RHDDNet: Multi-label Classification-Based Detection of Image Hybrid Distortions

Image distortion detection is a key step in image quality assessment and image reconstruction algorithms. In previous work, a large number of research focus on detecting the single distortion in the image. However, the number of distortion types in the image is often uncertain. Thus, we propose a model that can be used for hybrid distortion detection. Concretely, we transform the hybrid distortion detection task into a multi-label classification task and abstract it as a convolutional network optimization problem. A dataset is created to train the model and evaluate its performance. Experiments show that the proposed model performs well in the detection of hybrid distortions in images.