Deep Image Quality

Deep image quality assessment aims to develop computational methods that accurately predict the perceived quality of images, aligning with human judgment. Current research focuses on leveraging deep learning architectures, particularly those incorporating large language models and attention mechanisms, to improve the correlation between automated scores and human perception, addressing limitations of traditional metrics like SSIM and PSNR. This field is crucial for optimizing image processing techniques across various applications, from e-commerce to video enhancement, by providing objective and explainable quality measures that guide algorithm development and improve user experience. Furthermore, the exploration of deep image quality models offers insights into the principles of human visual perception.