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RESEARCH PRODUCT
Improving Lossless Image Compression with Contextual Memory
Remus BradAlexandru Dorobanțiusubject
Computer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONgeometric weightingData_CODINGANDINFORMATIONTHEORY02 engineering and technologylcsh:TechnologylosslessGrayscale030218 nuclear medicine & medical imagingImage (mathematics)lcsh:Chemistry03 medical and health sciences0302 clinical medicineProbabilistic methodSoftware0202 electrical engineering electronic engineering information engineeringprobabilistic methodGeneral Materials Sciencelcsh:QH301-705.5InstrumentationFluid Flow and Transfer ProcessesLossless compressioncontextual informationlcsh:Tbusiness.industryProcess Chemistry and TechnologyGeneral EngineeringEnsemble learninglcsh:QC1-999image compressionComputer Science ApplicationsTerm (time)lcsh:Biology (General)lcsh:QD1-999Computer engineeringlcsh:TA1-2040ensemble learning020201 artificial intelligence & image processinglcsh:Engineering (General). Civil engineering (General)businesslcsh:PhysicsImage compressiondescription
With the increased use of image acquisition devices, including cameras and medical imaging instruments, the amount of information ready for long term storage is also growing. In this paper we give a detailed description of the state-of-the-art lossless compression software PAQ8PX applied to grayscale image compression. We propose a new online learning algorithm for predicting the probability of bits from a stream. We then proceed to integrate the algorithm into PAQ8PX&rsquo
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-06-01 | Applied Sciences |