基于Riesz变换的结构相似度图像质量评价方法

Image quality assessment method via Riesz-transform based structural similarity

不同的图像处理过程,会对图像引入各种各样的失真,如何对图像的质量进行评价成为一个热点问题。针对传统的基于像素差值统计的峰值信噪比方法及结构相似度方法与人眼主观评价不够符合的情况,本文提出了一种基于Riesz变换的结构相似度图像质量评价方法。该方法先将参考图像和失真图像进行一阶Riesz变换和二阶Riesz变换,并利用得到的5组对应特征图计算出5幅相似度图和5幅权重图,利用平均法进行融合得到最终的相似度图和权重图,然后加入原参考图像和失真图像的亮度比较项,得到最终的图像质量评价指标。在LIVE图像数据库上的实验表明,本文方法对于5种失真的质量预测准确性和一致性都很高,在交叉失真实验中,本文方法也优于结构相似度方法,PLCC和SROCC值达到了0.9482和0.9532。与几种公认较好的方法相比,本文方法能够更好地预测图像质量,更加符合人眼的主观感知。

Various distortions will be introduced to images during different image processing proce-dures,and the assessment of image quality has become a hot topic.According to the fact that the tra-ditional pixel-difference statistics-based PSNR method and structural similarity method cannot corre-late well with human subjective evaluation,a novel image quality assessment method via Riesz-Trans-form based structural similarity is proposed.The method firstly applies 1st-order Riesz-Transform and 2nd-order Riesz Transform to the reference image and distorted image,and 5 groups of corre-sponding features maps are obtained to produce 5 similarity maps and 5 weighting maps.Then the average-method fusion is performed to get the final similarity map and weighting map.Lastly,the lu-minance comparison of the original reference image and distorted image is considered to get the final image quality index.The experiments on the LIVE database indicate that the proposed method has high prediction accuracy and consistency on all 5 distortion types.It also outperforms the SSIM method under cross-distortion conditions and the PLCC value and SROCC value reach 0.948 2 and 0.953 2 respectively.Compared with other state-of-the-art methods,the proposed method owns good predictive performance,which indicates better consistency with human subjective perception.