Blind Image Quality Assessment

Blind Image Quality Assessment (BIQA) focuses on automatically evaluating the perceived quality of images without needing a reference image, aiming to mimic human visual perception. Current research emphasizes developing robust and efficient models, often employing deep learning architectures like convolutional neural networks (CNNs) and transformers, sometimes incorporating techniques such as multi-scale feature extraction, attention mechanisms, and self-supervised learning to improve accuracy and generalization across diverse image content and distortion types. These advancements are crucial for applications ranging from image enhancement and compression to automated content curation and quality control in various multimedia domains. The field is also actively exploring explainable BIQA methods and addressing challenges like domain adaptation and the efficient use of limited training data.