Existing NR-IIQA (no reference-based inpainted image quality assessment) algorithms assess the quality of an inpainted image via artificially designed unnaturalness expression, which often fail to capture inpainted artifacts. This paper presents a new deep rank learning-based method for NR-IIQA. The model adopts a siamese deep architecture, which takes a pair of inpainted images as input and outputs their rank order. Each branch utilizes a CNN structure to capture the global structure coherence and a patch-wise coherence assessment module (PCAM) to depict the local color and texture consistency in an inpainted image. To train the deep model, we construct a new dataset, which contains thousands of pairs of inpainted images with ground-truth quality ranking labels. Rich ablation studies are conducted to verify the key modules of the proposed architecture. Comparative experimental results demonstrate that our method outperforms existing NR-IIQA metrics in evaluating both inpainted images and inpainting algorithms.

现有的 NR-IIQA（无基于参考的修复图像质量评估）算法通过人工设计的不自然表达来评估修复图像的质量，这通常无法捕获修复的伪影。本文提出了一种新的基于深度学习的 NR-IIQA 方法。该模型采用孪生深度架构，以一对修复图像作为输入并输出它们的排序顺序。每个分支利用 CNN 结构来捕获全局结构连贯性，并利用逐块连贯性评估模块 (PCAM) 来描绘修复图像中的局部颜色和纹理一致性。为了训练深度模型，我们构建了一个新数据集，其中包含数千对带有真实质量排名标签的修复图像。进行了丰富的消融研究以验证所提出架构的关键模块。比较实验结果表明，我们的方法在评估修复图像和修复算法方面均优于现有的 NR-IIQA 指标。