Blind Video Quality Assessment Using Fusion of Novel Structural Features and Deep Features
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-11349-9_19
Reference20 articles.
1. Dendi, S.V.R., Channappayya, S.S.: No-reference video quality assessment using natural spatiotemporal scene statistics. IEEE Trans. Image Processing 29, 5612–5624 (2020)
2. Ebenezer, J.P., Shang, Z., Wu, Y., Wei, H., Bovik, A.C.: No-reference video quality assessment using space-time chips. In: 2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6. IEEE (2020)
3. Ghadiyaram, D., Pan, J., Bovik, A.C., Moorthy, A.K., Panda, P., Yang, K.C.: In-capture mobile video distortions: a study of subjective behavior and objective algorithms. IEEE Trans. Circuits Syst. Video Technol. 28(9), 2061–2077 (2017)
4. Hosu, V., et al.: The Konstanz natural video database (konvid-1k). In: 2017 Ninth International Conference on Quality of Multimedia Experience (QoMEX), pp. 1–6. IEEE (2017)
5. Korhonen, J.: Two-level approach for no-reference consumer video quality assessment. IEEE Trans. Image Process. 28(12), 5923–5938 (2019)
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