Micro-Diffusion Compression -- Binary Tree Tweedie Denoising for Online Probability Estimation
We present Midicoth, a lossless compression system that introduces a micro-diffusion denoising layer for improving probability estimates produced by adaptive statistical models. In compressors such as Prediction by Partial Matching (PPM), probability estimates are smoothed by a prior to handle sparse observations. When contexts have been seen only a few times, this prior dominates the prediction and produces distributions that are significantly flatter than the true source distribution, leading to compression inefficiency. Midicoth addresses this limitation by treating prior smoothing as a shrinkage process and applying a reverse denoising step that corrects predicted probabilities using empirical calibration statistics. To make this correction data-efficient, the method decomposes each byte prediction into a hierarchy of binary decisions along a bitwise tree. This converts a single 256-way calibration problem into a sequence of binary calibration tasks, enabling reliable estimation of correction terms from relatively small numbers of observations. The denoising process is applied in multiple successive steps, allowing each stage to refine residual prediction errors left by the previous one. The micro-diffusion layer operates as a lightweight post-blend calibration stage applied after all model predictions have been combined, allowing it to correct systematic biases in the final probability distribution. Midicoth combines five fully online components: an adaptive PPM model, a long-range match model, a trie-based word model, a high-order context model, and the micro-diffusion denoiser applied as the final stage.
69b1d851206bad330cd7fea4
Roberto Tacconelli
claimed_verified
2026-03-13T08:14:27.534Z
6790ee0b6c8a6afd0dd2fec9
https://cdn-avatars.huggingface.co/v1/production/uploads/6790ee0b6c8a6afd0dd2fec9/3RTHNcj5eRUvw0j-AM4BH.jpeg
Roberto Tacconelli
robtacconelli
user
robtacconelli
Micro-Diffusion Compression -- Binary Tree Tweedie Denoising for Online Probability Estimation
We present Midicoth, a lossless compression system that introduces a micro-diffusion denoising layer for improving probability estimates produced by adaptive statistical models. In compressors such as Prediction by Partial Matching (PPM), probability estimates are smoothed by a prior to handle sparse observations. When contexts have been seen only a few times, this prior dominates the prediction and produces distributions that are significantly flatter than the true source distribution, leading to compression inefficiency. Midicoth addresses this limitation by treating prior smoothing as a shrinkage process and applying a reverse denoising step that corrects predicted probabilities using empirical calibration statistics. To make this correction data-efficient, the method decomposes each byte prediction into a hierarchy of binary decisions along a bitwise tree. This converts a single 256-way calibration problem into a sequence of binary calibration tasks, enabling reliable estimation of correction terms from relatively small numbers of observations. The denoising process is applied in multiple successive steps, allowing each stage to refine residual prediction errors left by the previous one. The micro-diffusion layer operates as a lightweight post-blend calibration stage applied after all model predictions have been combined, allowing it to correct systematic biases in the final probability distribution. Midicoth combines five fully online components: an adaptive PPM model, a long-range match model, a trie-based word model, a high-order context model, and the micro-diffusion denoiser applied as the final stage.
Midicoth enhances compression efficiency by applying a micro-diffusion denoising layer to refine probability estimates in adaptive statistical models, addressing limitations in sparse data scenarios through hierarchical binary decision making and iterative error correction.
lossless compression micro-diffusion denoising layer adaptive statistical models probability estimates Prediction by Partial Matching shrinkage process empirical calibration statistics bitwise tree binary calibration tasks residual prediction errors post-blend calibration trie-based word model high-order context model