Objective Quality Assessment of Image Retargeting by Incorporating Fidelity Measures and Inconsistency Detection.

The tremendous growth in mobile devices has resulted in huge generation and usage of digital images. Image quality assessment is thus an important issue for mobile media applications. In this work, we focus on the quality evaluation of images generated by content-aware image retargeting, in which the reference and the distorted images are of different sizes. Through retargeting, many types of deformation inconsistency lead to shape distortion, deformation artifacts and content information loss, worsening its perceptual quality. The deformation inconsistency occurs on different levels of the retargeted images. Limited by the accuracy of the alignment between the original and retargeted images, previous methods only focus on pixel-level and patch-level fidelity analyses and fail to detect deformation inconsistency. In this paper, we improve the alignment algorithm and propose a three-level representation of the retargeting process. Based on the analysis of this three-level representation, both fidelity measures and inconsistency detection are combined to determine the final retargeting quality. The proposed algorithm is validated on the public datasets RetargetMe and CUHK. Experimental results demonstrate that inconsistency detection contributes to accurately assessing the image retargeting perceptual quality. This inspires us to investigate more about deformation inconsistency to formulate the objective quality of image retargeting.