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<article article-type="research-article">
  <front>
    <journal-meta>
      <journal-id journal-id-type="aggregator">72010604</journal-id>
      <journal-title>Electronic Imaging</journal-title>
      <issn pub-type="ppub">2470-1173</issn><issn pub-type="epub"></issn>
      <publisher>
        <publisher-name>Society for Imaging Science and Technology</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.2352/ISSN.2470-1173.2018.12.IQSP-365</article-id>
      <article-id pub-id-type="sici">2470-1173(20180128)2018:12L.3651;1-</article-id>
      <article-id pub-id-type="publisher-id">s17.phd</article-id>
      <article-id pub-id-type="other">/ist/ei/2018/00002018/00000012/art00017</article-id>
      <article-categories>
        <subj-group>
          <subject>Articles</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Image Quality Loss and Compensation for Visually Impaired Observers</article-title>
      </title-group>
      <contrib-group>
        <contrib>
          <name>
            <surname>Triantaphillidou</surname>
            <given-names>Sophie</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Fry</surname>
            <given-names>Edward</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Sanchis</surname>
            <given-names>Vicent</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Pons</surname>
            <given-names>Álvaro</given-names>
          </name>
        </contrib>
      </contrib-group>
      <pub-date>
        <day>28</day>
        <month>01</month>
        <year>2018</year>
      </pub-date>
      <volume>2018</volume>
      <issue>12</issue>
      <fpage>365-1</fpage>
      <lpage>365-6</lpage>
      <permissions>
        <copyright-year>2018</copyright-year>
      </permissions>
      <abstract>
        <p>The measurement and modeling of image quality are aimed to assist the design and optimization of systems, typically built for 'normal' observer vision. But in reality image viewers rarely have perfect vision. There have been few attempts and no universal framework for measuring image
 quality loss due to visual impairments. The paper presents initial experiments designed to measure still image quality losses, as experienced by observers with visual accommodation problems, by proposing modifications to the Quality Ruler method described in ISO 20462-3:2012. A simple method
 is then presented, which compensates directly on the display for some of the quality lost due to the impairment. It uses a purpose-built image equalization software. The compensated image is finally examined in terms of quality gained. The losses and gains in image quality are measured on
 a Standard Quality Scale (SQS), where one unit corresponds to 1 JND. Initial results show that the quality lost due to visual accommodation impairments can be accurately measured with the modified ruler method. The loss is scene-dependent. Partial or full quality compensation can be achieved
 for such impairments, using image contrast equalization; the level of quality gained also scenedependent.</p>
      </abstract>
      <kwd-group>
        <kwd>IMAGE QUALITY</kwd>
        <kwd>VISUALLY IMPAIRED</kwd>
        <kwd>VISUAL DEFICIENCY</kwd>
        <kwd>QUALITY RULER</kwd>
        <kwd>IMAGE EQUALIZATION</kwd>
        <kwd>ISO 20462-3:2012</kwd>
        <kwd>IMAGE OPTIMIZATION</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
