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Video Metadata Search: Finding Footage by Date, Format, Resolution, and More
Video files carry two types of metadata: technical facts about the file itself and AI-generated descriptions of what is inside. Combining both in a single search turns a broad query into a precise one.
Every video file carries metadata. Some of it is obvious: the filename, the date it was created, how long it is. Some of it requires extraction: the codec, the resolution, the color space, the frame rate. And some of it does not exist in the file at all until AI generates it: transcripts, scene descriptions, object labels, face clusters.
Video metadata search uses all of these layers to help you find footage. Understanding what metadata exists and how to use it changes how efficiently you can search a large library.
Two types of video metadata
Video metadata falls into two categories, and they serve different purposes in search.
Technical metadata
Technical metadata is embedded in the file by the camera or recording device. It describes facts about the file itself:
- Codec and container. H.264, H.265, ProRes 422 HQ, R3D, BRAW, DNxHR, XAVC.
- Resolution. 1920x1080, 3840x2160, 4096x2160, 6144x3456, 8192x4320.
- Frame rate. 23.976, 24, 25, 29.97, 30, 48, 60, 120.
- Color space and gamma. Rec.709, Rec.2020, S-Log3, V-Log, REDWideGamutRGB, ACES.
- Duration. How long the clip is, from seconds of B-roll to hours of continuous recording.
- File size. Storage footprint. Varies based on codec, resolution, and bitrate.
- Creation date. When the file was recorded. The single most useful field for narrowing searches.
This metadata exists from the moment the file is created. It requires no processing to extract.
Descriptive metadata (AI-generated)
Descriptive metadata is generated by AI analysis of the video content:
- Transcripts. Every word spoken, with word-level timestamps and speaker attribution.
- Object labels. Every identifiable object detected in the video frames, timestamped.
- Scene descriptions. Natural-language summaries of what is happening visually.
- Face clusters. Identified individuals who appear in the footage.
- Color characteristics. Dominant colors and color profiles of the footage.
This metadata does not exist until you generate it. It requires processing the video through AI analysis models. Once generated, it is stored in a search index alongside the technical metadata.
How metadata filters narrow search results
Using metadata filters before or alongside a content search dramatically reduces your result set. Instead of searching your entire library and scrolling through hundreds of matches, you apply filters that eliminate irrelevant results before you even see them.
The effect is cumulative. Each filter narrows the pool further:
- Search "product demo" across your full library: 200 results
- Add date filter (last 6 months): 40 results
- Add resolution filter (4K only): 15 results
- Add codec filter (ProRes): 6 results
You went from a broad result set to a precise one by layering metadata constraints. No scrolling, no guesswork.
Date range filtering: the most effective first step
If you could only use one metadata filter, make it date range. Date filtering is the single most effective way to narrow a video search because you almost always have a rough idea of when something was shot.
"It was from the March shoot." "It was sometime last fall." "It was definitely this year." Even an approximate date range eliminates a huge portion of your library.
FrameQuery supports both absolute date ranges (March 1 to March 31, 2026) and relative ranges (last 30 days, last quarter, this year). For teams that shoot regularly, relative date ranges are especially useful for finding recent footage without specifying exact dates.
Resolution and codec filters for delivery workflows
Resolution and codec filters serve a specific purpose: ensuring you find footage that meets delivery requirements.
When a client requests 4K ProRes deliverables, you need to find your 4K ProRes originals, not HD proxies or compressed MP4 exports of the same content. A content search alone cannot distinguish between the same interview recorded as a 4K ProRes master and a 1080p H.264 copy. Metadata filters can.
Common filtering patterns for delivery:
- 4K or above when the deliverable requires high resolution
- ProRes or DNxHR when the post-production pipeline requires an intermediate codec
- R3D or BRAW when you need the original camera RAW files for maximum grading flexibility
- H.264 or H.265 when you are looking for compressed delivery copies
These filters also help identify what you have. Filtering by codec across your entire library shows you the format breakdown of your archive, which is useful for storage planning and transcoding decisions.
Combining technical and descriptive metadata
The real power of video metadata search is combining both types in a single query. Technical metadata narrows the pool. Descriptive metadata finds the content.
Example: Finding delivery-grade interview footage. Search for "interview" (descriptive, matches transcripts and scenes) + 4K resolution (technical) + ProRes codec (technical) + last 3 months (technical). You get only recent, high-resolution, production-quality interview recordings.
Example: Locating B-roll for social media. Search for "aerial city" (descriptive, matches scene descriptions) + under 60 seconds duration (technical) + HD or above (technical). You get short, high-quality aerial city shots suitable for social content.
Example: Finding a specific person's footage for archival. Search for a specific face (descriptive, face recognition) + 2024 date range (technical). You get every appearance of that person from that year, regardless of format or camera.
How FrameQuery extracts and indexes both types
FrameQuery handles both metadata types automatically. When you add source folders, the initial scan extracts technical metadata immediately by reading file headers. You can filter by date, resolution, codec, and duration before any AI processing runs. When you process videos, AI models generate the descriptive metadata: transcripts, object labels, scene descriptions, and face clusters. Both types join the same Tantivy search index, queryable together and evaluated instantly.
No manual tagging required
Traditional media management relies on manual metadata entry: watching footage, typing keywords, assigning categories. This works at small scale but collapses under volume. Nobody is going to manually tag 10,000 clips.
AI-generated descriptive metadata eliminates that bottleneck. Every video gets transcribed, objects get detected, scenes get described, and faces get clustered, all automatically. Technical metadata is extracted from the file itself. The baseline searchability of your library does not depend on anyone finding time to tag footage. It happens automatically during processing.
Join the waitlist to search your footage by metadata and content when FrameQuery launches.