Probability model adjustment for the CNN-based lossless image coding method

Hiroki Kojima; Yusuke Kameda; Yasuyo Kita; Ichiro Matsuda; Susumu Itoh

doi:10.1117/12.2590982

13 March 2021 Probability model adjustment for the CNN-based lossless image coding method

Hiroki Kojima, Yusuke Kameda, Yasuyo Kita, Ichiro Matsuda, Susumu Itoh

Proceedings Volume 11766, International Workshop on Advanced Imaging Technology (IWAIT) 2021; 1176605 (2021) https://doi.org/10.1117/12.2590982
Event: International Workshop on Advanced Imaging Technology 2021 (IWAIT 2021), 2021, Online Only

Abstract

Recently, convolutional neural network-based generative models of image signals have been proposed mainly for the purpose of image generation, restoration and compression. For example, PixelCNN++ approximates probability distribution of the image intensity value as a parametric function pel-by-pel, and can be used for lossless image coding tasks. However, such an approach cannot work well for specific images which have statistical properties different from the image dataset used for the network training. In this paper, we improve the coding efficiency by introducing a few parameters for adjusting the probability model generated by PixelCNN++. These parameters are numerically optimized to minimize coding rates of the given image and then encoded as side-information to enable same adjustment at the decoder side.

Citation Download Citation

Hiroki Kojima, Yusuke Kameda, Yasuyo Kita, Ichiro Matsuda, and Susumu Itoh "Probability model adjustment for the CNN-based lossless image coding method", Proc. SPIE 11766, International Workshop on Advanced Imaging Technology (IWAIT) 2021, 1176605 (13 March 2021); https://doi.org/10.1117/12.2590982

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