Deep Blind Image Quality Assessment Using Dual-Order Statistics

Deep convolutional neural networks (CNNs) have become a promising approach to blind image quality assessment (BIQA). Existing CNN-based BIQA methods often employ global average pooling (GAP) to aggregate feature maps into a fixed-size representation for regression, so as to handle input images with varying sizes. However, GAP is only capable of extracting the first-order statistics of feature distributions, which is ineffective for distinguishing complex distortions that cause local degradation or preserve global features. To tackle this problem, we introduce the second-order global covariance pooling (GCP) for aggregating feature maps, leading to a more distortion-sensitive and more discriminative global representation. By incorporating GCP and GAP into a ResNet backbone, we propose an effective deep model for BIQA. The experimental results on five BIQA benchmark datasets, including both the synthetic and authentic ones, have demon-strated the excellent performance of the proposed method.