2 June 2000 Univariant assessment of the visual quality of images
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In order to evaluate the visual quality of images, most methods compare a degraded image to a perfect reference. We propose an original univariant (i.e. without reference) method based on the use of artificial neural networks. The principle is first to use a neural network to learn the quality of images taken from a pool of known examples, then use it to assess the quality of unknown images. The considered defects are compression artefacts, ringing or local singularities. To simplify, only images with defects that are not mixed with each other were first used. The method follows four steps. Observers are first required to mark degraded images to establish a pool of examples. Then, a characterization of the defect is extracted mathematically from the image. Then, the neural network learns how to establish a relation between the mathematical characterization of the defect and the visual mark. Finally, it can be used to assess the visual quality of an unknown image from the mathematical characterization of its defects. Two illustrative examples are presented: the assessment of the quality of JPEG compressed images and the detection of local defects.
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Mathieu Jung, Dominique Leger, "Univariant assessment of the visual quality of images", Proc. SPIE 3959, Human Vision and Electronic Imaging V, (2 June 2000); doi: 10.1117/12.387195; https://doi.org/10.1117/12.387195

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