Andrade Rodas, Juan Manuel2024-03-062024-03-062023979-8-3503-1806-70000-0000https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182024784&doi=10.1109%2fIISA59645.2023.10345886&partnerID=40&md5=bacce65e17af6c3f5fd72989bdc5a11cThe fact that human visual perception is highly spe-cialized for extracting structural information has been efficiently exploited in full-reference image quality assessment algorithms; additionally, it has been shown that the human perception of sharpness depends on the local image contrast. Since the image gradients can measure effectively the image structure, we propose a training-free no-reference objective image sharpness assessment method based on the statistical analysis of perceptually-weighted normalized -gradients of relevant pixels in the input image. Results over six subject-rated publicly available databases show that the proposed no-reference sharpness assessment algorithm correlates well with perceived sharpness and provides competitive performance when compared with state-of-the-art algorithms despite its low computational burden.es-ESImage blurImage sharpnessNo-reference image quality assessmentObjective blur assessmentPerceptual-basedARTÍCULO DE CONFERENCIA10.1109/IISA59645.2023.10345886