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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.2470-1173.2018.13.IPAS-260</article-id>
      <article-id pub-id-type="sici">2470-1173(20180128)2018:13L.2601;1-</article-id>
      <article-id pub-id-type="publisher-id">s7.phd</article-id>
      <article-id pub-id-type="other">/ist/ei/2018/00002018/00000013/art00007</article-id>
      <article-categories>
        <subj-group>
          <subject>Articles</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Robust linearized combined metrics of image visual quality</article-title>
      </title-group>
      <contrib-group>
        <contrib>
          <name>
            <surname>Ieremeiev</surname>
            <given-names>Oleg</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Lukin</surname>
            <given-names>Vladimir</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Ponomarenko</surname>
            <given-names>Nikolay</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Egiazarian</surname>
            <given-names>Karen</given-names>
          </name>
        </contrib>
      </contrib-group>
      <pub-date>
        <day>28</day>
        <month>01</month>
        <year>2018</year>
      </pub-date>
      <volume>2018</volume>
      <issue>13</issue>
      <fpage>260-1</fpage>
      <lpage>260-6</lpage>
      <permissions>
        <copyright-year>2018</copyright-year>
      </permissions>
      <abstract>
        <p>Existing full-reference metrics still do not provide a desirable degree of adequacy to a human visual perception, for evaluation of images with different types and levels of distortions. One reason for this is that it is difficult to incorporate the peculiarities of human visual system
 in the metrics design. In this paper, a robust approach to full-reference metrics' design is proposed, based on a combination of several existing full-reference metrics. A preliminary linearization (fitting) of the dependence of MOS with respect to the components metrics is performed in order
 to compensate shortcomings of each component. The proposed method is tested on several known databases, and demonstrate better performance than existing metrics.</p>
      </abstract>
      <kwd-group>
        <kwd>IMAGE VISUAL QUALITY ASSESSMENT</kwd>
        <kwd>FULL-REFERENCE METRICS</kwd>
        <kwd>COMBINED METRICS</kwd>
        <kwd>ROBUST METRICS</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
