AI video enhancement is one of those features that gets marketed with dramatic before/after clips and very little explanation of what's actually going on under the hood. This post covers the technical foundations — what models are doing, where they work well, and where they hit real limits. The Problem with Traditional Approaches Classical video processing tools (think FFmpeg filters, VirtualDub, early sharpening algorithms) work in pixel space with hand-crafted rules: Sharpen: increase contrast at detected edges using kernels like Laplacian or Unsharp Mask Denoise: average surrounding pixel values (Gaussian blur, median filter) Upscale: bicubic or bilinear interpolation — essentially weighted averaging of neighboring pixels These methods are fast and deterministic, but they have a fundamental ceiling: they have no model of what the content should look like. A denoising filter doesn't know whether a patch of variation is film grain or fabric texture.…