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Neural Multi-scale Image Compression

  • Ken M. NakanishiEmail author
  • Shin-ichi Maeda
  • Takeru Miyato
  • Daisuke Okanohara
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11366)

Abstract

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.

Supplementary material

484523_1_En_45_MOESM1_ESM.pdf (3.2 mb)
Supplementary material 1 (pdf 3261 KB)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Graduate School of ScienceThe University of TokyoBunkyo-kuJapan
  2. 2.Preferred Networks, Inc.Chiyoda-kuJapan

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