IEEE Trans Image Process. 2017 Nov;26(11):5138-5148. doi: 10.1109/TIP.2017.2736422. Epub 2017 Aug 4.
Image retargeting technology has been widely studied to adapt images for the devices with heterogeneous screen resolutions. Meanwhile effective objective retargeting quality assessment algorithms are also very important for optimizing and selecting favorable retargeting methods. Unlike previous assessment algorithms which rely on image local structure features and unidirectional prediction of information loss, we propose a bi-directional natural salient scene distortion model (BNSSD) including image natural scene statistics (NSS) measurement, salient global structure distortion measurement, and bi-directional salient information loss measurement. First, we propose a new NSS model in log-Gabor domain and verify its effectiveness in reflecting nature scene statistical distortions introduced during the retargeting process. Second, the concept of salient global structure distortion is proposed to measure the global structure uniformity in the corresponding salient regions between original and retargeted images. Finally, we propose a bidirectional salient information loss metric to measure the information loss between salient areas in original image and retargeted image. The effectiveness of the BNSSD model is verified on two widely recognized public databases, and the experimental results show that our method outperforms the state-of-the-art algorithms under different statistical assessment criteria.
图像重定向技术已被广泛研究，以适应具有异构屏幕分辨率的设备。同时，有效的客观重定向质量评估算法对于优化和选择有利的重定向方法也非常重要。与以前依赖于图像局部结构特征和信息损失单向预测的评估算法不同，我们提出了一种包括图像自然场景统计（NSS）测量、显著全局结构失真测量和双向显著信息损失测量的双向自然显著场景失真模型（BNSSD）。首先，我们在对数 Gabor 域中提出了一个新的 NSS 模型，并验证了其在反映重定目标过程中引入的自然场景统计失真方面的有效性。其次，提出了显著全局结构失真的概念，以测量原始图像和重定目标图像之间对应显著区域的全局结构均匀性。最后，我们提出了一种双向显著信息损失度量，用于测量原始图像和重定目标图像中显著区域之间的信息损失。在两个广泛认可的公共数据库上验证了 BNSSD 模型的有效性，实验结果表明，在不同的统计评估标准下，我们的方法优于最新算法。
