5,416 research outputs found

    Color space selection for JPEG image compression

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    The Joint Photographic Experts Group\u27s image compression algorithm has been shown to be a very efficient and powerful method of compressing images. However, there is little substantive information about which color space should be utilized when implementing the JPEG algorithm. Currently, the JPEG algorithm is set up for use with any three component color space. The objective of this research was to determine whether or not the color space selected will significantly improve image compression capabilities. The RGB, XYZ, YIQ, CIELAB, CIELUV, and CIELAB LCh color spaces were examined and compared. Both numerical measures and psychophysical techniques were used to assess the results. The final results indicate that the device space, RGB, is the worst color space to compress images. In comparison, the nonlinear transforms of the device space, CIELAB and CIELUV, are the best color spaces to compress images. The XYZ, YIQ, and CIELAB LCh color spaces resulted in intermediate levels of compression

    CoMoFoD #x2014; New database for copy-move forgery detection

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    Due to the availability of many sophisticated image processing tools, a digital image forgery is nowadays very often used. One of the common forgery method is a copy-move forgery, where part of an image is copied to another location in the same image with the aim of hiding or adding some image content. Numerous algorithms have been proposed for a copy-move forgery detection (CMFD), but there exist only few benchmarking databases for algorithms evaluation. We developed new database for a CMFD that consist of 260 forged image sets. Every image set includes forged image, two masks and original image. Images are grouped in 5 categories according to applied manipulation: translation, rotation, scaling, combination and distortion. Also, postprocessing methods, such as JPEG compression, blurring, noise adding, color reduction etc., are applied at all forged and original images. In this paper we present database organization and content, creation of forged images, postprocessing methods, and database testing. CoMoFoD database is available at http://www.vcl.fer.hr/comofodMinistry of Science, Education and Sport, China; project numbers: 036-0361630-1635 and 036-0361630-164
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