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A Novel Contractive GAN Model for a Unified Approach Towards Blind Quality Assessment of Images from Heterogeneous Sources

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Advances in Visual Computing (ISVC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12509))

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Abstract

The heterogeneous distributions of pixel intensities between natural scene and document images casts challenges for generalizing quality assessment models across these two types of images, where human perceptual scores and optical character recognition accuracy are the respective quality metrics. In this paper we propose a novel contractive generative adversarial model to learn a unified quality-aware representation of images from heterogeneous sources in a latent domain. We then build a unified image quality assessment framework by applying a regressor in the unveiled latent domain, where the regressor operates as if it is assessing the quality of a single type of images. Test results on blur distortion across three benchmarking datasets show that the proposed model achieves promising performance competitive to the state-of-the-art simultaneously for natural scene and document images.

This research is supported by the Auditing Digitisation Outputs in the Cultural Heritage Sector (ADOCHS) project (Contract No. BR/154/A6/ADOCHS), financed by the Belgian Science Policy (Belspo) within the scope of the BRAIN programme and by funding from the Flemish Government under the “Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen” programme.

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Authors and Affiliations

  1. Department of Mathematics and Data Science, Vrije Universiteit Brussel, Brussels, Belgium

    Tan Lu & Ann Dooms

Authors
  1. Tan Lu
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  2. Ann Dooms
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Corresponding author

Correspondence to Tan Lu .

Editor information

Editors and Affiliations

  1. University of Nevada Reno, Reno, NV, USA

    George Bebis

  2. Stony Brook University, Stony Brook, NY, USA

    Zhaozheng Yin

  3. Drexel University, Philadelphia, PA, USA

    Edward Kim

  4. RWTH Aachen University, Aachen, Germany

    Jan Bender

  5. University of Edinburgh, Edinburgh, UK

    Kartic Subr

  6. IBM Research – Cambridge, Cambridge, MA, USA

    Bum Chul Kwon

  7. University of Waterloo, Waterloo, ON, Canada

    Jian Zhao

  8. Graz University of Technology, Graz, Austria

    Denis Kalkofen

  9. The Hong Kong Polytechnic University, Hong Kong, Hong Kong

    George Baciu

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Lu, T., Dooms, A. (2020). A Novel Contractive GAN Model for a Unified Approach Towards Blind Quality Assessment of Images from Heterogeneous Sources. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science(), vol 12509. Springer, Cham. https://doi.org/10.1007/978-3-030-64556-4_3

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  • DOI: https://doi.org/10.1007/978-3-030-64556-4_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64555-7

  • Online ISBN: 978-3-030-64556-4

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