Perceptually-Weighted Cnn For 360-Degree Image Quality Assessment Using Visual Scan-Path And Jnd - Archive ouverte HAL
Communication Dans Un Congrès Année : 2021

Perceptually-Weighted Cnn For 360-Degree Image Quality Assessment Using Visual Scan-Path And Jnd

Résumé

Image quality assessment of immersive content and more specifically 360-degree one is still in its infancy. There are many challenges regarding sphere vs. projected representation, human visual system (HVS) properties in a 360-degree environment, etc. In this paper, we propose the use of CNNs to design a no reference model to predict visual quality of 360-degree images. Instead of feeding the CNN with ERPs, visually important viewports are extracted based on visual scan-path prediction and given to a multi-channel CNN using DenseNet-121. Moreover, information about visual fixations and just noticeable difference are used to account for the HVS properties and make the network closer to human judgment. The scan-path is also used to create multiple instances of the database so as to perform a robust generalization analysis and compensate for the lack of databases.
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hal-03791581 , version 1 (17-10-2024)

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Abderrezzaq Sendjasni, Mohamed-Chaker Larabi, Faouzi Alaya Cheikh. Perceptually-Weighted Cnn For 360-Degree Image Quality Assessment Using Visual Scan-Path And Jnd. 2021 IEEE International Conference on Image Processing (ICIP 2021), IEEE ICIP Organizing Committee; IEEE Signal Processing Society, Sep 2021, Anchorage (virtual conference), United States. pp.1439-1443, ⟨10.1109/ICIP42928.2021.9506044⟩. ⟨hal-03791581⟩
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