Blind Image Quality Assessment Based on Perceptual Comparison

Blind image quality assessment (BIQA) is a regression task with continuous label space, the feature space of which is expected to have a corresponding continuity in the target space. However, existing approaches typically learn quality score regression directly in an end-to-end fashion, which leaves networks susceptible to interference from task-agnostic information, and fails to capture the continuity of BIQA. In this work, by explicitly establishing inter-sample associations, a simple yet effective BIQA framework based on perceptual comparison is proposed to capture the continuity. To this end, besides the basic quality score regression, the relative quality scores between images are predicted to exploit the relative quality relationships between samples for optimizing the representation of image perceptual quality. In addition, based on the human perceptual characteristic, we derive a novel sample weighting strategy to dynamically adjust the weights for different samples in the network learning process for further improving the robustness of the model. The performances on both single-database and cross-database experiments achieve state-of-the-art, indicating the effectiveness of the proposed method. Besides, the proposed framework is model-agnostic, which can effectively improve the performance of the benchmark model with no extra inference cost.