Quality Assessment Method

Quality assessment methods aim to objectively measure the quality of various data types, such as images, videos, text summaries, and even digital human models, often by correlating with human perception. Current research focuses on developing no-reference methods, particularly leveraging deep learning architectures like Vision Transformers and large language models, to overcome limitations of traditional approaches that require reference data or are computationally expensive. These advancements are crucial for improving various applications, including video super-resolution, text summarization, and biometric security systems, by enabling automated quality control and optimization.