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Article Dans Une Revue Journal of Statistical Mechanics: Theory and Experiment Année : 2023

A New Spin on Color Quantization

Samy Lakhal
  • Fonction : Auteur
Alexandre Darmon
  • Fonction : Auteur
Michael Benzaquen

Résumé

We address the problem of image color quantization using a Maximum Entropy based approach. We argue that adding thermal noise to the system yields better visual impressions than that obtained from a simple energy minimization. To quantify this observation, we introduce the coarse-grained quantization error, and seek the optimal temperature which minimizes this new observable. By comparing images with different structural properties, we show that the optimal temperature is a good proxy for complexity at different scales. Finally, having shown that the convoluted error is a key observable, we directly minimize it using a Monte Carlo algorithm to generate a new series of quantized images. Adopting an original approach based on the informativity of finite size samples, we are able to determine the optimal convolution parameter leading to the best visuals.
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Dates et versions

hal-03797225 , version 1 (04-10-2022)

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  • HAL Id : hal-03797225 , version 1

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Samy Lakhal, Alexandre Darmon, Michael Benzaquen. A New Spin on Color Quantization. Journal of Statistical Mechanics: Theory and Experiment, 2023, pp.033401. ⟨hal-03797225⟩
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