The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
Open accessAbstract
We present a deep neural network-based approach to image quality assessment (IQA). The network is trained end-to-end and comprises ten convolutional layers and five pooling layers for feature extraction, and two fully connected layers for regression, which makes it significantly deeper than related IQA models. Unique features of the proposed architecture are that: 1) with slight adaptations it can be used in a no-reference (NR) as well as in a full-reference (FR) IQA setting and 2) it allows for joint learning of local quality and local weights, i.e., relative importance of local quality to the global quality estimate, in an unified framework. Our approach is purely data-driven and does not rely on hand-crafted features or other types of prior domain knowledge about the human visual system or image statistics. We evaluate the proposed approach on the LIVE, CISQ, and TID2013 databases as well as the LIVE In the wild image quality challenge database and show superior performance to state-of-the-art NR and FR IQA methods. Finally, cross-database evaluation shows a high ability to generalize between different databases, indicating a high robustness of the learned features.
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Cites
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“The obtained mean opinion score (MOS) values lie in the range [0, 9], where larger MOS indicate better visual”
BackgroundNo-Reference Image Quality Assessment in the Spatial Domain
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On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
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Self-citationSignature Verification Using A "Siamese" Time Delay Neural Network
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Sattler, F., Wiedemann, S., Muller, K.-R., & Samek, W. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2944481 · Cited by 1,725
Self-citationNo-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency
Golestaneh, S. A., Dadsetan, S., & Kitani, K. M. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv51458.2022.00404 · Cited by 465
PieAPP: Perceptual Image-Error Assessment Through Pairwise Preference
Prashnani, E., Cai, H., Mostofi, Y., & Sen, P. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00194 · Cited by 339
State-of-the-Art in 360° Video/Image Processing: Perception, Assessment and Compression
Xu, M., Li, C., Zhang, S., & Callet, P. L. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.2966864 · Cited by 281
Hu, J., Liao, X., Wang, W., & Qin, Z. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3074259 · Cited by 195
Metadata
- Authors
- Bosse, S., Maniry, D., Muller, K.-R., Wiegand, T., & Samek, W.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2018-01
- Volume
- 27
- Issue
- 1
- Pages
- 206-219
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2563786098
- Cited by
- 1,138
- References
- 9
- FWCI
- 28.98
- Type
- journal article
- Language
- en
- Open access
- hybrid
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