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Detection of visual saliency is of great interest for a lot of computer vision applications in particular for content based image retrieval. The work presented in this paper is devoted to develop an algorithm of saliency detection that performs adequately in predicting human fixations for stimuli containing blur and sharp regions. This work is based on an experimental study on the effect of blurriness on visual attention when observers see images with no prior knowledge in free viewing conditions. A ground-truth has been derived from this experimental study to test the saliency model we developed.

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International audienceIn recent years, image enhancement methods have been developed to assist visually impaired people in the everyday life. These methods are promising but they currently suffer from the problem of their correct adjustment according to the specificities of each patient. To address such a problem, an objective quality metric could be used to quantify if enhancement schemes do not introduce artifacts that could be perceived as troublesome by visually deficient persons. As all existing metrics were designed to assess the image quality for observers with normal or corrected to normal vision, they are not appropriate in the context of low vision. Then an alternate framework is presented in this paper. This framework combines three distinct quality attributes that were identified as important features for the visually impaired in image quality assessment and it has been developed to adapt to the different types of visual pathologies