Recent years have witnessed tremendous growth in the generation and consumption of digital images. Monitoring and evaluating image quality is an important issue for online and mobile media applications. Conventional quality assessment work mostly focus on intensity level distortion caused by operations that do not change image aspect ratio/size, such as distortion caused by compression, noise, and blurring. Here, we study the problem of quality assessment for images undergone content-adaptive resizing, also known as retargeting operations. Retargeting is an increasingly popular technique for rendering images to screens with different aspect ratios, and the dominant distortion in question is mostly geometrical at the content level rather than pixel-wise at the intensity level. Quality assessment on geometric distortion is not as well studied as the quality assessment on intensity-level distortions. In this work, we design a reduced-reference interest-point-matching-based framework to analyze the geometric distortion caused by retargeting and propose a set of candidate quality scores and fuse them to achieve positive correlation with human observations. (This paper is an unpublished manuscript last modified on 9/5/2013.)