Blind visual quality assessment for super-resolution images: database and model

Author:

Zheng Ruidi,Jiang Xiuhua,Zhang Jiaqi,Yu Hualong

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Reference54 articles.

1. Irani M, Peleg S (1991) Improving resolution by image registration. CVGIP: Graph Model Image Proc 53:231–239

2. Wang Z, Chen J, Hoi SCH (2021) Deep learning for image super-resolution: A survey. IEEE Trans Patt Anal Mach Intell 43(10):3365–3387

3. Antkowiak J, Jamal Baina T, Baroncini FV, Chateau N, FranceTelecom F, Pessoa ACF, Stephanie Colonnese F, Contin IL, Caviedes J, Philips F (2000) Final report from the video quality experts group on the validation of objective models of video quality assessment march 2000

4. Zhou W, Wang Z, Chen Z (2021) Image super-resolution quality assessment: Structural fidelity versus statistical naturalness. In: 2021 13th International conference on quality of multimedia experience (QoMEX), pp 61–64

5. Zhou F, Yao R, Liu B, Qiu G (2019) Visual quality assessment for super-resolved images: Database and method. IEEE Trans Image Process 1–1

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