An Efficient Quality Metric for Video Frame Interpolation Based on Motion-Field Divergence

Abstract

Video frame interpolation is a fundamental tool for temporal video enhancement, but existing quality metrics struggle to evaluate the perceptual impact of interpolation artefacts effectively. Metrics like PSNR, SSIM and LPIPS ignore temporal coherence. State-of-the-art quality metrics tailored towards video frame interpolation, like FloLPIPS, have been developed but suffer from computational inefficiency that limits their practical application. We present ${\text{PSNR}}_{\text{DIV}}$, a novel full-reference quality metric that enhances PSNR through motion divergence weighting, a technique adapted from archival film restoration where it was developed to detect temporal inconsistencies. Our approach highlights singularities in motion fields which is then used to weight image errors. Evaluation on the BVI-VFI dataset (180 sequences across multiple frame rates, resolutions and interpolation methods) shows ${\text{PSNR}}_{\text{DIV}}$ achieves statistically significant improvements: +0.09 Pearson Linear Correlation Coefficient over FloLPIPS, while being 2.5$\times$ faster and using 4$\times$ less memory. Performance remains consistent across all content categories and are robust to the motion estimator used. The efficiency and accuracy of ${\text{PSNR}}_{\text{DIV}}$ enables fast quality evaluation and practical use as a loss function for training neural networks for video frame interpolation tasks. An implementation of our metric is available at www.github.com/conalld/psnr-div.

IEEE 17th International Conference on Quality of Multimedia Experience 2025 accepted manuscript, 7 pages

References in corpus (6)

• Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index
• ST-MFNet: A Spatio-Temporal Multi-Flow Network for Frame Interpolation
• ST-GREED: Space-Time Generalized Entropic Differences for Frame Rate Dependent Video Quality Prediction
• Deep Slow Motion Video Reconstruction with Hybrid Imaging System
• A Subjective Quality Study for Video Frame Interpolation
• BVI-VFI: A Video Quality Database for Video Frame Interpolation