Enhancing CNN-Based Blind Image Quality Assessment via Deep Cross-Layer Pattern Encoding

Evaluating image quality without reference images, known as blind image quality assessment (BIQA), is crucial for image communication. Recently, convolutional neural networks (CNNs) have emerged as a prominent BIQA approach due to their feature learning power. Usually, both high-level semantic information and low-level details significantly impact perceived visual quality. However, most existing CNN-based methods focus on high-level semantic information via aggregating features on top of the last convolutional layer into a global descriptor, neglecting the importance of shallow, low-level cues. To address this limitation, this paper proposes a novel approach that exploits local encoding and histogram-based pyramid pooling on crosslayer features produced by a CNN, achieving a joint local and global analysis. Specifically, we introduce a cross-layer pattern encoding model that characterizes features generated along convolutional layers via a soft histogram of local 3D binary patterns. This leads to a highly informative yet compact descriptor for score regression. By building this module into a ResNet backbone, we present an effective BIQA model demonstrating state-ofthe-art performance in extensive experiments on synthetic and authentic datasets.