Interpolation Quality

Interpolation quality, focusing on accurately estimating intermediate values between known data points, is a crucial area of research across diverse scientific fields. Current efforts concentrate on improving the efficiency and accuracy of interpolation methods, particularly within image and video processing, using techniques like deep neural networks (DNNs), Gaussian process regression, and novel architectures incorporating multi-flow prediction and coarse-to-fine processing. These advancements are driving improvements in applications ranging from image editing and video frame synthesis to scientific machine learning, where rigorous error bounds and verifiable accuracy are paramount. The development of robust and efficient interpolation techniques is essential for advancing numerous scientific and technological domains.