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WU Ya-dong, ZHANG Hong-ying. A New Perceptual Image Quality Assessment Method Based on Total Variation Model[J]. Journal of University of Electronic Science and Technology of China, 2015, 44(1): 79-83. doi: 10.3969/j.issn.1001-0548.2015.01.013
Citation: WU Ya-dong, ZHANG Hong-ying. A New Perceptual Image Quality Assessment Method Based on Total Variation Model[J]. Journal of University of Electronic Science and Technology of China, 2015, 44(1): 79-83. doi: 10.3969/j.issn.1001-0548.2015.01.013
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A New Perceptual Image Quality Assessment Method Based on Total Variation Model

doi: 10.3969/j.issn.1001-0548.2015.01.013
  • 1.

    School of Computer Science and Technology,Southwest University of Science and Technology Mianyang Sichuan 621010;

  • 2.

    Fundamental Science on Nuclear Wastes and Environmental Safety Laboratory,Southwest University of Science and Technology Mianyang Sichuan 621010;

  • 3.

    School of Information and Engineering,Southwest University of Science and Technology Mianyang Sichuan 621010;

  • 4.

    Robot Technology Used for Special Environment Key Laboratory of Sichuan Province,Southwest University of Science and Technology Mianyang Sichuan 621010

  • Received Date: 2014-02-25
  • Rev Recd Date: 2014-11-30
  • Publish Date: 2015-02-15
  • In this paper, based on the assumption that human visual system(HVS) is sensitive for image structures (edges) and local luminance (light stimulation), we propose a new perceptual image quality assessment (PIQA) metric based on total variation(TV) model in spatial domain. In the proposed metric, the TV's comparison between a distorted image and its reference image is applied to measure the extent of the loss of the image structural information. As a complementary part to measure the distortion, the energy of enclosed regions in a difference image is used to measure the missing luminance information which is sensitive to human visual system. The performance of the proposed metric is validated with an extensive subjective database. The results show that the proposed metric outperforms the state-of-the-art of image quality assessment metrics.
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A New Perceptual Image Quality Assessment Method Based on Total Variation Model

doi: 10.3969/j.issn.1001-0548.2015.01.013
  • 1. School of Computer Science and Technology,Southwest University of Science and Technology Mianyang Sichuan 621010;
  • 2. Fundamental Science on Nuclear Wastes and Environmental Safety Laboratory,Southwest University of Science and Technology Mianyang Sichuan 621010;
  • 3. School of Information and Engineering,Southwest University of Science and Technology Mianyang Sichuan 621010;
  • 4. Robot Technology Used for Special Environment Key Laboratory of Sichuan Province,Southwest University of Science and Technology Mianyang Sichuan 621010
  • Received Date: 2014-02-25
  • Rev Recd Date: 2014-11-30
  • Publish Date: 2015-02-15

Abstract: In this paper, based on the assumption that human visual system(HVS) is sensitive for image structures (edges) and local luminance (light stimulation), we propose a new perceptual image quality assessment (PIQA) metric based on total variation(TV) model in spatial domain. In the proposed metric, the TV's comparison between a distorted image and its reference image is applied to measure the extent of the loss of the image structural information. As a complementary part to measure the distortion, the energy of enclosed regions in a difference image is used to measure the missing luminance information which is sensitive to human visual system. The performance of the proposed metric is validated with an extensive subjective database. The results show that the proposed metric outperforms the state-of-the-art of image quality assessment metrics.

WU Ya-dong, ZHANG Hong-ying. A New Perceptual Image Quality Assessment Method Based on Total Variation Model[J]. Journal of University of Electronic Science and Technology of China, 2015, 44(1): 79-83. doi: 10.3969/j.issn.1001-0548.2015.01.013
Citation: WU Ya-dong, ZHANG Hong-ying. A New Perceptual Image Quality Assessment Method Based on Total Variation Model[J]. Journal of University of Electronic Science and Technology of China, 2015, 44(1): 79-83. doi: 10.3969/j.issn.1001-0548.2015.01.013
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