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Image quality assessment: from error visibility to structural similarity

Zhou WangCenter for Neural Sci., New York Univ., NY, USAAlan C. BovikLaboratory for Image and Video Engineering (LIVE), Department of Electrical and Computer Engineering, University of Texas, Austin, Austin, TX, USAHamid R. SheikhLaboratory for Image and Video Engineering (LIVE), Department of Electrical and Computer Engineering, University of Texas, Austin, Austin, TX, USAEero P. SimoncelliHoward Hughes Medical Institute, Center for Neural Science and the Courant Institute for Mathematical Sciences, New York University, New York, NY, USA
2004en
ABI

Аннотация

Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a Structural Similarity Index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000.

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Цитирований: 7Использованных источников: 0