Qualitative comparisons of temporal interpolation of any scale. Credit: Arxiv (2025). doi:10.48550/arxiv.2501.11043
A research team led by Professor Jaejoong Yoo of UNIST’s Graduate School of Artificial Intelligence has announced the development of an advanced artificial intelligence (AI) model.
The study was led by the first author, Eunjin Kim, and co-authored by Heeonjin Kim. Their findings were presented at the Conference on Computer Vision and Pattern Recognition (CVPR 2025) held in Nashville from June 11-15. This survey was posted on the ARXIV preprint server.
Resolution and frame rate are key factors that determine the quality of your video. Higher resolutions will result in sharper images with more detailed visuals, but higher frame rates ensure smoother movements without sudden jumps.
Traditional AI-based video repair techniques typically rely heavily on pre-trained optical flow prediction networks for motion estimation, which handle resolution and frame rate improvements individually. Light flow calculates the direction and velocity of the object’s movement to generate an intermediate frame. However, this approach involves complex calculations, is prone to accumulated errors, and limits both the speed and quality of video repair.
In contrast, “BF-STVSR” introduces a signal processing method tailored to video characteristics, allowing models to independently learn bidirectional movement between frames, without relying on external optical flow networks. By collaboratively inferring the contour and motion flow of objects, the model effectively enhances both resolution and frame rate at the same time, resulting in more natural and coherent video reconstruction.
Applying this AI model to low-resolution low frame-rate video demonstrated superior performance compared to existing models, as evidenced by peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) scores. The rise in PSNR and SSIM values indicates that even videos with critical motions retain clear, undistorted human figures and details, producing more realistic results.
Professor Yoo said, “The technology has a wide range of applications, from restoring security camera footage and black box recordings captured on low-end devices to rapid enhancing compressed streaming videos for high-quality media content. It also helps in fields such as medical imaging and virtual reality (VR).”
Details: Eunjin Kim et al, BF-STVSR: B-SPLINES and FORIER-BEST FRIENDS FOR High Fidelity Spatial-Temporal Video Super-Resolution, Arxiv (2025). doi:10.48550/arxiv.2501.11043
Journal Information: arxiv
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