Color Interpolation Steps and Calculations
Color interpolation is the process of estimating intermediate colors between two or more known color points, commonly used to create gradients, blend colors in computer graphics, and reconstruct images (demosaicking) from raw camera sensor data. It involves calculating new numerical values for color channels (like RGB) for positions between known data points.
Algorithms and Steps - Common interpolation algorithms include:
1. Nearest Neighbor
- Steps:
- Map the new pixel coordinate to the closest pixel in the original, low-resolution image.
- Assign the exact color value of that nearest neighbor to the new pixel.
- Pros/Cons: Very fast, but produces "blocky" or pixelated images.
2. Bilinear Interpolation (2D)
- Steps:
- Identify Neighbors: Locate the 4 nearest pixels (2x2 neighborhood) surrounding the target location.
- Horizontal Interpolation: Linearly interpolate between the top two pixels, and then between the bottom two pixels, based on the horizontal distance (
tx) - Vertical Interpolation: Linearly interpolate the two results from step 2 along the vertical distance (ty)
- Pros/Cons: Smoother results than Nearest Neighbor, but can look blurry. It is technically quadratic, not linear, in 2D space.
3. Bicubic Interpolation
- Steps: Uses 16 neighboring pixels (4×4 grid) instead of 4.
- Calculation: Applies cubic polynomial equations to compute the weighted average.
- Pros/Cons: Produces much sharper, higher-quality images, but is more computationally expensive.
Color Spaces Matter
Interpolating in sRGB space can produce muddy, "gray" colors in the middle of a transition (e.g., Red to Green passing through brown).
- Better Practice: Convert RGB to a linear color space (like Linear RGB or Oklab) or a perceptual space (like HSL/LCH) before interpolation.
- CSS Example: CSS defaults to the Oklab color space for interpolation because it is perceptually uniform, meaning it provides more visually accurate gradients.
Key Steps in Image Processing (Demosaicing)
- Initialize: Read the Bayer filter pattern where each pixel has only one color.
- Interpolate Green: Green has the highest density, so it is usually interpolated first using surrounding green pixels.
- Interpolate Red/Blue: Use the interpolated Green channel to guide the interpolation of Red and Blue (e.g., interpolate the Red-Green difference, not the Red value itself).
- Refine: Apply edge-sensing algorithms to prevent "zipper" artifacts on sharp edges.
Core Concepts and Calculations
The most common form is Linear Interpolation (Lerp), which creates a straight-line path between two colors.
Formula:
The formula to fina a new color (Cr) between color-1 (C1) and color-2 (C2) is: Resulr = (1-t)*C1 + t*C2 where 't' is the interpolation factor ranging from 0.0 to 1.0 (0% to 100%).
Channel-wise Calculation:
Rnew = (1-t)*R1 + t*R2
Gnew = (1-t)*G1 + t*G2
Bnew = (1-t)*B1 + t*B2
Example Calculation (RGB 0-255):
Start Color (C1): Red (255, 0, 0)
End Color (C2): Blue (0, 0, 255)
Factor (t): 0.5 (50% blend)
Red: (1 - 0.5) * 255 + 0.5*0 = 127.5
Green: (1 - 0.5) * 0+ 0.5*0 = 0
Blue: (1 - 0.5) * 0+ 0.5*255 = 127.5
Result: (127.5,0,127.5) ≈ (128,0,128) {Purple}