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Image quality and visual attention interactions: Towards a more reliable analysis in the saliency space.

, and . QoMEX, page 201-206. IEEE, (2011)

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The aesthetic appeal of depth of field in photographs., , , and . QoMEX, page 81-86. IEEE, (2014)Journey through the crowd: Best practices and recommendations for crowdsourced QoE, and . 2015 Seventh International Workshop on Quality of Multimedia Experience (QoMEX), page 1--2. IEEE, (2015)When the crowd challenges the lab: Lessons learnt from subjective studies on image aesthetic appeal, , , , and . Proceedings of the Fourth International Workshop on Crowdsourcing for Multimedia, page 33--38. (2015)Crowdsourcing Quality of Experience Experiments, , , , , , , and . Evaluation in the Crowd. Crowdsourcing and Human-Centered Experiments, Springer, (September 2017)iCaCoT - Interactive Camera-based Coaching and Training., , and . WSICC@TVX, volume 1516 of CEUR Workshop Proceedings, CEUR-WS.org, (2015)Maximal-discrepancy bounds for regularized classifiers., , , and . IJCNN, page 652-656. IEEE Computer Society, (2009)How to apply spatial saliency into objective metrics for JPEG compressed images?, , , , and . ICIP, page 961-964. IEEE, (2009)No-reference image quality assessment based on localized gradient statistics: application to JPEG and JPEG2000., , , , and . Human Vision and Electronic Imaging, volume 7527 of SPIE Proceedings, page 75271. SPIE, (2010)Crowdsourcing for Rating Image Aesthetic Appeal: Better a Paid or a Volunteer Crowd?, and . CrowdMM, page 25-30. ACM, (2014)Image quality and visual attention interactions: Towards a more reliable analysis in the saliency space., and . QoMEX, page 201-206. IEEE, (2011)