基于尺度不变性的无参考图像质量评价

现有的通用型无参考图像质量评价方法大多是利用失真图像及其主观值来训练回归模型预测图像质量指标,然而这种方法需要消耗大量的时间进行训练,并且评价效果依赖于训练图像库中的失真类型,通用性较差,很难应用到实际场合中。为了解决数据库依赖问题,提出一种归一化的基于图像尺度不变性的无参考图像质量评价方法。该方法不依赖外部数据,将图像的统计特性及边缘结构特性作为图像质量评价的有效特征,利用图像多尺度不变性计算多尺度间的整体特征差异,从而预测图像质量。实验结果表明,所提方法对混合失真图像质量评价效果好,运行效率高,与目前现有的无参考图像质量评估方法相比具有较好的综合性能,具有较好的应用价值。

No-reference image quality assessment based on scale invariance

The existing general no-reference image quality assessment methods mostly use machine learning method to learn regression models from training images with associated human subjective scores to predict the perceptual quality of testing image. However, such opinion-aware methods expend much time on training, and rely on the distortion types of the training database. These methods have weak generalization capability, hereby limiting their usability in practice. To solve the database dependence, a normalized scale invariance based no-reference image quality assessment method was proposed. In the proposed method, the Natural Scene Statistic (NSS) feature and edge characteristic were combined as the valid features for image quality assessment, and no extra information was required beyond the testing image, then the two feature vectors were used to compute the global difference across scales as the image quality score. The experimental results show that the proposed method has good evaluation for multi-distorted images with low computational complexity. Compared to the state-of-the-art no-reference image quality assessment models, the proposed method has better comprehensive performance, and it is suitable for applications.