VSFA code for the following papers: Dingquan Li, Tingting Jiang, and Ming Jiang. Quality Assessment of In-the-Wild Videos. In Proceedings of the 27th ACM International Conference on Multimedia (MM ’19), October 21-25, 2019, Nice, France. [arxiv version] You need to specify the database and change the corresponding videos_dir.

2020年4月9日 · vsfa方法：提高直播视频质量评估的准确性 通过上述知识点的详细介绍，我们可以了解到 VSFA 框架在直播 视频 质量 评估中的应用背景、技术细节 和 实现方法。

2019年8月1日 · We propose an objective no-reference video quality assessment method by integrating both effects into a deep neural network. For content-dependency, we extract features from a pre-trained image classification neural network for its inherent content-aware property.

Download the KoNViD-1k, CVD2014 (alternative link), LIVE-Qualcomm, and LIVE-VQC datasets. Then, run the following ln commands in the root of this project. # Training, intra-dataset …

利用了人脸视觉系统 (human visual system, HVS)的两个主要效应，即content-dependency和 temporal-memory effects，把这两个特性结合到深度神经网络中，提出一种主观的无参考的视频质量评价方法。

With more than 9,000 members across the Commonwealth, the VSFA works to provide for better and affordable fire protection in Virginia by advocating for legislative action to benefit Virginia fire departments, rescue squads and support Virginia’s emergency responders.

2020年3月25日 · 1.Human judgments of visual image/video quality depend on content：主观评价受图片内容的影响。 2.Human judgments of video quality are affected by their temporal memory.之前质量差的帧，对后续帧的评分有影响。 (temporal hysteresis effect) 作者提出的方法，用图像分类任务中预训练的 CNN 提取内容信息，然后用 GRU 建模时序信息，再用 temporal pooling 解决 temporal hysteresis effects。 如下图: 2. Proposed Method.

二. vsfa算法的解决措施. 作者分析了对人类视觉系统影响比较大的两个特性: 内容依赖性与时间记忆性. 2.1 内容依赖: 上面每对图像都是在相同的拍摄条件下（例如焦距、物体距离）拍摄的。对于第一行的聚焦图像对，10 个受试者中有 9 个更喜欢左边的那个；

2024年9月2日 · VSFA（Video Quality Assessment in the Wild）是基于ACM MM 2019会议发表的一篇论文实现的开源项目。 作者丁全、蒋婷婷和蒋鸣通过此项目提出了一个用于评价真实拍摄视频质量的框架。

VSFA code for the following papers: Dingquan Li, Tingting Jiang, and Ming Jiang. Quality Assessment of In-the-Wild Videos. In Proceedings of the 27th ACM International Conference on Multimedia (MM ’19), October 21-25, 2019, Nice, France. [arxiv version] Intra-Database Experiments (Training and Evaluating) Feature extraction