VideoClarity: High-speed meaning extraction in large video datasets

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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.