Color Quantization Algorithms

Pixel Art Rust implements several color quantization algorithms, each with its own strengths.

Comparison

Algorithm	Speed	Quality	Memory	Use Case
Average	Fastest	Good	Low	Quick previews, uniform images
Median Cut	Fast	Better	Medium	Balanced quality/speed
K-Means	Slow	Best	High	Maximum quality
Quadtree	Fast	Adaptive	Medium	Images with varying detail

Algorithm Details

Average Color

Simple and fast - calculates the arithmetic mean of all pixels in each cell.

Median Cut

Recursively divides the color space to create a balanced palette.

K-Means Clustering

Iteratively refines color clusters for optimal representation.

Adaptive Quadtree

Dynamically subdivides regions based on color variance.

How Color Quantization Works

Color quantization is the process of reducing the number of distinct colors in an image. In pixel art conversion, this serves two purposes:

1. Simplification: Reduces visual complexity to achieve the characteristic pixel art aesthetic
2. Regionalization: Groups similar colors together to define pixel boundaries

The Pixel Art Process

Step 1: Grid Division

The image is divided into a grid of cells (or adaptive regions for quadtree).

Step 2: Color Analysis

For each cell, the algorithm analyzes all contained pixels to determine the representative color.

Step 3: Color Selection

Different algorithms use different strategies:

• Average: Simple arithmetic mean
• Median Cut: Optimal color space division
• K-Means: Iterative clustering
• Quadtree: Variance-based subdivision

Step 4: Reconstruction

The final image is built by filling each cell with its representative color.

Visual Quality Factors

Color Accuracy

How well the chosen colors represent the original image content.

Edge Preservation

How well the algorithm maintains important visual boundaries.

Gradient Handling

How smoothly the algorithm handles color transitions.

Detail Retention

How much fine detail is preserved in the conversion.

Performance Characteristics

Time Complexity

• Average: O(n) - fastest
• Median Cut: O(n log n) - moderate
• K-Means: O(n × k × i) - slowest
• Quadtree: O(n log d) - moderate

Where:

• n = number of pixels
• k = number of colors (for k-means)
• i = number of iterations
• d = maximum depth

Memory Usage

• Average: Minimal - only stores running totals
• Median Cut: Moderate - stores color lists for division
• K-Means: High - maintains cluster assignments
• Quadtree: Variable - depends on image complexity

Choosing the Right Algorithm

For Speed (Real-time Processing)

Choose Average Color when you need the fastest possible conversion:

• Live previews while adjusting parameters
• Batch processing large numbers of images
• Resource-constrained environments

For Quality (Final Output)

Choose K-Means when you want the best possible results:

• Final artwork creation
• Professional projects
• When processing time is not a concern

For Balance (Most Common Use)

Choose Median Cut for the best compromise:

• General-purpose pixel art creation
• When you need good quality in reasonable time
• Most photography and artwork conversion

For Adaptive Detail (Complex Images)

Choose Quadtree for images with varying complexity:

• Landscapes with both detailed and uniform areas
• Technical drawings with mixed content
• When you want automatic detail preservation

Color Spaces

RGB Color Space

• Standard computer graphics representation
• Simple calculations
• Not perceptually uniform

LAB Color Space

• Perceptually uniform
• Better for color distance calculations
• Used internally for higher-quality algorithms

The library automatically handles color space conversions for optimal results with each algorithm.

Algorithm Limitations

Average Color

• Can produce muddy colors in diverse regions
• No consideration of color distribution
• May lose important color relationships

Median Cut

• Limited by binary division strategy
• May not find optimal global palette
• Can struggle with very sparse color distributions

K-Means

• Requires multiple iterations to converge
• Can get stuck in local optima
• Sensitive to initial centroid placement

Quadtree

• Recursive subdivision may miss optimal boundaries
• Variance threshold selection affects results
• Can create irregular pixel shapes

Future Improvements

The algorithm implementations are continuously improved. Planned enhancements include:

• SIMD optimizations for parallel color processing
• GPU acceleration for large image processing
• Perceptual weighting for better visual results
• Custom color palettes for specific aesthetic goals

Technical Resources

For implementation details and API documentation, see:

• Algorithm API Reference - Technical implementation details
• Core Library API - High-level programming interface
• CLI Reference - Command-line usage and options

For practical usage examples:

• Usage Guide - Detailed usage patterns
• Examples - Real-world conversion examples