KonIQ-10k extended with annotations about the presence and type of defectKonIQ++ contains labels for 10,073 images regarding the presence of four categories of distortion, as an extension of the original KonIQ-10k dataset. KonIQ++ is meant to explore how this information is beneficial to NR-IQA (quality score prediction), and also to evaluate how well image distortions could be identified.
Each participant in the study provided a 5-point rating for the technical quality, from bad (1) to excellent (5), and a binary annotation for the presence and type of distortion visible. The categories of distortion were blurs, artifacts, colors, contrast, and a category for "other" (multiple could be selected). This yielded a mean opinion score in [0,1] for each of the distortions, from an average of 58 ratings per image. In the paper we also introduce a NR-IQA model, with two side networks connected to a shared backbone network that predict both image quality and distortion magnitude. You can find the code on Github. |
Cite usKonIQ++ is freely available to the research community. If you use our database in your research, you can cite it as follows:
@inproceedings{su2021koniq++, author={S. {Su}, V. {Hosu}, H. {Lin}, Y. {Zhang}, D. {Saupe}}, booktitle={The 32nd British Machine Vision Conference (BMVC)}, title={KonIQ++: Boosting No-Reference Image Quality Assessment in the Wild by Jointly Predicting Image Quality and Defects}, year={2021}} |
Downloads
1.6 Mb download
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The annotations file header
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