# STEC: A Reference-Free Spatio-Temporal Entropy Coverage Metric for Evaluating Sampled Video Frames

arXiv: https://arxiv.org/abs/2601.13974 · HTML: https://mlsanity.com/paper/2601.13974/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 20 Jan 2026.

## Builds on (most cited first, 5 of 5)

- [Adaptive Keyframe Sampling for Long Video Understanding](https://mlsanity.com/paper/2502.21271/) (2025)
- [M-LLM Based Video Frame Selection for Efficient Video Understanding](https://mlsanity.com/paper/2502.19680/) (2025)
- [Rethinking the Evaluation of Video Summaries](https://mlsanity.com/paper/1903.11328/) (2019)
- [KeyVideoLLM: Towards Large-scale Video Keyframe Selection](https://mlsanity.com/paper/2407.03104/) (2024)
- [From Captions to Keyframes: KeyScore for Multimodal Frame Scoring and Video-Language Understanding](https://mlsanity.com/paper/2510.06509/) (2025)

## Abstract (arXiv)

Frame sampling is a fundamental component in video understanding and video--language model pipelines, yet evaluating the quality of sampled frames remains challenging. Existing evaluation metrics primarily focus on perceptual quality or reconstruction fidelity, and are not designed to assess whether a set of sampled frames adequately captures informative and representative video content. We propose Spatio-Temporal Entropy Coverage (STEC), a simple and non-reference metric for evaluating the effectiveness of video frame sampling. STEC builds upon Spatio-Temporal Frame Entropy (STFE), which measures per-frame spatial information via entropy-based structural complexity, and evaluates sampled frames based on their temporal coverage and redundancy. By jointly modeling spatial information strength, temporal dispersion, and non-redundancy, STEC provides a principled and lightweight measure of sampling quality. Experiments on the MSR-VTT test-1k benchmark demonstrate that STEC clearly differentiates common sampling strategies, including random, uniform, and content-aware methods. We further show that STEC reveals robustness patterns across individual videos that are not captured by average performance alone, highlighting its practical value as a general-purpose evaluation tool for efficient video understanding. We emphasize that STEC is not designed to predict downstream task accuracy, but to provide a task-agnostic diagnostic signal for analyzing frame sampling behavior under constrained budgets.