Technical Program

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TQ.L3: Machine Learning-based approaches to prediction of perceived video quality

Session Type: Lecture
Time: Tuesday, October 9, 16:20 - 18:20
Location: Kokkali
Session Chair: Sebastian Bosse, HHI Fraunhofer
 
 TQ.L3.1: WHERE TO PLACE: A REAL-TIME VISUAL SALIENCY BASED LABEL PLACEMENT FOR AUGMENTED REALITY APPLICATIONS
         Neel Rakholia; Stanford University
         Srinidhi Hegde; TCS Innovation Labs
         Ramya Hebbalaguppe; TCS Innovation Labs
 
 TQ.L3.2: BLIND IMAGE QUALITY ASSESSMENT WITH A PROBABILISTIC QUALITY REPRESENTATION
         Hui Zeng; The Hong Kong Polytechnic University
         Lei Zhang; The Hong Kong Polytechnic University
         Alan C. Bovik; The University of Texas at Austin
 
 TQ.L3.3: ENHANCING TEMPORAL QUALITY MEASUREMENTS IN A GLOBALLY DEPLOYED STREAMING VIDEO QUALITY PREDICTOR
         Christos Bampis; The University of Texas at Austin
         Zhi Li; Netflix Inc.
         Alan C. Bovik; The University of Texas at Austin
 
 TQ.L3.4: DEEP BLIND VIDEO QUALITY ASSESSMENT BASED ON TEMPORAL HUMAN PERCEPTION
         Sewoong Ahn; Yonsei University
         Sanghoon Lee; Yonsei University
 
 TQ.L3.5: TOWARDS A SEMANTIC PERCEPTUAL IMAGE METRIC
         Troy Chinen; Google Inc.
         Johannes Ballé; Google Inc.
         Chunhui Gu; Google Inc.
         Sung Jin Hwang; Google Inc.
         Sergey Ioffe; Google Inc.
         Nick Johnston; Google Inc.
         Thomas Leung; Google Inc.
         David Minnen; Google Inc.
         Sean O'Malley; Google Inc.
         Charles Rosenberg; Pinterest
         George Toderici; Google Inc.
 
 TQ.L3.6: NEURAL NETWORK-BASED ESTIMATION OF DISTORTION SENSITIVITY FOR IMAGE QUALITY PREDICTION
         Sebastian Bosse; HHI Fraunhofer
         Sören Becker; HHI Fraunhofer
         Zacharias Virgil Fisches; HHI Fraunhofer
         Wojciech Samek; HHI Fraunhofer
         Thomas Wiegand; HHI Fraunhofer