摘要
针对当前高动态范围(HDR)图像质量评价方法未考虑图像色度和结构信息的问题,提出了一种新的HDR图像客观质量评价方法。首先,利用HDR-VDP-2.2中的基于视觉感知的模型得到关于亮度与对比度的视觉保真度特征;然后,将HDR图像转换到YIQ彩色空间,对彩色空间中的Y、I、Q通道分别进行处理,求得色度相似度和结构相关度特征;最后,利用支持向量回归(SVR)的方法对特征进行融合,预测得到高动态范围图像质量的客观评价值。实验结果表明,与HDR-VDP-2.2相比,该方法的Pearson相关系数和Spearman等级相关系数分别提升了23.09%和25.34%;均方根误差(RMSE)降低了38.01%。所提出的方法与主观视觉感知具有更高的一致性。
Aiming at the problem that High Dynamic Range （HDR） image quality evaluation method does not consider the color and structure information of HDR image, a novel objective quality assessment method of HDR image was proposed. Firstly, the feature of visual fidelity about brightness and contrast was obtained based on the visual model of HDR-VDP-2.2. Then, the HDR image was transformed into the YIQ color space, and the color similarity and structural correlation coefficient were gotten by dealing with the Y, I, Q channel, respectively. Finally, Support Vector Regression （SVR） was used to fuse the features, and the objective evaluation value of the high dynamic range image quality could be obtained by predicting the similarity degree and the struetural relevance degree. The experimental results show that compared with HDR-VDP-2.2, the Pearson correlation coefficient and Spearman rank correlation coefficient of the proposed method are increased by 23.09% and 25.34%, respectively; the Root Mean Square Error （RMSE） is reduced by 38.01%. The proposed method has higher consistency with subjective visual perception.
出处
《计算机应用》
CSCD
北大核心
2017年第3期695-698,745,共5页
journal of Computer Applications
基金
国家自然科学基金资助项目(61271270)
浙江省自然科学基金资助项目(LY15F010005)~~
关键词
高动态范围图像
质量评价
特征
支持向量回归
视觉感知
High Dynamic Range （HDR） image
quality assessment
feature
Support Vector Regression （SVR）
visual perception
