Multiple-Degradation CID:IQ Dataset

This work introduces a novel Image Quality Assessment (IQA) dataset designed to mirror real-world scenarios by incorporating diverse and mixed image degradations. Unlike existing IQA datasets that primarily focus on single types of distortion, this dataset encompasses a wide spectrum of real-world challenges, including noise, blur, compression, and colour gamut mappings. This comprehensive approach aims to better reflect the complexity of images encountered in everyday life and addresses the limitations of conventional IQA models. By providing a multifaceted benchmark, this dataset facilitates the development of robust and versatile IQA algorithms, ultimately improving the accuracy and applicability of image quality evaluation in various practical domains.