. This contrasts with the more rudimentary system known as RYB (Red, Yellow, Blue), which is based on a painter’s primary colors.

These models fall under what is termed “subtractive color mixing,” meaning they work by subtracting the amount of light that can pass through once colors are added, whether it be dyes, inks, or paints. This concept explains why paper typically appears white; printing and dyeing methods tend to look more vivid when applied to the lightest possible base. Interestingly, while CMY can produce black through repeated layering, incorporating black directly is more efficient for printers, preventing excessive ink usage that could lead to messiness.

• RGB (Red, Green, Blue): In contrast, this is an additive color model predominantly used in electronic devices. Rather than subtracting light from a white background, RGB adds varying frequencies of light to a black surface. Since black represents the absence of light, displaying true blacks became challenging until technological innovations like OLED emerged, allowing each pixel to possess its own miniature light source.

Now consider HSL / HSB / HSV (Hue, Saturation, Lightness / Brightness / Value): This model is likely familiar to anyone who has adjusted a webcam or utilized color pickers in programs such as Adobe Photoshop. While RGB is tailored for machine comprehension, HSL was designed with human readability in mind, facilitating easier manipulation of colors to achieve desired outcomes. In this model, hue denotes the color tone on a wheel measured in degrees, saturation assesses the intensity ranging from fully vibrant to grayscale, and lightness/brightness/value measures the extent between black (0%) and white (100%).

To visualize the differences among these models, consider several commonly used examples. It becomes evident what constitutes a color model: there are numerous methods to generate specific colors, but certain techniques lend themselves better to particular applications, be it dyeing fabric, printing labels, or color-correcting video footage. Think of them as different paths leading to the same destination.

Color spaces are derived from color models and define a particular range of displayable colors, often tailored to the limitations of their corresponding applications, such as the displays found on computer monitors and televisions. This necessity arises because colors can be likened to mathematical concepts; while it is possible to create an almost infinite number of subtle color variations, pursuing such changes would be labor-intensive and, in many cases, impractical, as some colors simply cannot be detected by the human eye. The CIE 1931 XYZ color space, developed to replicate colors based on human perception, has since formed the foundation for nearly every modern color space.

“Color spaces serve different purposes,” notes Eric Chan, a digital imaging fellow at Adobe. If it is feasible to plot virtually every color we can perceive, why not create a singular universal color space applicable to all scenarios? While theoretically possible, such a solution could hinder optimization across various applications, from website shades to color grading in television productions.

Take, for instance, sRGB, a color space developed collaboratively by HP and Microsoft in 1996. sRGB is now the standard color space for nearly everything on the web, providing a sensible range of colors that ensures consistency across numerous devices.

When editing a photograph intended for online sharing, sRGB is a solid choice. However, for professional printing, one might opt for Adobe RGB, another RGB-based space developed by Adobe boasting a broader color range that accommodates colors achievable through CMYK printing techniques. Alternatively, CIELAB (or Lab) offers a “perceptually uniform space” independent of devices, ensuring that coordinates used to specify colors will yield consistent results, making it a valuable tool for advanced color grading.

Moreover, video and display technology utilize specific color spaces such as Rec.709, Rec.2100, and DCI-P3. Rec.709 was established to delineate the color range achievable by high-definition televisions, while Rec.2100 represents a more modern, broader standard for ultra-high-definition TVs, HDR, and upcoming video technologies. DCI-P3, also referred to as Display P3 or simply P3, was designed for digital cinema applications, occupying a color range between Rec.709 and Rec.2100.

To ensure optimal experiences, the technology employed across these applications can differ significantly, necessitating the use of varying color tools. As Chan aptly explains, “My analogy is like a language. Yes, it’s possible to create a ‘universal language,’ but that doesn’t necessarily make it more useful or practical than the ones we already have.”

When discussing color, the concept of gamut becomes integral, as it measures a device's ability to display every color within a specific space. Gamut, defined as a range, is often represented as a percentage indicating how much of a color space a device can capture.

If you’ve recently purchased a computer monitor, you might have noticed specifications highlighting the percentage of various color spaces. For instance, the BenQ PD3225U Designer Monitor boasts 98% coverage of P3, 99% of sRGB, and 99% of Rec.709, whereas the more budget-friendly Dell S2425HS advertises only 99% coverage of sRGB. This information provides a quick reference for gauging the suitability of a display for specific creative tasks—if sRGB coverage surpasses P3, it suggests that the monitor is better suited for graphic design rather than video editing. However, it's worth noting that after a certain point, broader gamut coverage yields diminishing returns.

“Wider is better, but only to a point,” Chan elaborates. In terms of TV resolution, the transition from VGA to HD was significant, while the leap from HD to 4K is less perceptible. Similarly, the jump from 4K to 8K becomes even less noticeable for the average viewer. In relation to broader color spaces, higher dynamic range (HDR) is often more readily observable by most individuals compared to merely expanding the gamut.

Think of color gamut like frame rates—you can only increase them to a certain limit before you cease to notice any benefits. Although color spaces like OKLCH offer wider gamut support than sRGB, they require broader acceptance across displays and web applications to rival the current standard. For most applications, sRGB remains adequate.

Ultimately, this overview merely scratches the surface of the complexities involved in color display. Fortunately, the average person need not grasp all these intricacies because technical color work is not intended for them. Even professionals engaged in design often only need to familiarize themselves with the color spaces relevant to their specific fields. For the rest of us, we can rely on computers to handle the arduous calculations, allowing us to appreciate and enjoy the vibrancy of colors in the world around us.