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How saccadic models help predict where we look during a visual task? Application to visual quality assessment

Olivier Le Meur 1 Antoine Coutrot 2
1 Sirocco - Analysis representation, compression and communication of visual data
Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : In this paper, we present saccadic models which are an alternative way to predict where observers look at. Compared to saliency models, saccadic models generate plausible visual scan-paths from which saliency maps can be computed. In addition these models have the advantage of being adaptable to different viewing conditions, viewing tasks and types of visual scene. We demonstrate that saccadic models perform better than existing saliency models for predicting where an observer looks at in free-viewing condition and quality-task condition (i.e. when observers have to score the quality of an image). For that, the joint distributions of saccade amplitudes and orientations in both conditions (i.e. free-viewing and quality task) have been estimated from eye tracking data. Thanks to saccadic models, we hope we will be able to improve upon the performance of saliency-based quality metrics, and more generally the capacity to predict where we look within visual scenes when performing visual tasks.
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https://hal.inria.fr/hal-01391750
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Submitted on : Thursday, November 3, 2016 - 5:16:33 PM
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Olivier Le Meur, Antoine Coutrot. How saccadic models help predict where we look during a visual task? Application to visual quality assessment. SPIE Image Quality And System Performance, Feb 2016, San Fransisco, United States. ⟨hal-01391750⟩

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