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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.2470-1173.2017.14.HVEI-135</article-id>
      <article-id pub-id-type="sici">2470-1173(20170129)2017:14L.151;1-</article-id>
      <article-id pub-id-type="publisher-id">s19.phd</article-id>
      <article-id pub-id-type="other">/ist/ei/2017/00002017/00000014/art00019</article-id>
      <article-categories>
        <subj-group>
          <subject>Articles</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Robust Dynamic Range Computation for High Dynamic Range Content</article-title>
      </title-group>
      <contrib-group>
        <contrib>
          <name>
            <surname>Hulusic</surname>
            <given-names>Vedad</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Valenzise</surname>
            <given-names>Giuseppe</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Debattista</surname>
            <given-names>Kurt</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Dufaux</surname>
            <given-names>Frédéric</given-names>
          </name>
        </contrib>
      </contrib-group>
      <pub-date>
        <day>29</day>
        <month>01</month>
        <year>2017</year>
      </pub-date>
      <volume>2017</volume>
      <issue>14</issue>
      <fpage>151</fpage>
      <lpage>155</lpage>
      <permissions>
        <copyright-year>2017</copyright-year>
      </permissions>
      <abstract>
        <p>High dynamic range (HDR) imaging has become an important topic in both academic and industrial domains. Nevertheless, the concept of dynamic range (DR), which underpins HDR, and the way it is measured are still not clearly understood. The current approach to measure DR results in a
 poor correlation with perceptual scores (r ≈ 0.6). In this paper, we analyze the limitations of the existing DR measure, and propose several options to predict more accurately subjective DR judgments. Compared to the traditional DR estimates, the proposed measures show significant improvements
 in Spearman's and Pearson's correlations with subjective data (up to r ≈ 0.9). Despite their straightforward nature, these improvements are particularly evident in specific cases, where the scores obtained by using the classical measure have the highest error compared to the perceptual
 mean opinion score.</p>
      </abstract>
      <kwd-group>
        <kwd>HIGH DYNAMIC RANGE</kwd>
        <kwd>PERCEPTION</kwd>
        <kwd>IMAGE PYRAMID</kwd>
        <kwd>OBJECTIVE METRIC</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
