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28 July 2023 Hierarchical lossless image coding using CNN predictors considering prediction error distribution
Kazuki Nakashima, Ryo Nakazawa, Hideharu Toda, Hisashi Aomori, Tsuyoshi Otake, Ichiro Matsuda, Susumu Itoh
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Proceedings Volume 12749, Sixteenth International Conference on Quality Control by Artificial Vision; 127490O (2023) https://doi.org/10.1117/12.2691664
Event: Sixteenth International Conference on Quality Control by Artificial Vision, 2023, Albi, France
Abstract
We have researched a hierarchical lossless encoding method using cellular neural networks (CNN) as predictors. In our method, which belongs to the hierarchical lossless coding method, the prediction accuracy is improved by adaptively using different CNN predictors depending on the direction of the image edges. The prediction error obtained by CNN prediction is encoded by adaptive arithmetic coding using multiple probabilistic models based on the context modeling. In previous research,1 a new approach is introduced in which the prediction errors of each predictor are encoded separately by arithmetic coding. Although this method improves the performance of encoding prediction errors, increasing side information became an issue. Therefore, to reduce the side information of the arithmetic coders, we propose a grouping algorithm that groups the prediction errors corresponding to each predictor based on the utilization of the predictors.
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Kazuki Nakashima, Ryo Nakazawa, Hideharu Toda, Hisashi Aomori, Tsuyoshi Otake, Ichiro Matsuda, and Susumu Itoh "Hierarchical lossless image coding using CNN predictors considering prediction error distribution", Proc. SPIE 12749, Sixteenth International Conference on Quality Control by Artificial Vision, 127490O (28 July 2023); https://doi.org/10.1117/12.2691664
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KEYWORDS
Image compression
Modeling
Neural networks
Mathematical optimization
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