JPEG Compression History Estimation for Color Images

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dc.citation.bibtexNamearticleen_US
dc.citation.journalTitleIEEE Transactions on Image Processingen_US
dc.contributor.authorNeelamani, Rameshen_US
dc.contributor.authorde Queiroz, Ricardoen_US
dc.contributor.authorFan, Zhigangen_US
dc.contributor.authorDash, Sanjeeben_US
dc.contributor.authorBaraniuk, Richard G.en_US
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)en_US
dc.date.accessioned2007-10-31T00:55:47Zen_US
dc.date.available2007-10-31T00:55:47Zen_US
dc.date.issued2006en_US
dc.date.modified2006-07-19en_US
dc.date.submitted2005-07-28en_US
dc.descriptionJournal Paperen_US
dc.description.abstractWe routinely encounter digital color images that were previously JPEG-compressed. En route to the image's current representation, the previous JPEG compression's various settings—termed its JPEG compression history (CH)—are often discarded after the JPEG decompression step. Given a JPEG-decompressed color image, this paper aims to estimate its lost JPEG CH. We observe that the previous JPEG compression's quantization step introduces a lattice structure in the discrete cosine transform (DCT) domain. This paper proposes two approaches that exploit this structure to solve the JPEG Compression History Estimation (CHEst) problem. First, we design a statistical dictionary-based CHEst algorithm that tests the various CHs in a dictionary and selects the maximum a posteriori estimate. Second, for cases where the DCT coefficients closely conform to a 3-D parallelepiped lattice, we design a blind lattice-based CHEst algorithm. The blind algorithm exploits the fact that the JPEG CH is encoded in the nearly orthogonal bases for the 3-D lattice and employs novel lattice algorithms and recent results on nearly orthogonal lattice bases to estimate the CH. Both algorithms provide robust JPEG CHEst performance in practice. Simulations demonstrate that JPEG CHEst can be extremely useful in JPEG recompression; the estimated CH allows us to recompress a JPEG-decompressed image with minimal distortion (large signal-to-noise-ratio) and simultaneously achieve a small file-size.en_US
dc.description.sponsorshipTexas Instrumentsen_US
dc.description.sponsorshipDefense Advanced Research Projects Agencyen_US
dc.description.sponsorshipOffice of Naval Researchen_US
dc.description.sponsorshipNational Science Foundationen_US
dc.description.sponsorshipAir Force Office of Scientific Researchen_US
dc.identifier.citationR. Neelamani, R. de Queiroz, Z. Fan, S. Dash and R. G. Baraniuk, "JPEG Compression History Estimation for Color Images," <i>IEEE Transactions on Image Processing,</i> 2006.en_US
dc.identifier.doihttp://dx.doi.org/10.1109/TIP.2005.864171en_US
dc.identifier.urihttps://hdl.handle.net/1911/20147en_US
dc.language.isoengen_US
dc.relation.projecthttp://www-dsp.rice.edu/software/color.shtmlen_US
dc.relation.softwarehttp://www-dsp.rice.edu/software/color.shtmlen_US
dc.subjectJPEGen_US
dc.subjectcompressionen_US
dc.subjectcoloren_US
dc.subjecthistoryen_US
dc.subjectrecompressionen_US
dc.subjectlatticeen_US
dc.subjectquantizationen_US
dc.subject.keywordJPEGen_US
dc.subject.keywordcompressionen_US
dc.subject.keywordcoloren_US
dc.subject.keywordhistoryen_US
dc.subject.keywordrecompressionen_US
dc.subject.keywordlatticeen_US
dc.subject.keywordquantizationen_US
dc.subject.otherImage Processing and Pattern analysisen_US
dc.titleJPEG Compression History Estimation for Color Imagesen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
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