Blind Image Quality Assessment with a Probabilistic Quality Representation

Most existing blind image quality assessment (BIQA) methods learn a regression model to predict scalar quality scores. Such a scheme ignores the fact that an image will receive divergent subjective scores from different subjects, which cannot be adequately represented by a single scalar number. This is particularly true on complex, real-world distorted images. However, the more informative score distributions are unavailable in existing image quality assessment (IQA) databases and can be potentially noisy when limited number of opinions are collected on each image. This paper proposes a probabilistic quality representation (PQR) and employs a more robust loss function to train deep BIQA models. Using a very straightforward implementation, the proposed method is shown to not only speed up the convergence of deep model training, but also greatly improve the quality prediction accuracy relative to scalar quality score regression methods under the same setting. The source code is available at https://github.com/HuiZeng/BIQA_Toolbox.