多特征融合的图像质量评价方法

为了规避图像质量评价中的视觉特征和池化策略难以选取和解释的问题,在提取参考图像和失真图像的多种底层特征的基础上,采用机器学习的方法自动预测真实图像质量,提出多特征融合的图像质量评价方法.针对参考图像和失真图像分别提取相位一致性、梯度、视觉显著性、对比度特征,计算4种特征的相似度图,提取相似度图的均值和方差特征,最后采用支持向量回归评价文中方法.在LIVE、CSIQ、TID2008和TID2013图像库上的实验表明,文中方法的主客观一致性较好.

Multi-feature Fusion Based Image Quality Assessment Method

To avoid the difficulties in choosing and explaining the visual features and pooling strategies in image quality assessment, a reference images quality assessment method based on multi-feature fusion is proposed. Various underlying features of reference and distorted images are extracted,and a machine learning method is applied to predict the quality of real images. Firstly, phase congruency, gradient, visual saliency and contrast of reference and distorted images are extracted. Then similarity maps of four features are calculated, respectively. The mean and variance characteristics of these similarity maps are extracted. Finally, the assessment model is learned by support vector regression. The experimental results on four benchmark databases demonstrate a high coherence between subjective and objective assessment by the proposed method.