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. 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. 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. 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.