18210

Neural Multi-scale Image Compression

Ken Nakanishi, Shin-ichi Maeda, Takeru Miyato, Daisuke Okanohara
The University of Tokyo
arXiv:1805.06386 [stat.ML], (16 May 2018)

@article{nakanishi2018neural,

   title={Neural Multi-scale Image Compression},

   author={Nakanishi, Ken and Maeda, Shin-ichi and Miyato, Takeru and Okanohara, Daisuke},

   year={2018},

   month={may},

   archivePrefix={"arXiv"},

   primaryClass={stat.ML}

}

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This study presents a new lossy image compression method that utilizes the multi-scale features of natural images. Our model consists of two networks: multi-scale lossy autoencoder and parallel multi-scale lossless coder. The multi-scale lossy autoencoder extracts the multi-scale image features to quantized variables and the parallel multi-scale lossless coder enables rapid and accurate lossless coding of the quantized variables via encoding/decoding the variables in parallel. Our proposed model achieves comparable performance to the state-of-the-art model on Kodak and RAISE-1k dataset images, and it encodes a PNG image of size $768 times 512$ in 70 ms with a single GPU and a single CPU process and decodes it into a high-fidelity image in approximately 200 ms.
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