@misc{Ayunts_Hrach_Y._A,
 author={Ayunts, Hrach Y. and Agaian, Sos S.},
 howpublished={online},
 publisher={Изд-во НАН РА},
 language={en},
 abstract={Image decolorization, the process of color-to-gray conversion, plays a crucial role insingle-channel processing, computer vision, digital printing, and monochrome visualization. This process induces new artifacts, the impact of which on visual quality hasto be identified. While visual quality assessment has been the subject of many stud-ies, there are still some open questions regarding new color-to-gray conversion qualitymetrics. For example, computer simulations show that the commonly used grayscaleconversion quality metrics such as CCPR, CCFR, and E-score depend on parametersand may pick different best decolorization methods by changing the parameters.This paper proposes a new quality metric to evaluate image decolorization methods.It uses the human visual properties information and regression method. Experimentalresults also show (i) strong correlations between the presented image decolorizationquality metric and the Mean Opinion Score (MOS), (ii) more robust than the existingquality metrics, and (iii) help to choose the best state-of-the-art decolorization methodsusing the presented metric and existing quality metrics.},
 title={A New Image Decolorization Evaluation Quality Metric},
 type={Հոդված},
 keywords={Mathematical cybernetics, Computer science},
}