Reference Quality

Reference quality assessment, crucial for evaluating the fidelity of various media types, is actively being researched, particularly focusing on no-reference methods due to the frequent unavailability of pristine originals. Current efforts leverage deep learning architectures, including convolutional neural networks, vision transformers, and ResNets, to analyze image and video content, as well as 3D point clouds, extracting features indicative of quality degradation. These advancements are improving objective quality metrics for diverse applications, from assessing user-generated content and medical imaging to optimizing video compression and transmission. The development of robust and efficient no-reference metrics is vital for advancing multimedia processing and ensuring high-quality experiences across various platforms.