Turkan, Mehmet2023-06-162023-06-1620211300-70092147-5881https://doi.org/10.5505/pajes.2020.69158https://search.trdizin.gov.tr/yayin/detay/488103https://hdl.handle.net/20.500.14365/2709An image quality metric is proposed by introducing a new framework for full reference image quality assessment from the perspective of image patch manifolds. Assuming that most natural scenes are sampled from low dimensional manifolds or submanifolds, perceived image degradations in structural variations can be quantitatively evaluated on the surfaces of highly nonlinear image manifolds. Manifold distortion image quality index first characterizes intrinsic geometric properties of the locally linear manifold structures of spatially local patch spaces, and then measures the deviation from the original smooth manifold structure to calculate the distortion index. Experimental results demonstrate a strong promise with a comparison to both subjective evaluation and state-of-the-art objective quality assessment methods.eninfo:eu-repo/semantics/openAccessImage quality assessmentImage quality indexManifold learningNeighbor embeddingFidelity-CriterionSuperresolutionDegradationInformationArticle10.5505/pajes.2020.69158