E-Bench: Subjective-Aligned Benchmark Suite for Text-Driven Video Editing Quality Assessment

• URL: http://arxiv.org/abs/2408.11481v1
• Date: Wed, 21 Aug 2024 09:49:32 GMT
• Title: E-Bench: Subjective-Aligned Benchmark Suite for Text-Driven Video Editing Quality Assessment
• Authors: Shangkun Sun, Xiaoyu Liang, Songlin Fan, Wenxu Gao, Wei Gao,
• Abstract summary: E-Bench is a benchmark suite tailored to the assessment of text-driven video editing. It includes E-Bench DB, a video quality assessment (VQA) database for video editing. E-Bench QA focuses on the text-video alignment and the relevance modeling between source and edited videos.
• Score: 6.627422081288281
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-driven video editing has recently experienced rapid development. Despite this, evaluating edited videos remains a considerable challenge. Current metrics tend to fail to align with human perceptions, and effective quantitative metrics for video editing are still notably absent. To address this, we introduce E-Bench, a benchmark suite tailored to the assessment of text-driven video editing. This suite includes E-Bench DB, a video quality assessment (VQA) database for video editing. E-Bench DB encompasses a diverse set of source videos featuring various motions and subjects, along with multiple distinct editing prompts, editing results from 8 different models, and the corresponding Mean Opinion Scores (MOS) from 24 human annotators. Based on E-Bench DB, we further propose E-Bench QA, a quantitative human-aligned measurement for the text-driven video editing task. In addition to the aesthetic, distortion, and other visual quality indicators that traditional VQA methods emphasize, E-Bench QA focuses on the text-video alignment and the relevance modeling between source and edited videos. It proposes a new assessment network for video editing that attains superior performance in alignment with human preferences. To the best of our knowledge, E-Bench introduces the first quality assessment dataset for video editing and an effective subjective-aligned quantitative metric for this domain. All data and code will be publicly available at https://github.com/littlespray/E-Bench.

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