An opinion-unaware blind quality assessment algorithm for multiply distorted images

Tongle Wang; Junchen Deng

doi:10.1117/12.2559541

31 December 2019 An opinion-unaware blind quality assessment algorithm for multiply distorted images

Tongle Wang, Junchen Deng

Proceedings Volume 11384, Eleventh International Conference on Signal Processing Systems; 1138415 (2019) https://doi.org/10.1117/12.2559541
Event: Eleventh International Conference on Signal Processing Systems, 2019, Chengdu, China

Abstract

The blind image quality assessment algorithms produced every year are mostly “opinion-aware” (OA). It means that they require large numbers of subjective quality scores for regression model training. Subjective quality scores are not easily available, so people are eager to design an opinion-unaware (OU) algorithm which has free subjective quality scores. Besides, the OU algorithm has greater generalization capability than the OA algorithm. Therefore, we propose an OU algorithm based on a visual codebook for multiply distorted image quality assessment. Extensive experiments conducted on the three databases demonstrate that the proposed method is superior to the existing five OU methods in terms of the coherence with the human subjective rating. The MATLAB code is available at https://tonglewang.github.io.

Citation Download Citation

Tongle Wang and Junchen Deng "An opinion-unaware blind quality assessment algorithm for multiply distorted images", Proc. SPIE 11384, Eleventh International Conference on Signal Processing Systems, 1138415 (31 December 2019); https://doi.org/10.1117/12.2559541

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KEYWORDS

Image quality

Databases

Visualization

Distortion

Calibration

Feature extraction

Binary data

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