IEEE Trans Image Process. 2018 Jan;27(1):451-463. doi: 10.1109/TIP.2017.2761556. Epub 2017 Oct 9.
Objective image retargeting quality assessment aims to use computational models to predict the retargeted image quality consistent with subjective perception. In this paper, we propose a multiple-level feature (MLF)-based quality measure to predict the perceptual quality of retargeted images. We first provide an in-depth analysis on the low-level aspect ratio similarity feature, and then propose a mid-level edge group similarity feature, to better address the shape/structure related distortion. Furthermore, a high-level face block similarity feature is designed to deal with sensitive region deformation. The multiple-level features are complementary as they quantify different aspects of quality degradation in the retargeted image, and the MLF measure learned by regression is used to predict the perceptual quality of retargeted images. Extensive experimental results performed on two public benchmark databases demonstrate that the proposed MLF measure achieves higher quality prediction accuracy than the existing relevant state-of-the-art quality measures.
客观图像重定向质量评估旨在使用计算模型预测与主观感知一致的重定向图像质量。在本文中，我们提出了一种基于多层次特征 (MLF) 的质量度量方法来预测重定向图像的感知质量。我们首先对低层次的纵横比相似性特征进行了深入分析，然后提出了一种中层边缘组相似性特征，以更好地解决与形状/结构相关的失真问题。此外，还设计了一个高层的面部块相似性特征来处理敏感区域变形。多层次特征是互补的，因为它们量化了重定向图像中质量下降的不同方面，并且通过回归学习到的 MLF 度量用于预测重定向图像的感知质量。在两个公共基准数据库上进行的广泛实验结果表明，所提出的 MLF 度量方法比现有的相关最先进的质量度量方法具有更高的质量预测准确性。
