A range/domain approximation error-based approach for fractal image compression
Fractals can be an effective approach for several applications other than image coding and transmission: database indexing, texture mapping, and even pattern recognition problems such as writer authentication. However, fractal-based algorithms are strongly asymmetric because, in spite of the lineari...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on image processing 2006-01, Vol.15 (1), p.89-97 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 97 |
|---|---|
| container_issue | 1 |
| container_start_page | 89 |
| container_title | IEEE transactions on image processing |
| container_volume | 15 |
| creator | Distasi, R. Nappi, M. Riccio, D. |
| description | Fractals can be an effective approach for several applications other than image coding and transmission: database indexing, texture mapping, and even pattern recognition problems such as writer authentication. However, fractal-based algorithms are strongly asymmetric because, in spite of the linearity of the decoding phase, the coding process is much more time consuming. Many different solutions have been proposed for this problem, but there is not yet a standard for fractal coding. This paper proposes a method to reduce the complexity of the image coding phase by classifying the blocks according to an approximation error measure. It is formally shown that postponing range/spl bsol/slash domain comparisons with respect to a preset block, it is possible to reduce drastically the amount of operations needed to encode each range. The proposed method has been compared with three other fractal coding methods, showing under which circumstances it performs better in terms of both bit rate and/or computing time. |
| doi_str_mv | 10.1109/TIP.2005.860334 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_pascalfrancis_primary_17389832</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>1556627</ieee_id><sourcerecordid>70714043</sourcerecordid><originalsourceid>FETCH-LOGICAL-c502t-a549a89d00a5cca38a0f26e92807f76846562a886fa76cdbf4a9a534610fa12b3</originalsourceid><addsrcrecordid>eNqF0ctrGzEQB2BRWppHe86hEJZASi9rj97S0YQ-AoH0kJ7FWCslG3ZXrmRD899XZg2GHtKTBPpmpNGPkAsKC0rBLh9ufy4YgFwYBZyLN-SUWkFbAMHe1j1I3Woq7Ak5K-UZgApJ1XtyQpXgUnJ7Su5XTcbpMSy7NGI_NbjZ5PSnH3Hbp6kJOafcrrGEbj5B_9TElJuY0W9xaCp8DI1P4yaHUmrJB_Iu4lDCx8N6Tn59-_pw86O9u_9-e7O6a70Etm1RCovGdgAovUduECJTwTIDOmplhJKKoTEqola-W0eBFiUXikJEytb8nHye-9ZH_d6FsnVjX3wYBpxC2hWnoc4Ngv8X1huNMIJV-OVVSJWmEsByUenVP_Q57fJU53VGaS4VN7Si5Yx8TqXkEN0m1-_KL46C24fnanhuH56bw6sVl4e2u_UYuqM_pFXB9QFg8TjUECbfl6PT3FjD96N8ml0fQjgeS6kU0_wvoOqoxQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>867356381</pqid></control><display><type>article</type><title>A range/domain approximation error-based approach for fractal image compression</title><source>IEEE Electronic Library (IEL)</source><creator>Distasi, R. ; Nappi, M. ; Riccio, D.</creator><creatorcontrib>Distasi, R. ; Nappi, M. ; Riccio, D.</creatorcontrib><description>Fractals can be an effective approach for several applications other than image coding and transmission: database indexing, texture mapping, and even pattern recognition problems such as writer authentication. However, fractal-based algorithms are strongly asymmetric because, in spite of the linearity of the decoding phase, the coding process is much more time consuming. Many different solutions have been proposed for this problem, but there is not yet a standard for fractal coding. This paper proposes a method to reduce the complexity of the image coding phase by classifying the blocks according to an approximation error measure. It is formally shown that postponing range/spl bsol/slash domain comparisons with respect to a preset block, it is possible to reduce drastically the amount of operations needed to encode each range. The proposed method has been compared with three other fractal coding methods, showing under which circumstances it performs better in terms of both bit rate and/or computing time.</description><identifier>ISSN: 1057-7149</identifier><identifier>EISSN: 1941-0042</identifier><identifier>DOI: 10.1109/TIP.2005.860334</identifier><identifier>PMID: 16435539</identifier><identifier>CODEN: IIPRE4</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Algorithms ; Applied sciences ; Approximation ; Approximation error ; Authentication ; Classification ; Coding ; Coding, codes ; Computer Graphics ; Computer Simulation ; Data Compression - methods ; Data Interpretation, Statistical ; Decoding ; Exact sciences and technology ; feature vector ; Fractal analysis ; fractal image compression ; Fractals ; Image coding ; Image databases ; Image Enhancement - methods ; Image Interpretation, Computer-Assisted - methods ; Image processing ; Indexing ; Information, signal and communications theory ; Linearity ; Mathematical analysis ; Models, Statistical ; Numerical Analysis, Computer-Assisted ; Pattern recognition ; Phase measurement ; Signal and communications theory ; Signal processing ; Signal Processing, Computer-Assisted ; Studies ; Telecommunications and information theory</subject><ispartof>IEEE transactions on image processing, 2006-01, Vol.15 (1), p.89-97</ispartof><rights>2006 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2006</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c502t-a549a89d00a5cca38a0f26e92807f76846562a886fa76cdbf4a9a534610fa12b3</citedby><cites>FETCH-LOGICAL-c502t-a549a89d00a5cca38a0f26e92807f76846562a886fa76cdbf4a9a534610fa12b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1556627$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,4010,27900,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1556627$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=17389832$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/16435539$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Distasi, R.</creatorcontrib><creatorcontrib>Nappi, M.</creatorcontrib><creatorcontrib>Riccio, D.</creatorcontrib><title>A range/domain approximation error-based approach for fractal image compression</title><title>IEEE transactions on image processing</title><addtitle>TIP</addtitle><addtitle>IEEE Trans Image Process</addtitle><description>Fractals can be an effective approach for several applications other than image coding and transmission: database indexing, texture mapping, and even pattern recognition problems such as writer authentication. However, fractal-based algorithms are strongly asymmetric because, in spite of the linearity of the decoding phase, the coding process is much more time consuming. Many different solutions have been proposed for this problem, but there is not yet a standard for fractal coding. This paper proposes a method to reduce the complexity of the image coding phase by classifying the blocks according to an approximation error measure. It is formally shown that postponing range/spl bsol/slash domain comparisons with respect to a preset block, it is possible to reduce drastically the amount of operations needed to encode each range. The proposed method has been compared with three other fractal coding methods, showing under which circumstances it performs better in terms of both bit rate and/or computing time.</description><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Approximation</subject><subject>Approximation error</subject><subject>Authentication</subject><subject>Classification</subject><subject>Coding</subject><subject>Coding, codes</subject><subject>Computer Graphics</subject><subject>Computer Simulation</subject><subject>Data Compression - methods</subject><subject>Data Interpretation, Statistical</subject><subject>Decoding</subject><subject>Exact sciences and technology</subject><subject>feature vector</subject><subject>Fractal analysis</subject><subject>fractal image compression</subject><subject>Fractals</subject><subject>Image coding</subject><subject>Image databases</subject><subject>Image Enhancement - methods</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Image processing</subject><subject>Indexing</subject><subject>Information, signal and communications theory</subject><subject>Linearity</subject><subject>Mathematical analysis</subject><subject>Models, Statistical</subject><subject>Numerical Analysis, Computer-Assisted</subject><subject>Pattern recognition</subject><subject>Phase measurement</subject><subject>Signal and communications theory</subject><subject>Signal processing</subject><subject>Signal Processing, Computer-Assisted</subject><subject>Studies</subject><subject>Telecommunications and information theory</subject><issn>1057-7149</issn><issn>1941-0042</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNqF0ctrGzEQB2BRWppHe86hEJZASi9rj97S0YQ-AoH0kJ7FWCslG3ZXrmRD899XZg2GHtKTBPpmpNGPkAsKC0rBLh9ufy4YgFwYBZyLN-SUWkFbAMHe1j1I3Woq7Ak5K-UZgApJ1XtyQpXgUnJ7Su5XTcbpMSy7NGI_NbjZ5PSnH3Hbp6kJOafcrrGEbj5B_9TElJuY0W9xaCp8DI1P4yaHUmrJB_Iu4lDCx8N6Tn59-_pw86O9u_9-e7O6a70Etm1RCovGdgAovUduECJTwTIDOmplhJKKoTEqola-W0eBFiUXikJEytb8nHye-9ZH_d6FsnVjX3wYBpxC2hWnoc4Ngv8X1huNMIJV-OVVSJWmEsByUenVP_Q57fJU53VGaS4VN7Si5Yx8TqXkEN0m1-_KL46C24fnanhuH56bw6sVl4e2u_UYuqM_pFXB9QFg8TjUECbfl6PT3FjD96N8ml0fQjgeS6kU0_wvoOqoxQ</recordid><startdate>200601</startdate><enddate>200601</enddate><creator>Distasi, R.</creator><creator>Nappi, M.</creator><creator>Riccio, D.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</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><scope>F28</scope><scope>FR3</scope><scope>7X8</scope></search><sort><creationdate>200601</creationdate><title>A range/domain approximation error-based approach for fractal image compression</title><author>Distasi, R. ; Nappi, M. ; Riccio, D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c502t-a549a89d00a5cca38a0f26e92807f76846562a886fa76cdbf4a9a534610fa12b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Algorithms</topic><topic>Applied sciences</topic><topic>Approximation</topic><topic>Approximation error</topic><topic>Authentication</topic><topic>Classification</topic><topic>Coding</topic><topic>Coding, codes</topic><topic>Computer Graphics</topic><topic>Computer Simulation</topic><topic>Data Compression - methods</topic><topic>Data Interpretation, Statistical</topic><topic>Decoding</topic><topic>Exact sciences and technology</topic><topic>feature vector</topic><topic>Fractal analysis</topic><topic>fractal image compression</topic><topic>Fractals</topic><topic>Image coding</topic><topic>Image databases</topic><topic>Image Enhancement - methods</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>Image processing</topic><topic>Indexing</topic><topic>Information, signal and communications theory</topic><topic>Linearity</topic><topic>Mathematical analysis</topic><topic>Models, Statistical</topic><topic>Numerical Analysis, Computer-Assisted</topic><topic>Pattern recognition</topic><topic>Phase measurement</topic><topic>Signal and communications theory</topic><topic>Signal processing</topic><topic>Signal Processing, Computer-Assisted</topic><topic>Studies</topic><topic>Telecommunications and information theory</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Distasi, R.</creatorcontrib><creatorcontrib>Nappi, M.</creatorcontrib><creatorcontrib>Riccio, D.</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>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</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><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transactions on image processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Distasi, R.</au><au>Nappi, M.</au><au>Riccio, D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A range/domain approximation error-based approach for fractal image compression</atitle><jtitle>IEEE transactions on image processing</jtitle><stitle>TIP</stitle><addtitle>IEEE Trans Image Process</addtitle><date>2006-01</date><risdate>2006</risdate><volume>15</volume><issue>1</issue><spage>89</spage><epage>97</epage><pages>89-97</pages><issn>1057-7149</issn><eissn>1941-0042</eissn><coden>IIPRE4</coden><abstract>Fractals can be an effective approach for several applications other than image coding and transmission: database indexing, texture mapping, and even pattern recognition problems such as writer authentication. However, fractal-based algorithms are strongly asymmetric because, in spite of the linearity of the decoding phase, the coding process is much more time consuming. Many different solutions have been proposed for this problem, but there is not yet a standard for fractal coding. This paper proposes a method to reduce the complexity of the image coding phase by classifying the blocks according to an approximation error measure. It is formally shown that postponing range/spl bsol/slash domain comparisons with respect to a preset block, it is possible to reduce drastically the amount of operations needed to encode each range. The proposed method has been compared with three other fractal coding methods, showing under which circumstances it performs better in terms of both bit rate and/or computing time.</abstract><cop>New York, NY</cop><pub>IEEE</pub><pmid>16435539</pmid><doi>10.1109/TIP.2005.860334</doi><tpages>9</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1057-7149 |
| ispartof | IEEE transactions on image processing, 2006-01, Vol.15 (1), p.89-97 |
| issn | 1057-7149 1941-0042 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_17389832 |
| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Applied sciences Approximation Approximation error Authentication Classification Coding Coding, codes Computer Graphics Computer Simulation Data Compression - methods Data Interpretation, Statistical Decoding Exact sciences and technology feature vector Fractal analysis fractal image compression Fractals Image coding Image databases Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image processing Indexing Information, signal and communications theory Linearity Mathematical analysis Models, Statistical Numerical Analysis, Computer-Assisted Pattern recognition Phase measurement Signal and communications theory Signal processing Signal Processing, Computer-Assisted Studies Telecommunications and information theory |
| title | A range/domain approximation error-based approach for fractal image compression |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-08T07%3A57%3A50IST&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=A%20range/domain%20approximation%20error-based%20approach%20for%20fractal%20image%20compression&rft.jtitle=IEEE%20transactions%20on%20image%20processing&rft.au=Distasi,%20R.&rft.date=2006-01&rft.volume=15&rft.issue=1&rft.spage=89&rft.epage=97&rft.pages=89-97&rft.issn=1057-7149&rft.eissn=1941-0042&rft.coden=IIPRE4&rft_id=info:doi/10.1109/TIP.2005.860334&rft_dat=%3Cproquest_RIE%3E70714043%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=867356381&rft_id=info:pmid/16435539&rft_ieee_id=1556627&rfr_iscdi=true |