Compressed Video

Compressed video research focuses on improving the quality and usability of videos subjected to compression, aiming to mitigate artifacts and enhance various aspects like visual fidelity, heart rate estimation accuracy from video, and gait recognition reliability. Current research employs deep learning models, often incorporating generative adversarial networks (GANs) and convolutional neural networks (CNNs), leveraging techniques like motion vector priors, temporal alignment, and adaptive quantization parameter handling to achieve this. These advancements have significant implications for various applications, including video streaming, surveillance, and remote healthcare, by enabling higher-quality video experiences even under bandwidth constraints.