Search results for "Image compression"

showing 10 items of 53 documents

HINTS: A novel approach for realistic simulations of vehicular communications

2012

One of the main challenges in the Vehicular Ad-Hoc Networks (VANETs) research domain is the simulation of vehicular communications using realistic mobility models. Several efforts have been put lately in this purpose; yet, the proposed models are either inappropriate, or carry significant disadvantages. In this paper, we propose a novel approach for realistic simulations in vehicular networks, inspired from the hierarchical video and image compression technique. We developed HINTS (Hybrid Integration of Network and Traffic Simulators), the correspondent platform, engendered from the integration of SUMO, a traffic simulator and NS-3, a network simulator after adding some supplemental modules…

Vehicular communication systemsImage codingMobility modelVehicular ad hoc networkComputer scienceDistributed computingComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKSSimulationTraffic simulatorNetwork simulationImage compressionDomain (software engineering)2012 Global Information Infrastructure and Networking Symposium (GIIS)
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Compression of binary images based on covering

1995

The paper describes a new technique to compress binary images based on an image covering algorithm. The idea is that binary images can be always covered by rectangles, univocally described by a vertex and two adjacent edges (L-shape). Some optimisations are necessary to consider degenerate configurations. The method has been tested on several images representing drawings and typed texts. The comparison with existing image file compression techniques shows a good performance of our approach. Further optimisations are under development.

Vertex (computer graphics)Medial axisComputer scienceCompression (functional analysis)Binary imageComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImage file formatscomputer.file_formatcomputerAlgorithmData compressionImage compressionImage (mathematics)
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Maximum likelihood difference scaling of image quality in compression-degraded images.

2007

International audience; Lossy image compression techniques allow arbitrarily high compression rates but at the price of poor image quality. We applied maximum likelihood difference scaling to evaluate image quality of nine images, each compressed via vector quantization to ten different levels, within two different color spaces, RGB and CIE 1976 L(*)a(*)b(*). In L(*)a(*)b(*) space, images could be compressed on average by 32% more than in RGB space, with little additional loss in quality. Further compression led to marked perceptual changes. Our approach permits a rapid, direct measurement of the consequences of image compression for human observers.

[ INFO ] Computer Science [cs]Image qualityColorImage processing[INFO] Computer Science [cs]Color space050105 experimental psychology03 medical and health sciences0302 clinical medicineOpticsImage Processing Computer-Assisted[INFO]Computer Science [cs]0501 psychology and cognitive sciences[SDV.MHEP.OS]Life Sciences [q-bio]/Human health and pathology/Sensory OrgansImage resolutionMathematicsColor imagebusiness.industry05 social sciencesVector quantizationData CompressionAtomic and Molecular Physics and OpticsElectronic Optical and Magnetic Materials[SDV.MHEP.OS] Life Sciences [q-bio]/Human health and pathology/Sensory Organs[ SDV.MHEP.OS ] Life Sciences [q-bio]/Human health and pathology/Sensory OrgansRGB color modelComputer Vision and Pattern RecognitionArtifactsbusiness030217 neurology & neurosurgeryImage compression
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An evaluation framework and a benchmark for multi/hyperspectral image compression

2011

International audience; This paper benchmarks three multi/hyperspectral image compression approaches: the classic Multi-2D compression approach and two different implementations of 3D approach (Full 3D and Hybrid). All approaches are combined with a spectral PCA decorrelation stage to optimize performance. These three compression approaches are compared within a larger comparison framework than the conventionally used PSNR, which includes eight metrics divided into three families. The comparison is carried out with regard to variations in bitrates, spatial, and spectral dimensions variations of images. The time and memory consumption difference between the three approaches is also discussed…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingComputer sciencebusiness.industryMultispectral image0211 other engineering and technologiesPattern recognition02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingcompressionwaveletsWavelet[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingCompression (functional analysis)Hyperspectral image compression0202 electrical engineering electronic engineering information engineeringBenchmark (computing)020201 artificial intelligence & image processingArtificial intelligencebusinessDecorrelation[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingMulti/hyperspectral images021101 geological & geomatics engineeringImage compression
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A new minimum trees-based approach for shape matching with improved time computing : application to graphical symbols recognition

2010

Recently we have developed a model for shape description and matching. Based on minimum spanning trees construction and specifics stages like the mixture, it seems to have many desirable properties. Recognition invariance in front shift, rotated and noisy shape was checked through median scale tests related to GREC symbol reference database. Even if extracting the topology of a shape by mapping the shortest path connecting all the pixels seems to be powerful, the construction of graph induces an expensive algorithmic cost. In this article we discuss on the ways to reduce time computing. An alternative solution based on image compression concepts is provided and evaluated. The model no longe…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingMatching (graph theory)[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputer scienceFeature extractionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingMinimum spanning tree[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingActive shape model0202 electrical engineering electronic engineering information engineeringDiscrete cosine transformComputingMilieux_MISCELLANEOUS[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingSpanning treebusiness.industry020206 networking & telecommunicationsPattern recognitionGraphShortest path problemGraph (abstract data type)020201 artificial intelligence & image processingArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingImage compression
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Kolmogorov Superposition Theorem and Wavelet Decomposition for Image Compression

2009

International audience; Kolmogorov Superposition Theorem stands that any multivariate function can be decomposed into two types of monovariate functions that are called inner and external functions: each inner function is associated to one dimension and linearly combined to construct a hash-function that associates every point of a multidimensional space to a value of the real interval $[0,1]$. These intermediate values are then associated by external functions to the corresponding value of the multidimensional function. Thanks to the decomposition into monovariate functions, our goal is to apply this decomposition to images and obtain image compression. We propose a new algorithm to decomp…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing010102 general mathematicsMathematical analysisWavelet transform02 engineering and technologyFunction (mathematics)[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingSuperposition theorem01 natural sciencesWavelet packet decompositionWavelet[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV]Dimension (vector space)[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV][ INFO.INFO-TI ] Computer Science [cs]/Image Processing0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingPoint (geometry)0101 mathematics[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingImage compressionMathematics
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A 3D multispectral integrated acquisition system : acquisition, data coding and compression

2010

We have developed an integrated system permitting the simultaneous acquisition of the 3D shape and the spectral spectral reflectance of scanned object surfaces. We call this system a 3D multispectral scanner because it combines within a stereopair, a multispectral video camera and a structured light projector. We see several application possibilities for a such acquisition system but we want to highlight applications in the field of digital archiving and broadcasting for heritage objects. In the manuscript we first introduce the acquisition system and its necessary calibrations and treatments needed for his use. Then, once the acquisition system is functional, data that are generated are ri…

[SPI.OTHER]Engineering Sciences [physics]/OtherAdaptative multiresolution analysis[ SPI.OTHER ] Engineering Sciences [physics]/Other[SPI.OTHER] Engineering Sciences [physics]/OtherMulti/hyperspectral image compressionOndelettes 3D anisotropes3D multispectral scannerScanner 3D multispectralSpiht 3DComparison frameworkCompression d'images multi/hyperspectralesAnisotropic 3D waveletsCadre d'évaluationAnalyse multirésolution adaptativeSpiht
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Learning-based multiresolution transforms with application to image compression

2013

In Harten's framework, multiresolution transforms are defined by predicting finer resolution levels of information from coarser ones using an operator, called prediction operator, and defining details (or wavelet coefficients) that are the difference between the exact and predicted values. In this paper we use tools of statistical learning in order to design a more accurate prediction operator in this framework based on a training sample, resulting in multiresolution decompositions with enhanced sparsity. In the case of images, we incorporate edge detection techniques in the design of the prediction operator in order to avoid Gibbs phenomenon. Numerical tests are presented showing that the …

business.industry020206 networking & telecommunicationsPattern recognition02 engineering and technologySample (graphics)Edge detectionGibbs phenomenonsymbols.namesakeWaveletOperator (computer programming)Control and Systems EngineeringCompression (functional analysis)Statistical learning theorySignal Processing0202 electrical engineering electronic engineering information engineeringsymbols020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligenceElectrical and Electronic EngineeringbusinessSoftwareImage compressionMathematicsSignal Processing
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Use of H.264 real-time video encoding to reduce display wall system bandwidth consumption

2015

This paper compares the DXT and JPEG image compression techniques used in display wall solutions SAGE and DisplayCluster with hardware accelerated H.264 video encoding that is used in the display wall system developed by the authors of this paper. The obtained processing power usage and generated bandwidth measurements presented in this paper demonstrate that hardware accelerated H.264 encoding offers multiple benefits over software implemented H.264, DXT and JPEG.

business.industryComputer scienceBandwidth (signal processing)ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONData_CODINGANDINFORMATIONTHEORYcomputer.file_formatJPEGPower usageReal time videoSoftwareJpeg image compressionbusinesscomputerComputer hardwareTransform codingData compression2015 IEEE 3rd Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE)
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The effects of image compression on quantitative measurements of digital panoramic radiographs

2012

WOS: 000314401800026

fractal dimensionPanoramic radiographOdontologíaangular measurementsMandibular first molarFractal dimensionstomatognathic systemClinical and Experimental DentistryRadiography PanoramicHumansGonial angleDigital panoramic radiographyeducationGeneral DentistryMathematicsObserver VariationOrthodonticseducation.field_of_studyRadiography Dental DigitalRepeatabilitycomputer.file_formatData Compression:CIENCIAS MÉDICAS [UNESCO]Ciencias de la saludJPEGimage compressionTagged Image File FormatOtorhinolaryngologyUNESCO::CIENCIAS MÉDICASlinear measurementsResearch-ArticleSurgerycomputerData compressionMedicina Oral Patología Oral y Cirugia Bucal
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