In this paper, a no-reference metric for evaluating the quality of multi-distortion images is introduced. This metric is based on a combination of structural features and image brightness. First, the structural features and brightness of the image, which change drastically due to distortion, were extracted. For different datasets, an optimal combination of properties was obtained by the particle swarm optimization algorithm. The optimal combination of features was supported by regression vector regression to the training model so that the trained model could measure the quality of other images. Due to the comprehensiveness of the selected features, this metric has the ability to measure image quality with a variety of degradations. According to the results of the implementation of the criterion, we had a significant improvement and also according to the research, the optimal combination of image properties has been obtained to investigate specific degradations, which can be useful for further research in the future.
Davoodi, Z. (2022). Feature Selection for no reference multi-distortion image quality assessment Based on Particle Swarm Optimization Algorithm. Journal of Machine Vision and Image Processing, 9(3), 35-48.
MLA
Zahra Davoodi. "Feature Selection for no reference multi-distortion image quality assessment Based on Particle Swarm Optimization Algorithm". Journal of Machine Vision and Image Processing, 9, 3, 2022, 35-48.
HARVARD
Davoodi, Z. (2022). 'Feature Selection for no reference multi-distortion image quality assessment Based on Particle Swarm Optimization Algorithm', Journal of Machine Vision and Image Processing, 9(3), pp. 35-48.
VANCOUVER
Davoodi, Z. Feature Selection for no reference multi-distortion image quality assessment Based on Particle Swarm Optimization Algorithm. Journal of Machine Vision and Image Processing, 2022; 9(3): 35-48.
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