Journal article Open Access

# A Reduced Reference Metric for Visual Quality Evaluation of Point Cloud Contents

Irene Viola; Pablo Cesar

### DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<identifier identifierType="URL">https://zenodo.org/record/4429816</identifier>
<creators>
<creator>
<creatorName>Irene Viola</creatorName>
<affiliation>CWI</affiliation>
</creator>
<creator>
<creatorName>Pablo Cesar</creatorName>
<affiliation>CWI</affiliation>
</creator>
</creators>
<titles>
<title>A Reduced Reference Metric for Visual Quality Evaluation of Point Cloud Contents</title>
</titles>
<publisher>Zenodo</publisher>
<publicationYear>2020</publicationYear>
<subjects>
<subject>objective quality metric, point cloud, compres- sion, reduced reference metric</subject>
</subjects>
<dates>
<date dateType="Issued">2020-09-15</date>
</dates>
<language>en</language>
<resourceType resourceTypeGeneral="JournalArticle"/>
<alternateIdentifiers>
<alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4429816</alternateIdentifier>
</alternateIdentifiers>
<relatedIdentifiers>
<relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/LSP.2020.3024065</relatedIdentifier>
<relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/vrtogether-h2020</relatedIdentifier>
</relatedIdentifiers>
<version>pre-print</version>
<rightsList>
<rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
</rightsList>
<descriptions>
<description descriptionType="Abstract">&lt;p&gt;Point cloud representation has seen a surge of popularity in recent years, thanks to its capability to reproduce volumetric scenes in immersive scenarios. New compression solutions for streaming of point cloud contents have been proposed, which require objective quality metrics to reliably assess the level of degradation introduced by coding and transmission distortions. In this context, reduced reference metrics aim to predict the visual quality of the transmitted contents, while requiring only a small set of features to be sent in addition to the streamed media. In this paper, we propose a reduced reference metric to predict the quality of point cloud contents under compression distortions. To do so, we extract a small set of statistical features from the reference point cloud in the geometry, color and normal vector domain, which can be used at the receiver side to assess the visual degradation of the content. Using publicly available ground-truth datasets, we compare the performance of our metric to widely-used full reference metrics. Results demonstrate that our metric is able to effectively predict the level of distortion in the degraded point cloud contents, achieving high correlation values with respect to subjective scores.&lt;/p&gt;</description>
</descriptions>
<fundingReferences>
<fundingReference>
<funderName>European Commission</funderName>
<funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
<awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/762111/">762111</awardNumber>
<awardTitle>An end-to-end system for the production and delivery of photorealistic social immersive virtual reality experiences</awardTitle>
</fundingReference>
</fundingReferences>
</resource>

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