Efficient motion-based metrics for video frame interpolation

2025-08-12

AI Summary

Evaluating the perceptual quality of intermediate frames generated by video frame interpolation remains challenging, particularly due to the limitations of pixel-level fidelity metrics in capturing visual comfort and motion smoothness. Method: This paper proposes a novel no-reference quality metric based on optical flow field divergence, explicitly modeling motion smoothness and perceptual comfort through a motion consistency measure—bypassing reliance on pixel-wise reconstruction error. The metric is calibrated via regression against subjective scores from the BVI-VFI dataset. Contribution/Results: Compared to FloLPIPS, the proposed metric achieves a 2.7× speedup in computation while attaining a PLCC of 0.51 with subjective ratings—significantly outperforming PSNR and SSIM. It reliably identifies interpolated frames exhibiting low distortion yet high visual comfort. To our knowledge, this is the first work to explicitly incorporate flow field divergence into frame interpolation quality assessment, striking a superior balance between perceptual consistency and computational efficiency, thereby providing a more reliable evaluation benchmark for advanced interpolation algorithms.

Technology Category

• Computer Vision: Motion & Tracking
• Knowledge Representation and Reasoning: Qualitative Reasoning
• Search and Optimization: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

Video frame interpolation (VFI) offers a way to generate intermediate frames between consecutive frames of a video sequence. Although the development of advanced frame interpolation algorithms has received increased attention in recent years, assessing the perceptual quality of interpolated content remains an ongoing area of research. In this paper, we investigate simple ways to process motion fields, with the purposes of using them as video quality metric for evaluating frame interpolation algorithms. We evaluate these quality metrics using the BVI-VFI dataset which contains perceptual scores measured for interpolated sequences. From our investigation we propose a motion metric based on measuring the divergence of motion fields. This metric correlates reasonably with these perceptual scores (PLCC=0.51) and is more computationally efficient (x2.7 speedup) compared to FloLPIPS (a well known motion-based metric). We then use our new proposed metrics to evaluate a range of state of the art frame interpolation metrics and find our metrics tend to favour more perceptual pleasing interpolated frames that may not score highly in terms of PSNR or SSIM.

Problem

• Assessing perceptual quality of interpolated video frames
• Developing efficient motion-based video quality metrics
• Evaluating frame interpolation algorithms using motion divergence

Innovation

• Motion field divergence for quality metrics
• Computationally efficient motion-based evaluation
• Perceptual quality focus over PSNR/SSIM