Self-Supervised Representation Learning for Video Quality Assessment-Reference-Cited by-同舟云学术

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Self-Supervised Representation Learning for Video Quality Assessment

Published:2023-03 Issue:1 Volume:69 Page:118-129
ISSN:0018-9316
Container-title:IEEE Transactions on Broadcasting
language:
Short-container-title:IEEE Trans. on Broadcast.

Author:

Jiang Shaojie¹,Sang Qingbing²^ORCID,Hu Zongyao¹,Liu Lixiong¹^ORCID

Affiliation:

1. Beijing Laboratory of Intelligent Information Technology, School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

2. School of Artificial Intelligence and Computer Science and the Jiangsu Key Laboratory of Media Design and Software Technology, Jiangnan University, Wuxi, China

Funder

National Natural Science Foundation of China

Open Project from Jiangsu Key Laboratory of Media Design and Software Technology, Jiangnan University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Media Technology

Link

http://xplorestaging.ieee.org/ielx7/11/10059242/09861385.pdf?arnumber=9861385

Reference69 articles.

1. Blind Prediction of Natural Video Quality

2. ViS3: an algorithm for video quality assessment via analysis of spatial and spatiotemporal slices

3. No-Reference Video Quality Assessment Based on Visual Memory Modeling

4. Study of Subjective and Objective Quality Assessment of Video

5. Deep Video Quality Assessor: From Spatio-Temporal Visual Sensitivity to a Convolutional Neural Aggregation Network

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1. Adversarial attacks on video quality assessment models;Knowledge-Based Systems;2024-06

2. A Database and Model for the Visual Quality Assessment of Super-Resolution Videos;IEEE Transactions on Broadcasting;2024-06

3. Deep Learning Approach for No-Reference Screen Content Video Quality Assessment;IEEE Transactions on Broadcasting;2024-06

4. Omnidirectional Video Quality Assessment With Causal Intervention;IEEE Transactions on Broadcasting;2024-03

5. Blind video quality assessment based on Spatio-Temporal Feature Resolver;Neurocomputing;2024-03

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