Full-Color Image Processing
Full-color image processing deals with processing images that contain multiple color components—typically three color channels, such as Red, Green, and Blue (RGB).
Color image processing is more complex than grayscale processing because each color pixel contains three intensity values, not just one.
Categories of Full-Color Image Processing
There are two main approaches to processing full-color images:
- Per-Component Processing (Channel-Wise Processing):
- Each color channel (R, G, B) is treated as a separate grayscale image.
- We apply grayscale techniques (e.g., filtering, enhancement) individually on each channel.
- After processing, the three channels are combined to form the final color image.
- Vector-Based Processing (Pixel-Wise or Color Vector Processing):
- A color pixel is treated as a vector in a color space like RGB.
- Processing is done by treating the entire pixel (all three components) as a single unit.
- This method is more accurate when color correlation matters.
Color Pixel as a Vector (RGB Representation)
Component-wise vs Vector Processing
1. Component-wise (Channel-wise) Processing
- Each of the RGB images is processed separately.
- Then they are merged back into a full-color image.
- Works well for operations like:
- Histogram equalization
- Noise filtering
- Smoothing
2. Vector-based (Voxel-based) Processing
- All 3 color values at a pixel are processed together as a vector.
- Useful when the relationship between R, G, and B is important.
- Example: Color edge detection, color space conversions.
Example: Neighborhood Averaging
Let’s consider neighborhood averaging (smoothing):
- In grayscale image (Fig 6.27a), a 3×3 window is used. All 9 values are averaged.
- In an RGB image (Fig 6.27b), the same 3×3 window is applied:
- We treat each RGB triplet as a 3D voxel.
- Average R values, G values, and B values separately, or treat the whole voxel as a single unit.
In this specific case, both approaches give the same result, so component-wise processing is valid.
- Left: A 3×3 grayscale neighborhood (2D)
- Right: A 3×3×3 RGB voxel block (3D), with arrows showing same (x, y) mapping across R, G, B
Label:
- Voxel = A pixel with 3 values (R, G, B)
- Same (x, y) index across all 3 channels
Important Note
For component-wise and vector-based processing to give same result, two conditions must be met:
- The operation must apply to both scalars and vectors.
- Each component must be processed independently.