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Adaptive Context Modeling for Arithmetic Coding Using Perceptron
  • Lucas Lopes ,
  • Philip Chou ,
  • Ricardo de Queiroz
Lucas Lopes
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Philip Chou
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Ricardo de Queiroz
Universidade de Brasilia

Corresponding Author:[email protected]

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Abstract

Arithmetic coding is used in most media compression methods. Context modeling is usually done through frequency counting and look-up tables (LUTs). For long-memory signals, probability modeling with large context sizes is often infeasible. Recently, neural networks have been used to model probabilities of large contexts in order to drive arithmetic coders. These neural networks have been trained offline. We introduce an online method for training a perceptron-based context-adaptive arithmetic coder on-the-fly, called adaptive perceptron coding, which continuously learns the context probabilities and quickly converges to the signal statistics.We test adaptive perceptron coding over a binary image database, with results always exceeding the performance of LUT-based methods for large context sizes and of recurrent neural networks. We also compare the method to a version requiring offline training, which leads to equally satisfactory results.
2022Published in IEEE Signal Processing Letters volume 29 on pages 2382-2386. 10.1109/LSP.2022.3223314