Image Compression Algorithms Deep Dive: From JPEG to AI Compression The development history of image compression algorithms reflects the progress of computer vision technology.

Let's understand the principles and applications of various compression technologies. #

Traditional Compression Algorithms

JPEG Compression- Principle: Based on Discrete Cosine Transform (DCT)

  • Advantages: Mature and stable, good compatibility

  • Limitations: Lossy compression, no transparency support

PNG Compression- Principle: Lossless compression based on LZ77 algorithm

  • Advantages: Lossless compression, transparency support

  • Limitations: Large file size, not suitable for photos #

Modern Compression Technologies

WebP Algorithm-VP8 Encoding: Video encoding technology applied to images

  • Predictive Coding: Utilize pixel correlation

  • Quantization Optimization: More precise quality control

AVIF Compression-AV1 Encoding: Next-generation video encoding standard

  • Transform Technology: More efficient frequency domain transformation

  • Entropy Coding: Improved compression efficiency #

AI-Driven Compression Neural Network Compression:- Use deep learning models - Learn latent representations of images - Achieve higher compression ratiosSemantic-Aware Compression: - Understand image content semantics - Protect important area quality - Intelligently allocate compression resources

Algorithm Selection Guide For Photos:- Prioritize AVIF - Backup WebP - Fallback JPEGFor Icons and Graphics:- Prioritize SVG - Backup PNG - Avoid JPEGFor Animated Images: - Use WebP animation - Or consider AVIF animation - Traditional GIF as fallback