Embedded image compression based on wavelet pixel classification and sorting
The method of modeling and ordering in wavelet domain is very important to design a successful algorithm of embedded image compression. In this paper, the modeling is limited to "pixel classification," the relationship between wavelet pixels in significance coding. Similarly, the ordering...
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| Veröffentlicht in: | IEEE transactions on image processing 2004-08, Vol.13 (8), p.1011-1017 |
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| container_title | IEEE transactions on image processing |
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| creator | Kewu Peng Kieffer, J.C. |
| description | The method of modeling and ordering in wavelet domain is very important to design a successful algorithm of embedded image compression. In this paper, the modeling is limited to "pixel classification," the relationship between wavelet pixels in significance coding. Similarly, the ordering is limited to "pixel sorting," the coding order of wavelet pixels. We use pixel classification and sorting to provide a better understanding of previous works. The image pixels in wavelet domain are classified and sorted, either explicitly or implicitly, for embedded image compression. A new embedded image code is proposed based on a novel pixel classification and sorting (PCAS) scheme in wavelet domain. In PCAS, pixels to be coded are classified into several quantized contexts based on a large context template and sorted based on their estimated significance probabilities. The purpose of pixel classification is to exploit the intraband correlation in wavelet domain. Pixel sorting employs several fractional bit-plane coding passes to improve the rate-distortion performance. The proposed pixel classification and sorting technique is simple, yet effective, producing an embedded image code with excellent compression performance. In addition, our algorithm is able to provide either spatial or quality scalability with flexible complexity. |
| doi_str_mv | 10.1109/TIP.2004.828441 |
| format | Article |
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(IEEE) 2004</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c434t-2474da3e3ffbed95f6720f89168d312c117140845ade43b1e9393d058414845a3</citedby><cites>FETCH-LOGICAL-c434t-2474da3e3ffbed95f6720f89168d312c117140845ade43b1e9393d058414845a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1315690$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1315690$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=15949454$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/15326843$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Kewu Peng</creatorcontrib><creatorcontrib>Kieffer, J.C.</creatorcontrib><title>Embedded image compression based on wavelet pixel classification and sorting</title><title>IEEE transactions on image processing</title><addtitle>TIP</addtitle><addtitle>IEEE Trans Image Process</addtitle><description>The method of modeling and ordering in wavelet domain is very important to design a successful algorithm of embedded image compression. 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The proposed pixel classification and sorting technique is simple, yet effective, producing an embedded image code with excellent compression performance. In addition, our algorithm is able to provide either spatial or quality scalability with flexible complexity.</description><subject>Algorithm design and analysis</subject><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Classification</subject><subject>Coding</subject><subject>Computer science; control theory; systems</subject><subject>Data Compression - methods</subject><subject>Discrete wavelet transforms</subject><subject>Exact sciences and technology</subject><subject>Image coding</subject><subject>Image compression</subject><subject>Image Enhancement - methods</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Image processing</subject><subject>Information, signal and communications theory</subject><subject>Partitioning algorithms</subject><subject>Pattern Recognition, Automated</subject><subject>Pattern recognition. Digital image processing. 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Digital image processing. Computational geometry</topic><topic>Pixel</topic><topic>Pixels</topic><topic>Principal component analysis</topic><topic>Reproducibility of Results</topic><topic>Scalability</topic><topic>Sensitivity and Specificity</topic><topic>Signal processing</topic><topic>Signal Processing, Computer-Assisted</topic><topic>Sorting</topic><topic>Streaming media</topic><topic>Telecommunications and information theory</topic><topic>Wavelet</topic><topic>Wavelet domain</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kewu Peng</creatorcontrib><creatorcontrib>Kieffer, J.C.</creatorcontrib><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>Kewu Peng</au><au>Kieffer, J.C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Embedded image compression based on wavelet pixel classification and sorting</atitle><jtitle>IEEE transactions on image processing</jtitle><stitle>TIP</stitle><addtitle>IEEE Trans Image Process</addtitle><date>2004-08-01</date><risdate>2004</risdate><volume>13</volume><issue>8</issue><spage>1011</spage><epage>1017</epage><pages>1011-1017</pages><issn>1057-7149</issn><eissn>1941-0042</eissn><coden>IIPRE4</coden><abstract>The method of modeling and ordering in wavelet domain is very important to design a successful algorithm of embedded image compression. In this paper, the modeling is limited to "pixel classification," the relationship between wavelet pixels in significance coding. Similarly, the ordering is limited to "pixel sorting," the coding order of wavelet pixels. We use pixel classification and sorting to provide a better understanding of previous works. The image pixels in wavelet domain are classified and sorted, either explicitly or implicitly, for embedded image compression. A new embedded image code is proposed based on a novel pixel classification and sorting (PCAS) scheme in wavelet domain. In PCAS, pixels to be coded are classified into several quantized contexts based on a large context template and sorted based on their estimated significance probabilities. The purpose of pixel classification is to exploit the intraband correlation in wavelet domain. Pixel sorting employs several fractional bit-plane coding passes to improve the rate-distortion performance. 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| ispartof | IEEE transactions on image processing, 2004-08, Vol.13 (8), p.1011-1017 |
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| subjects | Algorithm design and analysis Algorithms Applied sciences Artificial intelligence Classification Coding Computer science control theory systems Data Compression - methods Discrete wavelet transforms Exact sciences and technology Image coding Image compression Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image processing Information, signal and communications theory Partitioning algorithms Pattern Recognition, Automated Pattern recognition. Digital image processing. Computational geometry Pixel Pixels Principal component analysis Reproducibility of Results Scalability Sensitivity and Specificity Signal processing Signal Processing, Computer-Assisted Sorting Streaming media Telecommunications and information theory Wavelet Wavelet domain |
| title | Embedded image compression based on wavelet pixel classification and sorting |
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