IEEE Trans Image Process. 2018 Sep;27(9):4516-4528. doi: 10.1109/TIP.2018.2839890.
In this paper, an accurate and efficient full-reference image quality assessment (IQA) model using the extracted Gabor features, called Gabor feature-based model (GFM), is proposed for conducting objective evaluation of screen content images (SCIs). It is well-known that the Gabor filters are highly consistent with the response of the human visual system (HVS), and the HVS is highly sensitive to the edge information. Based on these facts, the imaginary part of the Gabor filter that has odd symmetry and yields edge detection is exploited to the luminance of the reference and distorted SCI for extracting their Gabor features, respectively. The local similarities of the extracted Gabor features and two chrominance components, recorded in the LMN color space, are then measured independently. Finally, the Gabor-feature pooling strategy is employed to combine these measurements and generate the final evaluation score. Experimental simulation results obtained from two large SCI databases have shown that the proposed GFM model not only yields a higher consistency with the human perception on the assessment of SCIs but also requires a lower computational complexity, compared with that of classical and state-of-the-art IQA models. The source code for the proposed GFM will be available at http://smartviplab.org/pubilcations/GFM.html.
本文提出了一种基于提取的 Gabor 特征的准确高效的全参考图像质量评估 (IQA) 模型，称为 Gabor 特征基础模型 (GFM)，用于对屏幕内容图像 (SCI) 进行客观评估。众所周知，Gabor 滤波器与人眼视觉系统 (HVS) 的响应高度一致，而 HVS 对边缘信息非常敏感。基于这些事实，利用具有奇数对称性并产生边缘检测的 Gabor 滤波器的虚部，分别对参考 SCI 和失真 SCI 的亮度进行提取。然后分别测量提取的 Gabor 特征和 LMN 颜色空间中记录的两个色度分量的局部相似性。最后，采用 Gabor 特征池化策略来组合这些测量值并生成最终的评估得分。来自两个大型 SCI 数据库的实验模拟结果表明，与经典和最先进的 IQA 模型相比，所提出的 GFM 模型不仅在评估 SCI 方面与人类感知具有更高的一致性，而且需要更低的计算复杂度。拟议的 GFM 的源代码将可在 http://smartviplab.org/pubilcations/GFM.html 获得。
