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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-loc>7003 Kilworth Lane, Springfield, VA 22151 USA</publisher-loc>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.2352/ISSN.2470-1173.2019.15.AVM-043</article-id>
      <article-id pub-id-type="sici">2470-1173(20190113)2019:15L.431;1-</article-id>
      <article-id pub-id-type="publisher-id">ei_24701173_v2019n15_input/s13.xml</article-id>
      <article-id pub-id-type="other">/ist/ei/2019/00002019/00000015/art00013</article-id>
      <article-categories>
        <subj-group>
          <subject>Articles</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Image-based compression of LiDAR sensor data</article-title>
      </title-group>
      <contrib-group>
        <contrib>
          <name>
            <surname>Beek</surname>
            <given-names>Peter van</given-names>
          </name>
        </contrib>
      </contrib-group>
      <pub-date>
        <day>13</day>
        <month>01</month>
        <year>2019</year>
      </pub-date>
      <volume>2019</volume>
      <issue>15</issue>
      <fpage>43-1</fpage>
      <lpage>43-7</lpage>
      <permissions>
        <copyright-year>2019</copyright-year>
      </permissions>
      <abstract>
        <p>
          <italic>Our goal is to develop methods for lossless encoding of automotive lidar sensor data with very low computational complexity and high compression ratio. In this paper, we propose a solution that is based on organizing and packing lidar data into a 2-D image array and subsequently
 using existing image compression methods. This approach leverages image compression technology that has been developed and proven over many years of R&amp;D, standardization, and wide deployment. In our approach, the X,Y,Z coordinates of lidar scan points are quantized, packed into one or
 more 2-D images, and subsequently compressed by an image codec. In addition, lidar scan points are re-ordered to optimize spatial prediction and compression efficiency. We have obtained initial results on automotive lidar data scans using several compression engines. Results using PNG and
 JPEG-LS and using very simple packing techniques show significant compression gains over traditional lidar data coding methods.</italic>
        </p>
      </abstract>
      <kwd-group>
        <kwd>Advanced driving assistance systems (ADAS)</kwd>
        <kwd>Autonomous driving (AD)</kwd>
        <kwd>Lidar sensors</kwd>
        <kwd>Data compression</kwd>
        <kwd>Image Compression</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
