• DocumentCode
    641484
  • Title

    Perceptual image quality: A dissimilarity measure to quantify the degradation of image quality

  • Author

    Saha, Simanto ; Si Liu ; Tahtali, Murat ; Lambert, Andrew ; Pickering, Mark

  • Author_Institution
    Sch. of Eng. & Inf. Technol., Univ. of New South Wales, Canberra, ACT, Australia
  • fYear
    2013
  • fDate
    10-12 June 2013
  • Firstpage
    245
  • Lastpage
    249
  • Abstract
    This paper introduces a novel image quality metric that outperforms other existing and widely used metrics, namely the Root Mean Square Error (RMSE) and Structural Similarity Index (SSIM). Objective methods for assessing perceptual image quality are important for many image processing applications nowadays, however none of the existing metrics are completely satisfactory. Structural Similarity Index has been found promising in some cases, however for medical imaging (especially for CT and MRI images) it still remains immature. Perceptual Dissimilarity (PD) has been found reliable considering medical imaging demands; however for natural images, SSIM still outperforms PD. On that perspective, the proposed image quality metric named Perceptual Image Quality (PIQ) is more consistent with the human visual system´s characteristics than SSIM and RMSE.
  • Keywords
    biomedical MRI; computerised tomography; medical image processing; visual perception; CT image; MRI image; PD; RMSE; SSIM; human visual system characteristics; image processing applications; image quality degradation; image quality metric; medical imaging; natural images; perceptual dissimilarity measure; perceptual image quality assessment; root mean square error; structural similarity index; Biomedical imaging; Degradation; Image quality; Image reconstruction; Indexes; Measurement; Visual systems; Root Mean Square Error; Structural Similarity Index; image quality metric; visual saliency;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Visual Information Processing (EUVIP), 2013 4th European Workshop on
  • Conference_Location
    Paris
  • Type

    conf

  • Filename
    6623969