目的 图像缩放质量评价是评估图像非等比例缩放优劣的依据，现有主流评价方法不能客观有效地给出定量评价结果，为此提出一种基于重要区域保持的图像非等比例缩放评价方法。方法 首先，结合视觉显著度、图像边缘和超像素分割构造重要区域识别算法；然后，设计描述图像缩放前后重要区域大小和内容保持的面积变化函数和成分变化函数；最后混合两种函数提出缩放质量评价函数。结果 在RetargetMe提供的标准测试集上对本文方法进行实验验证，计算缩放质量值以评价不同缩放方法的优劣，并利用肯德尔系数度量本文方法的评价结果与主观评价的一致性，本文方法的平均肯德尔系数比当前主流客观方法高0.5%~11%。结论 与现有主流客观评价方法相比，本文方法所得到的肯德尔系数值更大，证明该方法能够更准确有效、快速、定量地评价各类图像非等比例缩放结果。

Objective Image retargeting quality assessment is used to describe the quality of image retargeting results from different retargeting methods. However,the availablemethods cannot provide efficient assessments with objective and quantitative values. Thus, we present a new image retargeting quality assessment method based on the preservation of important regions in image retargeting. Method First, a new important region identification algorithm is defined by combining salient region detection, image edges, and superpixel segmentation. Second, area change and component change functions are constructed to describe the area and component preservation of the important regions in image retargeting. Finally, the image retargeting quality assessment function is defined by combining the first two steps. Result We conduct our experiments on the basic image database proposed in RetargetMe, which is a benchmark for image retargeting. We then compute for the quality value of the retargeted results to assess the different retargeting methods. Kendall coefficients are used to measure the consistency between our assessment and the subjective assessment. The average value of Kendall coefficient overcomes 0.5%—11% then the values of present objective assessment methods.Conclusion Compared with those of available objective assessment methods, the Kendall coefficients of the proposed method are larger. This finding demonstrates that the proposedmethod can quantitatively assess the retargeting results of different kinds of images accurately, efficiently, and rapidly.