Partitioned vector quantization: application to lossless compression of hyperspectral images
A novel design for a vector quantizer that uses multiple codebooks of variable dimensionality is proposed. High dimensional source vectors are first partitioned into two or more subvectors of (possibly) different length and then, each subvector is individually encoded with an appropriate codebook. F...
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| creator | Motta, G. Rizzo, F. Storer, J.A. |
| description | A novel design for a vector quantizer that uses multiple codebooks of variable dimensionality is proposed. High dimensional source vectors are first partitioned into two or more subvectors of (possibly) different length and then, each subvector is individually encoded with an appropriate codebook. Further redundancy is exploited by conditional entropy coding of the subvectors indices. This scheme allows practical quantization of high dimensional vectors in which each vector component is allowed to have different alphabet and distribution. This is typically the case of the pixels representing a hyperspectral image. We present experimental results in the lossless and near-lossless encoding of such images. The method can be easily adapted to lossy coding. |
| doi_str_mv | 10.1109/ICASSP.2003.1199152 |
| format | Conference Proceeding |
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The method can be easily adapted to lossy coding.</description><subject>Application software</subject><subject>Computer science</subject><subject>Distortion measurement</subject><subject>Entropy</subject><subject>Hyperspectral imaging</subject><subject>Image coding</subject><subject>Pixel</subject><subject>Principal component analysis</subject><subject>Spatial resolution</subject><subject>Vector quantization</subject><issn>1520-6149</issn><issn>2379-190X</issn><isbn>9780780376632</isbn><isbn>0780376633</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2003</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNp9j0FrAjEQhQdbwa31F3jJH1g72djdjTeRlvYm2EMPgoTt2EaiiZlYsL--sXguDMx73zweDMBY4kRK1A-vi_lqtZxUiCoDreVj1YOiUo0upcb3GxjppsU8qqlrVd1CkRNY1nKqB3DHvEPEtpm2BayXJiabrD_Qh_imLvkojidzSPbHXOhMmBCc7f6MSF44z-yIWXR-H2IWF-634uscKHLIDdE4Yffmk_ge-lvjmEbXPYTx89Pb4qW0RLQJMafieXN9QP1__QVj70ls</recordid><startdate>2003</startdate><enddate>2003</enddate><creator>Motta, G.</creator><creator>Rizzo, F.</creator><creator>Storer, J.A.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>2003</creationdate><title>Partitioned vector quantization: application to lossless compression of hyperspectral images</title><author>Motta, G. ; Rizzo, F. ; Storer, J.A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_11991523</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2003</creationdate><topic>Application software</topic><topic>Computer science</topic><topic>Distortion measurement</topic><topic>Entropy</topic><topic>Hyperspectral imaging</topic><topic>Image coding</topic><topic>Pixel</topic><topic>Principal component analysis</topic><topic>Spatial resolution</topic><topic>Vector quantization</topic><toplevel>online_resources</toplevel><creatorcontrib>Motta, G.</creatorcontrib><creatorcontrib>Rizzo, F.</creatorcontrib><creatorcontrib>Storer, J.A.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Motta, G.</au><au>Rizzo, F.</au><au>Storer, J.A.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Partitioned vector quantization: application to lossless compression of hyperspectral images</atitle><btitle>2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)</btitle><stitle>ICASSP</stitle><date>2003</date><risdate>2003</risdate><volume>3</volume><spage>III</spage><epage>241</epage><pages>III-241</pages><issn>1520-6149</issn><eissn>2379-190X</eissn><isbn>9780780376632</isbn><isbn>0780376633</isbn><abstract>A novel design for a vector quantizer that uses multiple codebooks of variable dimensionality is proposed. High dimensional source vectors are first partitioned into two or more subvectors of (possibly) different length and then, each subvector is individually encoded with an appropriate codebook. Further redundancy is exploited by conditional entropy coding of the subvectors indices. This scheme allows practical quantization of high dimensional vectors in which each vector component is allowed to have different alphabet and distribution. This is typically the case of the pixels representing a hyperspectral image. We present experimental results in the lossless and near-lossless encoding of such images. The method can be easily adapted to lossy coding.</abstract><pub>IEEE</pub><doi>10.1109/ICASSP.2003.1199152</doi></addata></record> |
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| identifier | ISSN: 1520-6149 |
| ispartof | 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03), 2003, Vol.3, p.III-241 |
| issn | 1520-6149 2379-190X |
| language | eng |
| recordid | cdi_ieee_primary_1199152 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Application software Computer science Distortion measurement Entropy Hyperspectral imaging Image coding Pixel Principal component analysis Spatial resolution Vector quantization |
| title | Partitioned vector quantization: application to lossless compression of hyperspectral images |
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