基于SVM和PSO的图像质量评价

为提高白噪声、高斯模糊、JPEG2000压缩等失真类型图像的评价准确率，提出一种基于支持向量机和粒子群优化算法的图像质量评价方法。提取样本图像数据和确定评价指标，对样本数据进行预处理。利用粒子群优化算法选择最优参数，使用最优参数对训练集数据进行训练，对预测集数据进行预测分析，并建立图像质量评价模型。实验结果表明，与线性回归模型、BP神经网络模型等传统方法相比，该方法的评价准确率较高，能够准确地反映人眼对图像的视觉感知。

Image Quality Assessment Based on Support Vector Machine and Particle Swarm Optimization

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(SVM) and Particle Swarm Optimization(PSO). It extracts the sample image data and determines the assessment indexes. It pre-treats the sample data, including normalized and Principal Component Analysis(PCA) dimensionality reduction process. It uses PSO to select the optimal parameters. It uses the best parameters to train the training set data. It predicts and analyzes the predictive set data and establishes the image quality assessment model. 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.