Image quality assessment method based on support vector machine and particle swarm optimization

Abstract

In order to improve the assessment accuracy of white noise, Gauss blur, JPEG2000 compression and other distorted images, this paper puts forward an image quality assessment method based on support vector machine and particle swarm optimization. Firstly, it extracts the sample image data and determines the assessment indexes. Secondly, it pre-treats the sample data, including normalized and PCA (Principal Component Analysis) dimensionality reduction process. Thirdly, it uses particle swarm optimization to select the optimal parameters. Fourthly, it uses the best parameters to train the training set data. Finally, it predicts and analyzes the predictive set data and establishes the image quality assessment model. The experimental results show that the image quality assessment method has a higher accuracy than traditional method and it can accurately reflect the image visual perception of the human eye. © 2012 Springer-Verlag GmbH.