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Vortragende Person/Vortragende Personen:
Dr. Haiqiang Wang, Tencent Inc., Peng Cheng Laboratory, Shenzhen, China

Diese Veranstaltung ist Teil der Veranstaltungsreihe „SFB-TRR 161 Lecture Series“.

Abstract:

Perceptual quality assessment has been a long-standing problem that attracts attentions from both academia and industry. The just-noticeable-difference (JND) methodology has been proposed to measure subjective experience of the human visual system in recent years. In this talk, I will try to give a brief review on JND-based subjective quality measurement, objective quality assessment metrics, and applications to perceptual coding. Then, I will present our recent work that applies Convolutional Neural Networks (CNN) to develop objective quality metrics. To be specific, we have developed a Full-Reference metric that uses 2D convolutional layers to extract spatial features and Convolutional Neural Network with 3D kernels (C3D) to learn spatiotemporal features. C3DVQA combines feature learning and score pooling into one spatiotemporal feature learning process. What's more, I would share an on-going work that aims to develop a No-Reference IQA metric. It adopts a coarse-to-fine multi-task learning strategy to reconstruct objective error maps in two subtasks optimized with different loss functions. The network is designed to be nested such that discriminative features learned from subtasks are efficiently shared by the primary task. Experimental results are given to show the performance of the proposed methods.

Biography:

Haiqiang Wang received his Ph.D. degree in 2018 from the University of Southern California. From 2018 to 2019, he was a Sr. researcher at Tencent Inc., Shenzhen, China. He is currently a research assistant professor at Peng Cheng Laboratory, Shenzhen, China. His research interests include deep learning, image/video processing, as well as quality assessment. Dr. Wang is a recipient of the 2017 CAPOCELLI PRIZE from the Data Compression Conference (DCC).