基于底层特征和高级语义的真实失真图像质量评价

目的由于现有无参考质量评价方法无法准确判断真实失真图像的质量,提出一种基于图像底层特征和高级语义提取的真实失真图像质量评价方法。方法首先根据真实失真图像的底层特征指标进行k-means聚类,在每一类图像中利用深度卷积神经网络(Deep Convolutional Neural Network, DCNN)的方法提取图像的一级高级语义特征,采用多种特征函数对一级高级语义特征进行聚合,得到图像的二级高级语义特征,并建立了高级语义特征与平均意见主观分(Mean Opinion Score, MOS)的高容量回归器。结果提出的算法在KonIQ-10k图像库预测出的质量分数与对应MOS值能达到很高的一致性,Spearman秩序相关系数(SROCC)和Pearson线性相关系数(PLCC)分别能达到0.95和0.97。结论提出的算法能够快速且准确地对真实失真图像质量作出评价。

Real Distorted Images Quality Assessment Based on Image Underlying Features and High-Level Semantics

The paper aims to propose a real distortion IQA method based on image underlying features and high-level semantics in view of the fact that the existing non-reference IQA methods can not accurately evaluate the quality of the real distortion image. Firstly, k-means clustering algorithm was performed according to the underlying feature index of the image Secondly, the deep convolutional neural network (DCNN) was used to extract the first-grade high-level semantics in each group. Then, second-grade high-level semantics that can provide better representation of image features were obtained by performing multiple statistical functions on first-grade high-level semantics. Besides, we established an effective high-capacity regressor with high-level semantics and subjective mean opinion scores (MOS) values of the human eyes. The experimental results showed that the proposed model on the KonIQ-10k image database can predict the quality score effectively and achieve a high consistency with the corresponding MOS value. Spearman order correlation coefficient (SROCC) and Kendall order correlation coefficient (KROCC) can reach 0.95 and 0.97, respectively. The proposed method can quickly and accurately evaluate the quality of real distorted image.