Deep Ordinal Regression Framework for No-Reference Image Quality Assessment

Huasheng Wang, Yulin Tu, Xiaochang Liu, Hongchen Tan, Hantao Liu

Abstract: Due to the rapid development of deep learning techniques, no-reference image quality assessment (NR-IQA) has achieved significant improvement. NR-IQA aims to predict a real-valued variable for image quality, using the image in question as the sole input. Existing deep learning-based NR-IQA models are formulated as a regression problem and trained by minimising the mean squared error. The error measurement does not consider the relative ordering between different ratings on the quality scale, which consequently affects the efficacy of the model. To account for this problem, we reformulate NR-IQA learning as an ordinal regression problem and propose a simple yet effective framework using deep convolutional neural networks (DCNN) and Transformers. NR-IQA learning is achieved by a deep ordinal loss and using a soft ordinal inference to transform the predicted probabilities to a continuous variable for image quality. Experimental results demonstrate the superiority of our proposed NR-IQA model based on deep ordinal regression. In addition, this framework can be easily extended with various DCNN architectures to build advanced IQA models.