Perceptual Quality Metric

Perceptual quality metrics aim to quantitatively assess the quality of images and videos as perceived by humans, a crucial task given the rise of AI-generated content and advanced image/video processing techniques. Current research focuses on developing metrics that better correlate with human judgments, particularly for specialized applications like video frame interpolation and audio-driven talking heads, often employing deep learning models such as transformers and exploring spatio-temporal feature extraction. The development of accurate perceptual quality metrics is vital for objectively evaluating and improving these technologies, facilitating progress in fields ranging from entertainment to medical imaging.