Mahalanobis Based Point to Distribution Metric for Point Cloud Geometry Quality Evaluation
Nowadays, point clouds (PCs) are a promising representation format for immersive content and target several emerging applications, notably in virtual and augmented reality. However, efficient coding solutions are critically needed due to the large amount of PC data required for high quality user exp...
Gespeichert in:
| Veröffentlicht in: | IEEE signal processing letters 2020, Vol.27, p.1350-1354 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1354 |
|---|---|
| container_issue | |
| container_start_page | 1350 |
| container_title | IEEE signal processing letters |
| container_volume | 27 |
| creator | Javaheri, Alireza Brites, Catarina Pereira, Fernando Ascenso, Joao |
| description | Nowadays, point clouds (PCs) are a promising representation format for immersive content and target several emerging applications, notably in virtual and augmented reality. However, efficient coding solutions are critically needed due to the large amount of PC data required for high quality user experiences. To address these needs, several PC coding standards were developed and thus, objective PC quality metrics able to accurately account for the subjective impact of coding artifacts are needed. In this paper, a scale-invariant PC geometry quality assessment metric is proposed based on a new type of correspondence, namely between a point and a distribution of points. This metric is able to reliably measure the geometry quality for PCs with different intrinsic characteristics and degraded by several coding solutions. Experimental results show the superiority of the proposed PC quality metric over relevant state-of-the-art. |
| doi_str_mv | 10.1109/LSP.2020.3010128 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_journals_2434956219</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9143408</ieee_id><sourcerecordid>2434956219</sourcerecordid><originalsourceid>FETCH-LOGICAL-c291t-37c9d08c7eb6acc03cb19248d58b1b3611c9bf7e01b6f14deecbc406b2c2f5bc3</originalsourceid><addsrcrecordid>eNo9kM1LAzEQxYMoWKt3wUvA89aZ7Fdy1Fqr0GJFvXgJSTaLW7abmuwK_e9NafE0D-b33gyPkGuECSKIu8X7asKAwSQFBGT8hIwwz3nC0gJPo4YSEiGAn5OLENYAwJHnI_K1VN-qVZ3TTaAPKtiKrlzT9bR39LEJvW_00Deuo0sbtaG180dg2rqhonPrNnGzo2-Dapt-R2e_qh3U3nJJzmrVBnt1nGPy-TT7mD4ni9f5y_R-kRgmsE_S0ogKuCmtLpQxkBqNgmW8yrlGHb9HI3RdWkBd1JhV1hptMig0M6zOtUnH5PaQu_XuZ7Chl2s3-C6elCxLM5EXDEWk4EAZ70LwtpZb32yU30kEuW9QxgblvkF5bDBabg6Wxlr7jwuMocDTP7M9bX4</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2434956219</pqid></control><display><type>article</type><title>Mahalanobis Based Point to Distribution Metric for Point Cloud Geometry Quality Evaluation</title><source>IEEE Electronic Library (IEL)</source><creator>Javaheri, Alireza ; Brites, Catarina ; Pereira, Fernando ; Ascenso, Joao</creator><creatorcontrib>Javaheri, Alireza ; Brites, Catarina ; Pereira, Fernando ; Ascenso, Joao</creatorcontrib><description>Nowadays, point clouds (PCs) are a promising representation format for immersive content and target several emerging applications, notably in virtual and augmented reality. However, efficient coding solutions are critically needed due to the large amount of PC data required for high quality user experiences. To address these needs, several PC coding standards were developed and thus, objective PC quality metrics able to accurately account for the subjective impact of coding artifacts are needed. In this paper, a scale-invariant PC geometry quality assessment metric is proposed based on a new type of correspondence, namely between a point and a distribution of points. This metric is able to reliably measure the geometry quality for PCs with different intrinsic characteristics and degraded by several coding solutions. Experimental results show the superiority of the proposed PC quality metric over relevant state-of-the-art.</description><identifier>ISSN: 1070-9908</identifier><identifier>EISSN: 1558-2361</identifier><identifier>DOI: 10.1109/LSP.2020.3010128</identifier><identifier>CODEN: ISPLEM</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject><![CDATA[<named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX"> Mahalanobis</tex-math> </inline-formula> </named-content> distance ; Augmented reality ; Coding ; Coding standards ; Correlation ; Covariance matrices ; Encoding ; Euclidean distance ; Geometry ; Point cloud ; point to distribution ; Quality ; Quality assessment ; Three-dimensional displays ; Virtual reality]]></subject><ispartof>IEEE signal processing letters, 2020, Vol.27, p.1350-1354</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c291t-37c9d08c7eb6acc03cb19248d58b1b3611c9bf7e01b6f14deecbc406b2c2f5bc3</citedby><cites>FETCH-LOGICAL-c291t-37c9d08c7eb6acc03cb19248d58b1b3611c9bf7e01b6f14deecbc406b2c2f5bc3</cites><orcidid>0000-0001-6100-947X ; 0000-0001-9902-5926 ; 0000-0002-6011-4574</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9143408$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,4024,27923,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9143408$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Javaheri, Alireza</creatorcontrib><creatorcontrib>Brites, Catarina</creatorcontrib><creatorcontrib>Pereira, Fernando</creatorcontrib><creatorcontrib>Ascenso, Joao</creatorcontrib><title>Mahalanobis Based Point to Distribution Metric for Point Cloud Geometry Quality Evaluation</title><title>IEEE signal processing letters</title><addtitle>LSP</addtitle><description>Nowadays, point clouds (PCs) are a promising representation format for immersive content and target several emerging applications, notably in virtual and augmented reality. However, efficient coding solutions are critically needed due to the large amount of PC data required for high quality user experiences. To address these needs, several PC coding standards were developed and thus, objective PC quality metrics able to accurately account for the subjective impact of coding artifacts are needed. In this paper, a scale-invariant PC geometry quality assessment metric is proposed based on a new type of correspondence, namely between a point and a distribution of points. This metric is able to reliably measure the geometry quality for PCs with different intrinsic characteristics and degraded by several coding solutions. Experimental results show the superiority of the proposed PC quality metric over relevant state-of-the-art.</description><subject><![CDATA[<named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX"> Mahalanobis</tex-math> </inline-formula> </named-content> distance]]></subject><subject>Augmented reality</subject><subject>Coding</subject><subject>Coding standards</subject><subject>Correlation</subject><subject>Covariance matrices</subject><subject>Encoding</subject><subject>Euclidean distance</subject><subject>Geometry</subject><subject>Point cloud</subject><subject>point to distribution</subject><subject>Quality</subject><subject>Quality assessment</subject><subject>Three-dimensional displays</subject><subject>Virtual reality</subject><issn>1070-9908</issn><issn>1558-2361</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kM1LAzEQxYMoWKt3wUvA89aZ7Fdy1Fqr0GJFvXgJSTaLW7abmuwK_e9NafE0D-b33gyPkGuECSKIu8X7asKAwSQFBGT8hIwwz3nC0gJPo4YSEiGAn5OLENYAwJHnI_K1VN-qVZ3TTaAPKtiKrlzT9bR39LEJvW_00Deuo0sbtaG180dg2rqhonPrNnGzo2-Dapt-R2e_qh3U3nJJzmrVBnt1nGPy-TT7mD4ni9f5y_R-kRgmsE_S0ogKuCmtLpQxkBqNgmW8yrlGHb9HI3RdWkBd1JhV1hptMig0M6zOtUnH5PaQu_XuZ7Chl2s3-C6elCxLM5EXDEWk4EAZ70LwtpZb32yU30kEuW9QxgblvkF5bDBabg6Wxlr7jwuMocDTP7M9bX4</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Javaheri, Alireza</creator><creator>Brites, Catarina</creator><creator>Pereira, Fernando</creator><creator>Ascenso, Joao</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0001-6100-947X</orcidid><orcidid>https://orcid.org/0000-0001-9902-5926</orcidid><orcidid>https://orcid.org/0000-0002-6011-4574</orcidid></search><sort><creationdate>2020</creationdate><title>Mahalanobis Based Point to Distribution Metric for Point Cloud Geometry Quality Evaluation</title><author>Javaheri, Alireza ; Brites, Catarina ; Pereira, Fernando ; Ascenso, Joao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c291t-37c9d08c7eb6acc03cb19248d58b1b3611c9bf7e01b6f14deecbc406b2c2f5bc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic><![CDATA[<named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX"> Mahalanobis</tex-math> </inline-formula> </named-content> distance]]></topic><topic>Augmented reality</topic><topic>Coding</topic><topic>Coding standards</topic><topic>Correlation</topic><topic>Covariance matrices</topic><topic>Encoding</topic><topic>Euclidean distance</topic><topic>Geometry</topic><topic>Point cloud</topic><topic>point to distribution</topic><topic>Quality</topic><topic>Quality assessment</topic><topic>Three-dimensional displays</topic><topic>Virtual reality</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Javaheri, Alireza</creatorcontrib><creatorcontrib>Brites, Catarina</creatorcontrib><creatorcontrib>Pereira, Fernando</creatorcontrib><creatorcontrib>Ascenso, Joao</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE signal processing letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Javaheri, Alireza</au><au>Brites, Catarina</au><au>Pereira, Fernando</au><au>Ascenso, Joao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Mahalanobis Based Point to Distribution Metric for Point Cloud Geometry Quality Evaluation</atitle><jtitle>IEEE signal processing letters</jtitle><stitle>LSP</stitle><date>2020</date><risdate>2020</risdate><volume>27</volume><spage>1350</spage><epage>1354</epage><pages>1350-1354</pages><issn>1070-9908</issn><eissn>1558-2361</eissn><coden>ISPLEM</coden><abstract>Nowadays, point clouds (PCs) are a promising representation format for immersive content and target several emerging applications, notably in virtual and augmented reality. However, efficient coding solutions are critically needed due to the large amount of PC data required for high quality user experiences. To address these needs, several PC coding standards were developed and thus, objective PC quality metrics able to accurately account for the subjective impact of coding artifacts are needed. In this paper, a scale-invariant PC geometry quality assessment metric is proposed based on a new type of correspondence, namely between a point and a distribution of points. This metric is able to reliably measure the geometry quality for PCs with different intrinsic characteristics and degraded by several coding solutions. Experimental results show the superiority of the proposed PC quality metric over relevant state-of-the-art.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/LSP.2020.3010128</doi><tpages>5</tpages><orcidid>https://orcid.org/0000-0001-6100-947X</orcidid><orcidid>https://orcid.org/0000-0001-9902-5926</orcidid><orcidid>https://orcid.org/0000-0002-6011-4574</orcidid></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1070-9908 |
| ispartof | IEEE signal processing letters, 2020, Vol.27, p.1350-1354 |
| issn | 1070-9908 1558-2361 |
| language | eng |
| recordid | cdi_proquest_journals_2434956219 |
| source | IEEE Electronic Library (IEL) |
| subjects | <named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX"> Mahalanobis</tex-math> </inline-formula> </named-content> distance Augmented reality Coding Coding standards Correlation Covariance matrices Encoding Euclidean distance Geometry Point cloud point to distribution Quality Quality assessment Three-dimensional displays Virtual reality |
| title | Mahalanobis Based Point to Distribution Metric for Point Cloud Geometry Quality Evaluation |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-28T19%3A54%3A55IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Mahalanobis%20Based%20Point%20to%20Distribution%20Metric%20for%20Point%20Cloud%20Geometry%20Quality%20Evaluation&rft.jtitle=IEEE%20signal%20processing%20letters&rft.au=Javaheri,%20Alireza&rft.date=2020&rft.volume=27&rft.spage=1350&rft.epage=1354&rft.pages=1350-1354&rft.issn=1070-9908&rft.eissn=1558-2361&rft.coden=ISPLEM&rft_id=info:doi/10.1109/LSP.2020.3010128&rft_dat=%3Cproquest_RIE%3E2434956219%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2434956219&rft_id=info:pmid/&rft_ieee_id=9143408&rfr_iscdi=true |