Light Field Saliency Estimation

@inproceedings{gill2021focus,

title = {Focus Guided Light Field Saliency Estimation},

author = {Ailbhe Gill and Emin Zerman and Martin Alain and Le Pendu, Mikael and Aljosa Smolic},

url = {https://v-sense.scss.tcd.ie:443/wp-content/uploads/2021/06/gill2021focus_preprint.pdf},

year  = {2021},

date = {2021-06-17},

booktitle = {Thirteenth International Conference on Quality of Multimedia Experience (QoMEX)},

organization = {IEEE},

abstract = {Light field imaging enables us to capture all light rays in a visual scene. As light fields are four-dimensional, their captures come with an increased amount of information to take advantage of. This has stimulated ongoing light field specific research into virtual viewpoints and shallow depth of field rendering, commonly called refocusing. However, the computation time and memory required to perform these operations can make tasks such as real-time rendering impractical. One solution is to exploit the salient information of light fields to focus resources on regions that attract visual attention when using these algorithms. Although saliency estimation methods for light fields have been previously explored, these focus mainly on salient object segmentation with the goal of generating one saliency map per light field.



Aiming to create a basis for a 4D saliency prediction model analogous to light fields, this paper proposes a saliency estimation method specific to light fields that considers the refocusing operation. The proposed method modifies an existing view rendering algorithm with focus guidance, obtained from the light field disparity. This facilitates the construction of saliency maps without the need to render the corresponding view itself, which will help to speed up processing operations that are compatible. The results show that the proposed saliency estimation approach yields very good predictions of visual attention across multiple planes of the light field. We anticipate that this approach can be extended for a range of rendering applications.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}