无参考图像质量评价是近年来的研究热点,目前常用的评价算法都是从灰度空间提取特征。为了增加颜色通道信息对图像质量的反馈,分别提取了RGB(Red,Green,Blue )、LAB(Luminosity,A,B)、HSV(Hue,Saturation,Value)颜色空间中各通道下的亮度去均值对比度归一化(MSCN)系数,并用非对称广义高斯分布模型(AGGD)拟合。对拟合得到的MSCN系数统计特征,用梯度提升回归算法训练,得到无参考图像质量评价模型,并将各颜色通道训练模型和灰度空间训练模型的预测分数与主观评分进行比较。结果表明,相比灰度空间,部分颜色通道下的无参考图像质量评价模型的单调性、主客观一致性、稳定性都有一定提升,用RGB_B通道下提取的特征训练的模型性能最好,Pearson相关系数从0.63提升到0.70。

Non-reference image quality evaluation is a research hotspot in recent years. At present, the commonly used evaluation algorithms are extracting features from gray space. In order to increase the reflection of the color channel information on the image quality, the mean subtracted contrast normalized (MSCN) coefficients of each channel in the RGB(Red, Green, Blue), LAB(Luminosity, A, B), and HSV(Hue, Saturation, Value) color spaces are extracted, respectively, and fitted through asymmetric generalized Gaussian distribution model. The statistical features of the fitted MSCN coefficients are trained by gradient boosting regression algorithm to obtain a non-reference image quality evaluation model. The predicted scores of each color channel training model and gray space training model are individually compared with subjective scores. The results show that the monotonicity, subjective and objective consistency, and stability of the non-reference image quality evaluation model under some color channels are improved to some extent compared to the gray space. The model trained with the features extracted under the RGB_B channel has the best performance, Pearson related coefficient increases from 0.63 to 0.70.