VideoClarity: High-speed meaning extraction in large video datasets

视频内容是蓬勃发展的英国和全球创意产业的主要商业资产，在今天的“大数据”洪流中占相当大的比例。除了创意产业之外，视频是一种主要的通信工具，占据了当今互联网流量的60%。然而，数以百万计的用户每周创建、处理和（不总是合法的）上传数百万字节的视频内容，视频仍然是大数据生态系统中最难管理的元素。这是因为目前所有的视频高级语义描述方法都需要手动注释或计算密集型的视频解码和处理。因此，提供具有成本效益和有意义的视频搜索已被证明是一个无法克服的问题。这个项目采用了一种新颖的见解：在现代压缩视频文件容器中已经存在与编码相关的元数据的隐藏源，并且它足以实现自动标记和可视化。利用这种“提示”元数据，可以以3~6个订单的速度增加和成本降低来分析视频流，从而使艾字节规模的视频数据集能够在商用硬件上被新发现和分析。

Video content is a primary business asset of the thriving UK and global creative industries, and represents a substantial proportion of today’s “big data” deluge. Beyond the creative industries, video comprises a major communications tool, occupying 60% of today’s Internet traffic. However, with millions of users creating, processing and (not always legally) uploading exabytes of video content each week, video remains the least-manageable element of the big data ecosystem. This is because all current methods for high-level semantic description in video require either manual annotating or compute-intensive video decoding & processing. Delivering cost-effective and meaningful video search has therefore proved to be an insurmountable problem. This project takes a novel insight: that a hidden source of coding-related metadata already exists within modern compressed video file containers, and it is sufficient for automated tagging and visualization. Tapping into this “hint” metadata enables video streams to be analyzed with 3~6 orders increase in speed and decrease in cost, enabling exabyte-scale video datasets to be newly-discovered and analysed over commodity hardware.