Skip Estimation, specifically Frequency-aware Skip Estimation (FaSE), is a module within diffusion-based image compression that refines the ε-prediction prior from a pre-trained latent diffusion model. It aligns this prior with compressed latents across timesteps to enable efficient and high-fidelity image reconstruction.
Skip Estimation is a technique used in advanced image compression systems that rely on AI diffusion models. It works by improving the model's initial guesses for image details, allowing for much faster image decoding and significantly smaller file sizes without losing visual quality.
FaSE, Frequency-aware Skip Estimation
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