基于彩色三要素的无参考对比度失真图像质量评价方法

图像质量评价是图像处理领域中基本且具有挑战性的问题。对比度失真对图像质量的感知影响较大，目前针对对比度失真图像的无参考图像质量评价研究相对较少。基于此，提出了基于彩色三要素的无参考对比度失真图像质量评价方法，利用彩色三要素的亮度、色调和饱和度3个参数实现了对比度失真图像的质量评价方法。在亮度方面，提取矩特征及图像直方图与均匀分布之间的Kullback-Leibler散度特征。在色调和饱和度方面，分别在HSV空间的H和S通道中提取颜色加权局部二值模式(LBP)直方图特征。利用AdaBoosting BP神经网络训练预测模型。在5个标准图像数据库中进行广泛的实验分析和交叉验证，结果表明，所提方法与现有的对比度失真图像质量评价方法相比，性能有明显的提升。 

No reference quality assessment method for contrast-distorted images based on three elements of color

Image quality assessment is a basic and challenging problem in the field of image processing, among which the contrast distortion has a greater impact on the perception of image quality. However, there is relatively little research on the no-reference image quality assessment of contrast-distorted images. This paper proposes a no-reference contrast-distorted image quality assessment method based on the three elements of color. The three parameters of brightness, hue and saturation of the three elements of color are used to realize the assessment of contrast-distorted images. First, in terms of brightness, the moment feature and the Kullback-Leibler divergence between the image histogram and the uniform distribution are extracted. Secondly, in terms of hue and saturation, the color-weighted local binary patterns (LBP) histogram features are extracted from the H and S channels of the HSV space, respectively. Finally, the AdaBoosting BP neural network is used to train the prediction model. Through extensive experimental analysis and cross-validation in five standard image databases, the experimental results show that the performance of this method is significantly improved compared with the existing contrast-distorted image quality assessment methods.