Infrared Image Quality Estimation With Node-to-Graph Regression

By comparison with the commonly seen visible light images that can be effectively characterized within a Euclidean space, infrared images have non-Euclidean characteristics since their pixels contain rich thermal radiation information, such as heat distribution, surface temperature and thermal radiation. Considering the advantages of Graph Convolutional Networks (GCNs) in processing non-Euclidean data, this study proposes to introduce the GCNs to estimate the quality of infrared images by developing the Node-to-Graph Regression (NGR) model. To specify, the proposed NGR model is composed of two main steps, namely network establishment and network training. In the first step, following the classical researches of image quality estimation that include local distortion measurement followed by pooling for inferring the image quality score, this study captures the local distortion of the input infrared images by stacking up a set of Vision Graph (VSG) blocks to generate one node map, and then conducts the weighted pooling method on the node map to yield the graph output as the estimated quality score. In the second step, for enhancing the model's performance and generalization ability in the network training process, this study implements the node regression with the big data pre-training method to raise the local distortion extraction ability in a broad range of image scenarios and distortion intensities, and then performs the graph regression by using the knowledge distillation method to reduce the over-fitting risk. Using the largest-size infrared image quality evaluation database (I2QED), this study compared the proposed NGR model with three dozen mainstream and state-of-the-art competitors, and results showed that our proposed NGR model achieved the optimal performance.