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Learning Print Artifact Detectors

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An important aspect of image and print quality is the existence of artifacts, such as compression or print artifacts. A general perceptual masking model, that describes the perceptual severity of artifacts on general background, could have been used to extract specific artifact detectors. However, currently general models are not mature enough to provide print artifact detectors for commercial print quality control application. Consequently we propose to employ machine learning techniques to learn a specific model for each print artifact based on a relevant set of features. We used the approach to develop two print artifact detectors. While the proposed approach was developed for print quality purpose, the method is general and can be used for learning automatic evaluators for image defects and quality degradation as well.
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Document Type: Research Article

Publication date: January 1, 2012

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  • Started in 2002 and merged with the Color and Imaging Conference (CIC) in 2014, CGIV covered a wide range of topics related to colour and visual information, including color science, computational color, color in computer graphics, color reproduction, volor vision/psychophysics, color image quality, color image processing, and multispectral color science. Drawing papers from researchers, scientists, and engineers worldwide, DGIV offered attendees a unique experience to share with colleagues in industry and academic, and on national and international standards committees. Held every year in Europe, DGIV papers were more academic in their focus and had high student participation rates.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual papers for details.

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