Explore the fascinating world of image compression algorithms and understand how they work to reduce file sizes while maintaining image quality.
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Popular Compression Algorithms
- Run-Length Encoding (RLE)
- Huffman Coding
- Discrete Cosine Transform (DCT)
- LZW Compression
- Wavelet Transform
Algorithm Selection Guide
- RLE: Best for images with large areas of same color
- Huffman: Optimal for general-purpose compression
- DCT: Excellent for photographic images
- LZW: Efficient for images with repeated patterns
Implementation Examples
1. Run-Length Encoding
// Basic RLE implementation
const encodeRLE = (data) => {
let encoding = '';
let prev = data[0];
let count = 1;
for(let i = 1; i < data.length; i++) {
if(data[i] !== prev) {
encoding += count + prev;
count = 1;
prev = data[i];
} else {
count++;
}
}
return encoding + count + prev;
};Compression Ratios
- RLE: 2:1 to 8:1
- Huffman: 2:1 to 4:1
- DCT: 10:1 to 50:1
- LZW: 3:1 to 5:1
Performance Considerations
- Compression speed
- Memory usage
- Quality retention
- File size reduction