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 VE-Bench, a benchmark suite tailored to the assessment of text-driven video editing. This suite includes VE-Bench DB, a video quality assessment (VQA) database for video editing. VE-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 VE-Bench DB, we further propose VE-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, VE-Bench QA focuses on the text-video alignment and the relevance modeling between source and edited videos. It introduces a new assessment network for video editing that attains superior performance in alignment with human preferences.To the best of our knowledge, VE-Bench introduces the first quality assessment dataset for video editing and proposes an effective subjective-aligned quantitative metric for this domain. All models, data, and code will be publicly available to the community.

The increasing demand for large-scale, high-quality datasets in dynamic point cloud compression (PCC) and human visual perception research underscores the limitations of existing datasets, which are often constrained by limited scale and insufficient dynamism, hindering algorithm validation and perceptual analysis in complex scenarios. To address this gap, we present DPCSet, a comprehensive dynamic point cloud dataset designed to support advanced research in PCC, human perception, and related domains. Comprising 100 dynamic object point clouds-the largest collection of its kind-DPCSet includes 200-frame sequences with geometry and attribute information, capturing diverse object types across real and virtual environments. Organized into seven superclasses, the dataset ensures broad scenario coverage. By rigorous selection, format conversion, quantization, DPCSet delivers standardized, high-precision point cloud data. Evaluation of multiple compression algorithms on a curated subset demonstrates DPCSet's efficacy in assessing trade-offs between compression efficiency and quality loss, positioning it as a potential benchmark for PCC. Furthermore, just noticeable distortion (JND) experiments on a compression-distorted subset reveal distinct perceptual characteristics of dynamic point clouds, offering valuable insights for perception-driven compression algorithms. The dataset is released at https://openi.pcl.ac.cn/gaowx/DPCSet.

Subjective experiments driven by human visual perception for images aid in the development of related technologies such as analysis, compression, and transmission, which garners substantial attention and research interest. However, the general subjective experiment platform is relatively lacking, and verifying the reliability of annotation data is often difficult. In response to these challenges, an open source subjective experiment platform, namely OpenSEP, is proposed in this paper. Specifically, OpenSEP mainly includes a contrast mode sub-platform that displays dual stimuli, allowing for the simultaneous display of both source stimulus and distorted stimulus for subjective testing. Moreover, a scoring mode sub-platform that displays single stimulus is also provided in OpenSEP. In this mode, subjects can only score the distorted stimulus individually after sequentially viewing the source stimulus and all the distorted stimuli. Besides, OpenSEP constructs a cross-validation sub-platform integrated mainstream Just Noticeable Distortion (JND) algorithms. Within this sub-platform, the reliability of subjective annotation data can be verified based on existing JND algorithms, and the effectiveness of newly proposed modeling algorithms can also be validated. The open source library for OpenSEP is available at https://openi.pcl.ac.cn/OpenDatasets/OpenSEP