Mean square error approximation for wavelet-based semiregular mesh compression
The objective of this paper is to propose an efficient model-based bit allocation process optimizing the performances of a wavelet coder for semiregular meshes. More precisely, this process should compute the best quantizers for the wavelet coefficient subbands that minimize the reconstructed mean s...
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| Veröffentlicht in: | IEEE transactions on visualization and computer graphics 2006-07, Vol.12 (4), p.649-657 |
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| description | The objective of this paper is to propose an efficient model-based bit allocation process optimizing the performances of a wavelet coder for semiregular meshes. More precisely, this process should compute the best quantizers for the wavelet coefficient subbands that minimize the reconstructed mean square error for one specific target bitrate. In order to design a fast and low complex allocation process, we propose an approximation of the reconstructed mean square error relative to the coding of semiregular mesh geometry. This error is expressed directly from the quantization errors of each coefficient subband. For that purpose, we have to take into account the influence of the wavelet filters on the quantized coefficients. Furthermore, we propose a specific approximation for wavelet transforms based on lifting schemes. Experimentally, we show that, in comparison with a "naive" approximation (depending on the subband levels), using the proposed approximation as distortion criterion during the model-based allocation process improves the performances of a wavelet-based coder for any model, any bitrate, and any lifting scheme. |
| doi_str_mv | 10.1109/TVCG.2006.73 |
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More precisely, this process should compute the best quantizers for the wavelet coefficient subbands that minimize the reconstructed mean square error for one specific target bitrate. In order to design a fast and low complex allocation process, we propose an approximation of the reconstructed mean square error relative to the coding of semiregular mesh geometry. This error is expressed directly from the quantization errors of each coefficient subband. For that purpose, we have to take into account the influence of the wavelet filters on the quantized coefficients. Furthermore, we propose a specific approximation for wavelet transforms based on lifting schemes. Experimentally, we show that, in comparison with a "naive" approximation (depending on the subband levels), using the proposed approximation as distortion criterion during the model-based allocation process improves the performances of a wavelet-based coder for any model, any bitrate, and any lifting scheme.</description><identifier>ISSN: 1077-2626</identifier><identifier>EISSN: 1941-0506</identifier><identifier>DOI: 10.1109/TVCG.2006.73</identifier><identifier>PMID: 16805271</identifier><identifier>CODEN: ITVGEA</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Algorithms ; Allocations ; Approximation ; biorthogonal wavelet ; bit allocation ; Bit rate ; butterfly scheme ; Coefficients ; Compression algorithms ; Computer Graphics ; Computer Science ; Computer Simulation ; Data Compression - methods ; Engineering Sciences ; Errors ; Filters ; Finite element method ; Geometry ; geometry coding ; Image Enhancement - methods ; Image Interpretation, Computer-Assisted - methods ; Image reconstruction ; Imaging, Three-Dimensional - methods ; Lattices ; Least-Squares Analysis ; lifting scheme ; Mathematical analysis ; Mean square error methods ; Mean square errors ; Mean square values ; Models, Statistical ; Numerical Analysis, Computer-Assisted ; Quantization ; semiregular meshes ; Signal and Image Processing ; Signal Processing, Computer-Assisted ; User-Computer Interface ; Wavelet ; Wavelet coefficients ; Wavelet transforms ; Weighted mean square error (MSE)</subject><ispartof>IEEE transactions on visualization and computer graphics, 2006-07, Vol.12 (4), p.649-657</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2006</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c471t-b1992d7b36f8365b94c1569638ff4d69977df60c487e478cf1a155fcf75ed7583</citedby><cites>FETCH-LOGICAL-c471t-b1992d7b36f8365b94c1569638ff4d69977df60c487e478cf1a155fcf75ed7583</cites><orcidid>0000-0002-7012-1735 ; 0000-0001-9885-6445</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1634328$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>230,314,780,784,796,885,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1634328$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/16805271$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://hal.science/hal-00264508$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Payan, F.</creatorcontrib><creatorcontrib>Antonini, M.</creatorcontrib><title>Mean square error approximation for wavelet-based semiregular mesh compression</title><title>IEEE transactions on visualization and computer graphics</title><addtitle>TVCG</addtitle><addtitle>IEEE Trans Vis Comput Graph</addtitle><description>The objective of this paper is to propose an efficient model-based bit allocation process optimizing the performances of a wavelet coder for semiregular meshes. More precisely, this process should compute the best quantizers for the wavelet coefficient subbands that minimize the reconstructed mean square error for one specific target bitrate. In order to design a fast and low complex allocation process, we propose an approximation of the reconstructed mean square error relative to the coding of semiregular mesh geometry. This error is expressed directly from the quantization errors of each coefficient subband. For that purpose, we have to take into account the influence of the wavelet filters on the quantized coefficients. Furthermore, we propose a specific approximation for wavelet transforms based on lifting schemes. Experimentally, we show that, in comparison with a "naive" approximation (depending on the subband levels), using the proposed approximation as distortion criterion during the model-based allocation process improves the performances of a wavelet-based coder for any model, any bitrate, and any lifting scheme.</description><subject>Algorithms</subject><subject>Allocations</subject><subject>Approximation</subject><subject>biorthogonal wavelet</subject><subject>bit allocation</subject><subject>Bit rate</subject><subject>butterfly scheme</subject><subject>Coefficients</subject><subject>Compression algorithms</subject><subject>Computer Graphics</subject><subject>Computer Science</subject><subject>Computer Simulation</subject><subject>Data Compression - methods</subject><subject>Engineering Sciences</subject><subject>Errors</subject><subject>Filters</subject><subject>Finite element method</subject><subject>Geometry</subject><subject>geometry coding</subject><subject>Image Enhancement - methods</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Image reconstruction</subject><subject>Imaging, Three-Dimensional - methods</subject><subject>Lattices</subject><subject>Least-Squares Analysis</subject><subject>lifting scheme</subject><subject>Mathematical analysis</subject><subject>Mean square error methods</subject><subject>Mean square errors</subject><subject>Mean square values</subject><subject>Models, Statistical</subject><subject>Numerical Analysis, Computer-Assisted</subject><subject>Quantization</subject><subject>semiregular meshes</subject><subject>Signal and Image Processing</subject><subject>Signal Processing, Computer-Assisted</subject><subject>User-Computer Interface</subject><subject>Wavelet</subject><subject>Wavelet coefficients</subject><subject>Wavelet transforms</subject><subject>Weighted mean square error (MSE)</subject><issn>1077-2626</issn><issn>1941-0506</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNqF0UtLxDAQAOAgiu-bN0GKB0Wwa96Poyy-YNWLeg3ZdqKVdrsmWx__3pRdVDzoKWHmY5KZQWiH4AEh2JzcPQwvBhRjOVBsCa0Tw0mOBZbL6Y6Vyqmkcg1txPiMMeFcm1W0RqTGgiqyjm6uwU2y-NK5ABmE0IbMTaehfa8aN6vaSeZT5M29Qg2zfOwilFmEpgrw2NUuZA3Ep6xom2mAGBPfQive1RG2F-cmuj8_uxte5qPbi6vh6SgvuCKpEDGGlmrMpNdMirHhBRHSSKa956U0RqnSS1xwrYArXXjiiBC-8EpAqYRmm-hoXvfJ1XYa0mfDh21dZS9PR7aPYUwlF1i_kmQP5za19dJBnNmmigXUtZtA20WrjSSKCt7Lgz-l1EIJxtW_kKbxCi1ogvu_4HPbhUkajdUy5QVjPKHjOSpCG2MA_9URwbbfse13bPsdW8US31vU7MYNlN94sdQEduegAoAf6fQU1ewTg66oeQ</recordid><startdate>20060701</startdate><enddate>20060701</enddate><creator>Payan, F.</creator><creator>Antonini, M.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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More precisely, this process should compute the best quantizers for the wavelet coefficient subbands that minimize the reconstructed mean square error for one specific target bitrate. In order to design a fast and low complex allocation process, we propose an approximation of the reconstructed mean square error relative to the coding of semiregular mesh geometry. This error is expressed directly from the quantization errors of each coefficient subband. For that purpose, we have to take into account the influence of the wavelet filters on the quantized coefficients. Furthermore, we propose a specific approximation for wavelet transforms based on lifting schemes. Experimentally, we show that, in comparison with a "naive" approximation (depending on the subband levels), using the proposed approximation as distortion criterion during the model-based allocation process improves the performances of a wavelet-based coder for any model, any bitrate, and any lifting scheme.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>16805271</pmid><doi>10.1109/TVCG.2006.73</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-7012-1735</orcidid><orcidid>https://orcid.org/0000-0001-9885-6445</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Allocations Approximation biorthogonal wavelet bit allocation Bit rate butterfly scheme Coefficients Compression algorithms Computer Graphics Computer Science Computer Simulation Data Compression - methods Engineering Sciences Errors Filters Finite element method Geometry geometry coding Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image reconstruction Imaging, Three-Dimensional - methods Lattices Least-Squares Analysis lifting scheme Mathematical analysis Mean square error methods Mean square errors Mean square values Models, Statistical Numerical Analysis, Computer-Assisted Quantization semiregular meshes Signal and Image Processing Signal Processing, Computer-Assisted User-Computer Interface Wavelet Wavelet coefficients Wavelet transforms Weighted mean square error (MSE) |
| title | Mean square error approximation for wavelet-based semiregular mesh compression |
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