The essence of Image Compression is balancing Storage Space and Visual Quality. This page deeply analyzes how to leverage Mathematical Redundancy and Visual Perception Limits for extreme data reduction.
Shannon Entropy defines the limit of data compression. Click blocks to change patterns:
The human eye is sensitive to Luma (Y) but insensitive to Chroma (Cb/Cr).
Discrete Cosine Transform (DCT) converts images to frequencies, then discards info via Quantization Steps.
Trade-off: How much detail can be kept at what size?
All Lossy Algorithms follow a rule: Lower Bitrate (Rate) means higher Distortion. Excellent encoders (like AV1) maintain high similarity at very low bitrates.
When compressed image PSNR reaches about 35-45dB, the Human Visual System (HVS) can barely distinguish it from the original. This art of "tricking" the eye is the core of lossy compression.
Compared to traditional PSNR, SSIM measures image structure, luminance, and contrast. It aligns better with human subjective aesthetics than simple pixel error calculation.
Lossless compression is limited by Information Entropy. If data is completely random noise, its entropy is maxed out, and no algorithm can reduce it. Lossy compression can discard more, but if the Quantization Step is too large, the image collapses into solid color blocks.
Modern formats like WebP and HEIF introduce Intra-prediction from video coding. They try to predict the current block using surrounding pixels, recording only the "prediction error" (residual), which is more efficient than JPEG's full block recording.