Nr Vqa

No-Reference Video Quality Assessment (NR-VQA) focuses on automatically evaluating video quality without needing a pristine original version, crucial for assessing user-generated content. Current research emphasizes developing robust and efficient NR-VQA models, employing deep learning architectures like convolutional neural networks and transformers, often incorporating multi-resolution processing to capture both global and fine-grained details. These advancements aim to improve video quality and user experience across various platforms, while also addressing vulnerabilities like adversarial attacks that can manipulate quality scores. The field's impact extends to enhancing video processing pipelines and improving the reliability of automated video quality control systems.