论文标题
基于卷积激活图的组合全参考图像质量评估方法
A combined full-reference image quality assessment approach based on convolutional activation maps
论文作者
论文摘要
全参考图像质量评估（FR-IQA）的目的是预测人类观察者使用其原始的参考物质所感知的图像的质量。在这项研究中，我们探索了一种新颖的合并方法，该方法通过从卷积激活图中编译特征向量来预测扭曲图像的感知质量。更具体地说，通过验证的卷积神经网络运行了参考图像对，并将激活图与传统的图像相似性度量进行了比较。随后，借助训练有素的支持向量回归器，将所得的特征向量映射到感知质量得分上。还提出了一项详细的参数研究，其中推理了该方法的设计选择。此外，我们研究训练图像量与预测性能之间的关系。具体而言，可以证明，所提出的方法可以用很少的数据进行培训以达到高预测性能。将我们的最佳建议 - ACTMAPFEAT与六个公有可用基准IQA数据库的最先进的提案进行了比较，例如KADID-10K，TID2013，TID2008，MDID，CSIQ和VCL-FER。具体而言，我们的方法能够显着胜过这些基准数据库的最先进。
The goal of full-reference image quality assessment (FR-IQA) is to predict the quality of an image as perceived by human observers with using its pristine, reference counterpart. In this study, we explore a novel, combined approach which predicts the perceptual quality of a distorted image by compiling a feature vector from convolutional activation maps. More specifically, a reference-distorted image pair is run through a pretrained convolutional neural network and the activation maps are compared with a traditional image similarity metric. Subsequently, the resulted feature vector is mapped onto perceptual quality scores with the help of a trained support vector regressor. A detailed parameter study is also presented in which the design choices of the proposed method is reasoned. Furthermore, we study the relationship between the amount of training images and the prediction performance. Specifically, it is demonstrated that the proposed method can be trained with few amount of data to reach high prediction performance. Our best proposal - ActMapFeat - is compared to the state-of-the-art on six publicly available benchmark IQA databases, such as KADID-10k, TID2013, TID2008, MDID, CSIQ, and VCL-FER. Specifically, our method is able to significantly outperform the state-of-the-art on these benchmark databases.
