perceptual compression

**Perceptual compression** is the **compression approach that preserves human-salient structure while discarding details with low perceptual importance** - it enables efficient latent representations for high-quality generative modeling. **What Is Perceptual compression?** - **Definition**: Optimizes compressed representations using perceptual criteria rather than pure pixel fidelity. - **Modeling Context**: Often implemented through learned autoencoders used in latent diffusion pipelines. - **Retention Goal**: Keeps semantic content and visible textures while reducing redundant information. - **Evaluation**: Requires perceptual metrics and human inspection, not only MSE or PSNR. **Why Perceptual compression Matters** - **Efficiency**: Reduces training and inference cost by shrinking representation size. - **Quality Balance**: Supports visually convincing outputs despite heavy compression. - **Scalability**: Makes high-resolution synthesis tractable on practical hardware. - **Pipeline Impact**: Compression ratio strongly influences downstream denoiser difficulty. - **Risk**: Excessive compression can remove fine details needed for specialized applications. **How It Is Used in Practice** - **Ratio Selection**: Tune compression factor against acceptable artifact levels for target use cases. - **Metric Mix**: Evaluate LPIPS, SSIM, and human review together for robust decisions. - **Domain Refit**: Adjust compression models when moving to medical, industrial, or technical imagery. Perceptual compression is **a key enabler of efficient latent generative pipelines** - perceptual compression should be optimized for the final user task, not only aggregate reconstruction scores.