IEEE Trans Image Process. 2017 Dec;26(12):5980-5993. doi: 10.1109/TIP.2017.2746260. Epub 2017 Aug 29.
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 paper, 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 data sets 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.
移动设备的飞速发展导致了大量数字图像的产生和使用。因此，图像质量评估是移动媒体应用中的一个重要问题。在本文中，我们专注于内容感知图像重定向生成的图像的质量评估，其中参考图像和失真图像的大小不同。通过重定向，许多类型的变形不一致会导致形状变形、变形伪影和内容信息丢失，从而降低其感知质量。变形不一致发生在重定目标图像的不同层次上。受原始图像和重定目标图像之间对齐精度的限制，以前的方法仅关注像素级和补丁级的保真度分析，无法检测到变形不一致。在本文中，我们改进了对齐算法，并提出了重定目标过程的三级表示。基于对这三级表示的分析，将保真度度量和不一致性检测相结合，以确定最终的重定目标质量。所提出的算法在公共数据集 RetargetMe 和 CUHK 上进行了验证。实验结果表明，不一致性检测有助于准确评估图像重定目标的感知质量。这启发我们进一步研究变形不一致性，以制定图像重定目标的客观质量。
