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Image Compression Based on Genetic Algorithm and Deep Neural Network

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Bio-inspired Computing – Theories and Applications (BIC-TA 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 681))

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Abstract

In conventional image compression algorithms, a high compression ratio can be obtained but at the cost of loss of details. In this paper, a new image compression algorithm is proposed. It is based on recently established deep learning model: deep auto encoder (DAE). We adopt the genetic algorithm to find optimal initial network weights to construct a DAE with multiple hidden layers for image compression. With the optimized network, the essential information from the input image can be extracted and represented. Experiments on typical images show that the proposed algorithm obtains higher Peak Signal to Noise Ratio (PSNR), and superior image quality is preserved at both low and high compression ratio compared with the existing algorithms.

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Acknowledgment

This work was supported by the Shaanxi collaborative innovation program (2015XT-61), Xijing University Research Foundation: (XJ150123), the National Natural Science Foundation of China (No. 61502369), the foundation from Ministry of Education of China (No. BK16015020001), the National Science Basic Research Plan in Shaanxi Province of China (No. 2016JQ6049), and the Fundamental Research Funds for the Central Universities (No. 7215598901).

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Authors and Affiliations

  1. Xijing University, Xi’an, 710123, China

    Haisheng Deng

  2. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, 710071, China

    Hongying Liu, Feixiang Wang & Zhi Wang

  3. Shaanxi Electrical Apparatus Research Institute, Xi’an, 710025, China

    Yikai Wang

Authors
  1. Haisheng Deng
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  2. Hongying Liu
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  3. Feixiang Wang
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  4. Zhi Wang
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  5. Yikai Wang
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Corresponding author

Correspondence to Haisheng Deng .

Editor information

Editors and Affiliations

  1. Xidian University, Xi’an, China

    Maoguo Gong

  2. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  3. China University of Petroleum, Qingdao, China

    Tao Song

  4. Faculty of Engineering, Computing and Science, Swinburne University of Technology Sarawak Campus, Kuching, China

    Gexiang Zhang

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© 2016 Springer Nature Singapore Pte Ltd.

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Deng, H., Liu, H., Wang, F., Wang, Z., Wang, Y. (2016). Image Compression Based on Genetic Algorithm and Deep Neural Network. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 681. Springer, Singapore. https://doi.org/10.1007/978-981-10-3611-8_36

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  • DOI: https://doi.org/10.1007/978-981-10-3611-8_36

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3610-1

  • Online ISBN: 978-981-10-3611-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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