Video Quality

Video quality assessment (VQA) focuses on developing methods to accurately measure how well a video is perceived by viewers, considering factors like compression artifacts, resolution, frame rate, and even aesthetic appeal. Current research emphasizes the development of deep learning models, often employing convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, to predict subjective quality scores from objective video features, addressing challenges like computational efficiency for high-resolution videos and the need for diverse datasets representing various content types and distortions. Improved VQA methods have significant implications for optimizing video compression, enhancing video streaming services, and guiding the development of new video processing technologies.