RGB Interpolation: Learn How Machines Interpret Colors
Should you learn color interpolation? Beginner programmers presumably don’t study Python or Java for color interpolation. But here are a few strong reasons to learn this vital skill:
- Apple and Android will continue to market increasingly efficient and powerful cameras.
- Out-of-the-box color interpolation options are simpler and cheaper. But excellent color interpolation algorithms are ideal for making your visuals vibrant and clear.
- As cameras demand expansion, professional developers will be required. So, if you want to make money by learning to code, this is an excellent field to focus on.
So, cameras have gotten better over time. High-quality cameras have fueled the expansion of social networks. They have also encouraged innovative sectors like augmented reality (AR).
The key to this expansion is developing HD image capture and color interpolation algorithms. It allows devices to read and process colors, identify photos, and use machine learning to sort or filter them.
What exactly is color interpolation, and how does it work? How can algorithms improve color interpolation for current devices?
What is RGB or Color Interpolation?
Colors are kept as numeric components in your computer. These components are like dimensions in an n-dimensional space, and are called a color space. Most color systems are 3D cubes because they have three components. You may be familiar with the RGB (Red, Green, and Blue).
RGB is the most natural color scheme. It utilizes one red, one blue, and one green spotlight on every pixel to display color.
As we know, interpolation uses sequenced known values to estimate an unknown value in between. So, breaking a color into components improves color interpolation. We only need three numbers. In other words, we have to move in between the values of these three numbers.
How Does Color Interpolation Work?
Companies use their color interpolation algorithms for many things. These involve smartphones, cameras, and screens, among others. These algorithms are kept secret to avoid giving competitors an edge. After all, there are billions of dollars in sales thanks to camera innovations.
Color interpolation transforms a CFA (color filter array) image into a three-color channel image. It leverages surrounding pixels to interpret each pixel’s missing color channels. As more surrounding pixels are used, the algorithm gets more accurate. But more precision requires more computing (i.e. it needs more memory and more powerful CPUs).
Developers and designers use three metrics to measure the performance of color interpolation algorithms. They are: sharpness, false color, and CPU usage or processing time.
Sharpness Matters!
Image sharpness results from a combination of the lens, imager, and color interpolation. If the first two components get optimized, the interpolation will soften the edges.
Interpolation causes color artifacts, compounding sharpness. Real-time interpolation is impossible without color artifacts. This is because color artifacts arise when the camera saves a subsample of the real image color.
CPU/Processing Time: Where Does It Fit?
Higher-end color interpolation uses more memory and processing power, so you need more powerful CPUs and more RAM.
A lot of software uses ready-to-use algorithms for color interpolation. Typical apps may not need top-level image processing. Yet sometimes, developers choose unique solutions for color interpolation.
Color Interpolation and Programming
Interpolation fills a gap between two numbers. Most APIs (application programming interfaces) offer linear interpolation composed of three parameters—the beginning point, the ending point, and a number between them, advancing along the segment.
The formula for linear interpolation is simple, efficient, and works in any dimension. 2D linear interpolation needs X and Y. Linear interpolation returns points on the line connecting the beginning and ending points, regardless of dimensions.
If linear interpolation works in 3D, color interpolation does not. Human eyes interpret colors differently in XYZ and RGB. In 3D, connecting two points with a line makes sense, but not in RGB. Interpolating R, G, and B individually guarantees no intermediate hue.
See the cube’s center? Gray. Now, suppose we go between the bottom left and top right corners of the RGB cube. In that case, we pass over a color that does not belong to the red-to-turquoise gradient. Let’s see another example:
Pure colors work well, but merged ones don’t. The reason is that we never expected the midway of violet and dark green to be gray. It won’t affect the starting and finishing colors. But the midpoints are disappointing and may have little to no chroma.
Luminosity Color Interpolation
Interpolating in various color spaces will provide different results. We will use colors between our start and finish values.
Spaces are tailored for subjective color perception.
Hue-Chroma-Luminance (HCL) is one of them. RGB (top) vs. HCL (bottom):
We described RGB above. In the first diagram, trace the cube from the top right to the bottom left corner to observe how it passes through a gray center. Mathematically correct, but visually surprising! On the other hand, HCL is more or less what we’d anticipate.
HCL is computationally intensive because the RGB value must undergo many transformations before being represented in HCL and then returned to RGB. It’s interesting to think about using HCL to make intermediate colors for interpolation in RGB space.
Adopt HCL To Go Farther!
Lerping RGB components is a systematic and straightforward way to interpolate colors. It helps tackle a complex problem. If interpolated colors must be seen simultaneously, you need an advanced approach. So, use HCL color space as a beginner programmer to go the extra mile.
