Lossless compression of color mosaic images
Lossless compression of color mosaic images poses a unique and interesting problem of spectral decorrelation of spatially interleaved R, G, B samples. We investigate reversible lossless spectral-spatial transforms that can remove statistical redundancies in both spectral and spatial domains and disc...
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| Veröffentlicht in: | IEEE transactions on image processing 2006-06, Vol.15 (6), p.1379-1388 |
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| container_title | IEEE transactions on image processing |
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| creator | Zhang, Ning Wu, Xiaolin |
| description | Lossless compression of color mosaic images poses a unique and interesting problem of spectral decorrelation of spatially interleaved R, G, B samples. We investigate reversible lossless spectral-spatial transforms that can remove statistical redundancies in both spectral and spatial domains and discover that a particular wavelet decomposition scheme, called Mallat wavelet packet transform, is ideally suited to the task of decorrelating color mosaic data. We also propose a low-complexity adaptive context-based Golomb-Rice coding technique to compress the coefficients of Mallat wavelet packet transform. The lossless compression performance of the proposed method on color mosaic images is apparently the best so far among the existing lossless image codecs. |
| doi_str_mv | 10.1109/TIP.2005.871116 |
| format | Article |
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We investigate reversible lossless spectral-spatial transforms that can remove statistical redundancies in both spectral and spatial domains and discover that a particular wavelet decomposition scheme, called Mallat wavelet packet transform, is ideally suited to the task of decorrelating color mosaic data. We also propose a low-complexity adaptive context-based Golomb-Rice coding technique to compress the coefficients of Mallat wavelet packet transform. 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We investigate reversible lossless spectral-spatial transforms that can remove statistical redundancies in both spectral and spatial domains and discover that a particular wavelet decomposition scheme, called Mallat wavelet packet transform, is ideally suited to the task of decorrelating color mosaic data. We also propose a low-complexity adaptive context-based Golomb-Rice coding technique to compress the coefficients of Mallat wavelet packet transform. 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Spectral analysis</subject><subject>Signal, noise</subject><subject>Spectra</subject><subject>Telecommunications and information theory</subject><subject>Transforms</subject><subject>Wavelet</subject><subject>Wavelet domain</subject><subject>Wavelet packets</subject><subject>Wavelet transforms</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>eNqF0U1LwzAYB_AgipvTswdBhqAepFuevDZHGb4MBnqY55CmqXS0zUy2g9_ejBUUD3rJG788Ic8foXPAEwCspsv564RgzCe5BABxgIagGGQYM3KY1pjLTAJTA3QS4wpjYBzEMRqAkIIRwYbobuFjbFyMY-vbdUiL2ndjX6Vt48O49dHUdly35t3FU3RUmSa6s34eobfHh-XsOVu8PM1n94vMMso3mTSiNJyoorBpVKUhppScqkKAFelRKSricldARakrGJNGKaAGM1OmI8HoCN3u666D_9i6uNFtHa1rGtM5v406V4JgrnKc5M2fUuTJcf4_JDkGhRVJ8OoXXPlt6NJ3dS4k5VLlOzTdIxtS94Kr9DqkFoVPDVjvctEpF73LRe9zSTcu-7LbonXlt--DSOC6ByZa01TBdLaO305KIRhAchd7VzvnfpShBBSlXysPm0A</recordid><startdate>20060601</startdate><enddate>20060601</enddate><creator>Zhang, Ning</creator><creator>Wu, Xiaolin</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Spectral analysis</topic><topic>Signal, noise</topic><topic>Spectra</topic><topic>Telecommunications and information theory</topic><topic>Transforms</topic><topic>Wavelet</topic><topic>Wavelet domain</topic><topic>Wavelet packets</topic><topic>Wavelet transforms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Ning</creatorcontrib><creatorcontrib>Wu, Xiaolin</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 Online</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>MEDLINE - Academic</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on image processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zhang, Ning</au><au>Wu, Xiaolin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Lossless compression of color mosaic images</atitle><jtitle>IEEE transactions on image processing</jtitle><stitle>TIP</stitle><addtitle>IEEE Trans Image Process</addtitle><date>2006-06-01</date><risdate>2006</risdate><volume>15</volume><issue>6</issue><spage>1379</spage><epage>1388</epage><pages>1379-1388</pages><issn>1057-7149</issn><eissn>1941-0042</eissn><coden>IIPRE4</coden><abstract>Lossless compression of color mosaic images poses a unique and interesting problem of spectral decorrelation of spatially interleaved R, G, B samples. We investigate reversible lossless spectral-spatial transforms that can remove statistical redundancies in both spectral and spatial domains and discover that a particular wavelet decomposition scheme, called Mallat wavelet packet transform, is ideally suited to the task of decorrelating color mosaic data. We also propose a low-complexity adaptive context-based Golomb-Rice coding technique to compress the coefficients of Mallat wavelet packet transform. The lossless compression performance of the proposed method on color mosaic images is apparently the best so far among the existing lossless image codecs.</abstract><cop>New York, NY</cop><pub>IEEE</pub><pmid>16764264</pmid><doi>10.1109/TIP.2005.871116</doi><tpages>10</tpages></addata></record> |
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| ispartof | IEEE transactions on image processing, 2006-06, Vol.15 (6), p.1379-1388 |
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| source | IEEE Electronic Library Online |
| subjects | Algorithms Applied sciences Bandwidth Codecs Coding, codes Color Colorimetry - methods Compressing Computer Communication Networks Computer Graphics Computer Simulation Context quantization Data Compression - methods Data Interpretation, Statistical Decorrelation digital camera Digital cameras entropy coding Exact sciences and technology Image coding image compression Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image processing Image quality Information, signal and communications theory Lossless Models, Statistical Mosaics Sampling, quantization Signal and communications theory Signal processing Signal Processing, Computer-Assisted Signal representation. Spectral analysis Signal, noise Spectra Telecommunications and information theory Transforms Wavelet Wavelet domain Wavelet packets Wavelet transforms |
| title | Lossless compression of color mosaic images |
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