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DeepSIC: Deep Semantic Image Compression

  • Sihui Luo
  • Yezhou Yang
  • Yanling Yin
  • Chengchao Shen
  • Ya Zhao
  • Mingli Song
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11301)

Abstract

Incorporating semantic analysis into image compression can significantly reduce the repetitive computation of fundamental semantic analysis in client-side applications such as semantic image retrieval. The same practice also enables the compressed code to carry semantic information of the image during its storage and transmission. In this paper, we propose a Deep Semantic Image Compression (DeepSIC) model to achieve this goal and put forward two novel architectures that aim to reconstruct the compressed image and generate corresponding semantic representations at the same time by a single end-to-end optimized network. The first architecture performs semantic analysis in the encoding process by reserving a portion of the bits from the compressed code to store the semantic representations. The second performs semantic analysis in the decoding step with the feature maps that are embedded in the compressed code. In both architectures, the feature maps are shared by the compression and the semantic analytics modules. Experiments over benchmarking datasets show promising performance of the proposed compression model.

Keywords

Deep image compression Semantic image compression End-to-end optimization 

Notes

Acknowledgment

This work is supported by National Natural Science Foundation of China (61572428, U1509206), Fundamental Research Funds for the Central Universities (2017FZA5014), National Key Research and Development Program (2016YFB1200203) and Key Research and Development Program of Zhejiang Province (2018C01004).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Sihui Luo
    • 1
  • Yezhou Yang
    • 2
  • Yanling Yin
    • 1
  • Chengchao Shen
    • 1
  • Ya Zhao
    • 1
  • Mingli Song
    • 1
  1. 1.Zhejiang UniversityHangzhouChina
  2. 2.Arizona State UniversityTempeUSA

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