Fast sparse fractal image compression
As a structure-based image compression technology, fractal image compression (FIC) has been applied not only in image coding but also in many important image processing algorithms. However, two main bottlenecks restrained the develop and application of FIC for a long time. First, the encoding phase...
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| Veröffentlicht in: | PloS one 2017-09, Vol.12 (9), p.e0184408-e0184408 |
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| description | As a structure-based image compression technology, fractal image compression (FIC) has been applied not only in image coding but also in many important image processing algorithms. However, two main bottlenecks restrained the develop and application of FIC for a long time. First, the encoding phase of FIC is time-consuming. Second, the quality of the reconstructed images for some images which have low structure-similarity is usually unacceptable. Based on the absolute value of Pearson's correlation coefficient (APCC), we had proposed an accelerating method to significantly speed up the encoding of FIC. In this paper, we make use of the sparse searching strategy to greatly improve the quality of the reconstructed images in FIC. We call it the sparse fractal image compression (SFIC). Furthermore, we combine both the APCC-based accelerating method and the sparse searching strategy to propose the fast sparse fractal image compression (FSFIC), which can effectively improve the two main bottlenecks of FIC. The experimental results show that the proposed algorithm greatly improves both the efficiency and effectiveness of FIC. |
| doi_str_mv | 10.1371/journal.pone.0184408 |
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However, two main bottlenecks restrained the develop and application of FIC for a long time. First, the encoding phase of FIC is time-consuming. Second, the quality of the reconstructed images for some images which have low structure-similarity is usually unacceptable. Based on the absolute value of Pearson's correlation coefficient (APCC), we had proposed an accelerating method to significantly speed up the encoding of FIC. In this paper, we make use of the sparse searching strategy to greatly improve the quality of the reconstructed images in FIC. We call it the sparse fractal image compression (SFIC). Furthermore, we combine both the APCC-based accelerating method and the sparse searching strategy to propose the fast sparse fractal image compression (FSFIC), which can effectively improve the two main bottlenecks of FIC. The experimental results show that the proposed algorithm greatly improves both the efficiency and effectiveness of FIC.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0184408</identifier><identifier>PMID: 28886137</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Artificial intelligence ; Biology and life sciences ; Classification ; Coding ; Compression ; Computer simulation ; Correlation coefficient ; Correlation coefficients ; Data Compression ; Dictionaries ; Engineering ; Engineering and Technology ; Fractals ; Handwriting ; Image coding ; Image compression ; Image processing ; Image Processing, Computer-Assisted ; Image quality ; Image reconstruction ; Information processing ; Laboratories ; Physical Sciences ; Quality ; Research and Analysis Methods ; Robotics ; Searching ; Signal Processing, Computer-Assisted</subject><ispartof>PloS one, 2017-09, Vol.12 (9), p.e0184408-e0184408</ispartof><rights>2017 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2017 Wang et al 2017 Wang et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c526t-a46fbc138b9a348d9607268048f977d488b7572b37d96f3a719222d0a6520f6d3</citedby><cites>FETCH-LOGICAL-c526t-a46fbc138b9a348d9607268048f977d488b7572b37d96f3a719222d0a6520f6d3</cites><orcidid>0000-0002-4284-3933</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5590925/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5590925/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28886137$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Zhang, Zhao</contributor><creatorcontrib>Wang, Jianji</creatorcontrib><creatorcontrib>Chen, Pei</creatorcontrib><creatorcontrib>Xi, Bao</creatorcontrib><creatorcontrib>Liu, Jianyi</creatorcontrib><creatorcontrib>Zhang, Yi</creatorcontrib><creatorcontrib>Yu, Shujian</creatorcontrib><title>Fast sparse fractal image compression</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>As a structure-based image compression technology, fractal image compression (FIC) has been applied not only in image coding but also in many important image processing algorithms. However, two main bottlenecks restrained the develop and application of FIC for a long time. First, the encoding phase of FIC is time-consuming. Second, the quality of the reconstructed images for some images which have low structure-similarity is usually unacceptable. Based on the absolute value of Pearson's correlation coefficient (APCC), we had proposed an accelerating method to significantly speed up the encoding of FIC. In this paper, we make use of the sparse searching strategy to greatly improve the quality of the reconstructed images in FIC. We call it the sparse fractal image compression (SFIC). Furthermore, we combine both the APCC-based accelerating method and the sparse searching strategy to propose the fast sparse fractal image compression (FSFIC), which can effectively improve the two main bottlenecks of FIC. The experimental results show that the proposed algorithm greatly improves both the efficiency and effectiveness of FIC.</description><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Biology and life sciences</subject><subject>Classification</subject><subject>Coding</subject><subject>Compression</subject><subject>Computer simulation</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Data Compression</subject><subject>Dictionaries</subject><subject>Engineering</subject><subject>Engineering and Technology</subject><subject>Fractals</subject><subject>Handwriting</subject><subject>Image coding</subject><subject>Image compression</subject><subject>Image processing</subject><subject>Image Processing, Computer-Assisted</subject><subject>Image quality</subject><subject>Image reconstruction</subject><subject>Information processing</subject><subject>Laboratories</subject><subject>Physical 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Jianji</au><au>Chen, Pei</au><au>Xi, Bao</au><au>Liu, Jianyi</au><au>Zhang, Yi</au><au>Yu, Shujian</au><au>Zhang, Zhao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fast sparse fractal image compression</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2017-09-08</date><risdate>2017</risdate><volume>12</volume><issue>9</issue><spage>e0184408</spage><epage>e0184408</epage><pages>e0184408-e0184408</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>As a structure-based image compression technology, fractal image compression (FIC) has been applied not only in image coding but also in many important image processing algorithms. However, two main bottlenecks restrained the develop and application of FIC for a long time. First, the encoding phase of FIC is time-consuming. Second, the quality of the reconstructed images for some images which have low structure-similarity is usually unacceptable. Based on the absolute value of Pearson's correlation coefficient (APCC), we had proposed an accelerating method to significantly speed up the encoding of FIC. In this paper, we make use of the sparse searching strategy to greatly improve the quality of the reconstructed images in FIC. We call it the sparse fractal image compression (SFIC). Furthermore, we combine both the APCC-based accelerating method and the sparse searching strategy to propose the fast sparse fractal image compression (FSFIC), which can effectively improve the two main bottlenecks of FIC. The experimental results show that the proposed algorithm greatly improves both the efficiency and effectiveness of FIC.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>28886137</pmid><doi>10.1371/journal.pone.0184408</doi><orcidid>https://orcid.org/0000-0002-4284-3933</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Artificial intelligence Biology and life sciences Classification Coding Compression Computer simulation Correlation coefficient Correlation coefficients Data Compression Dictionaries Engineering Engineering and Technology Fractals Handwriting Image coding Image compression Image processing Image Processing, Computer-Assisted Image quality Image reconstruction Information processing Laboratories Physical Sciences Quality Research and Analysis Methods Robotics Searching Signal Processing, Computer-Assisted |
| title | Fast sparse fractal image compression |
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