2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDIVMSP-21.2
Paper Title NESTED ERROR MAP GENERATION NETWORK FOR NO-REFERENCE IMAGE QUALITY ASSESSMENT
Authors Junming Chen, Peking University, China; Haiqiang Wang, Pengcheng lab, China; Ge Li, Peking University, China; Shan Liu, Tencent, China
SessionIVMSP-21: Image & Video Quality
LocationGather.Town
Session Time:Thursday, 10 June, 14:00 - 14:45
Presentation Time:Thursday, 10 June, 14:00 - 14:45
Presentation Poster
Topic Image, Video, and Multidimensional Signal Processing: [IVSMR] Image & Video Sensing, Modeling, and Representation
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract We propose a multi-task learning neural network for No-Reference image quality assessment (NR-IQA). The proposed architecture consists of a backbone feature extractor, a nested multi-task generative module and a quality regression module. We adopt a coarse-to-fine strategy to predict 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. Perceptual distortion maps are achieved by applying masking mechanism between reconstructed error maps and the learned distortion sensitivity map. At last, a quality regression module is adopted to nonlinearly map masked distortions to the subjective score. Experimental results demonstrate the superior performances of the proposed model over state-of-the-art models. The implementation of our method is released at https://github.com/R-JunmingChen/NEMG-IQA.