Image Quality

Image quality assessment (IQA) focuses on objectively measuring and improving the visual fidelity of images, crucial for various applications from medical imaging to autonomous driving. Current research emphasizes developing robust no-reference IQA methods, often employing deep learning architectures like transformers and convolutional neural networks, and exploring the use of generative AI models for image enhancement and compression. These advancements are significant because they enable automated quality control, improved diagnostic accuracy in healthcare, and more efficient data management across numerous fields.