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.
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