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Build the worker as a cascade: use SHA-256 to find byte-identical files, a documented perceptual-hash method to shortlist visually similar videos, and FFmpeg’s MPEG-7 video signature to examine the remaining candidates. Treat the last two stages as similarity signals, not proof of identity. Validate the complete pipeline on representative videos and use review or reversible quarantine until its false-positive behavior is known.
What each stage can—and cannot—tell you
| Stage | What it compares | Best use | Important limit |
|---|---|---|---|
| SHA-256 | Input bytes, summarized as a 256-bit message digest | Grouping likely byte-identical files | Does not identify visually similar files encoded differently. For destructive action, verify byte equality or define an explicit collision-risk policy. RFC 6234 |
| Perceptual hash | A representation of sampled visual content, compared using a chosen distance rule | Generating candidates that may look alike despite file or encoding changes | Algorithm, sampling policy, and cutoff must be selected and tested for your collection; no universal settings are established here. |
| FFmpeg signature | Video signatures calculated and compared by FFmpeg’s signature filter |
Further comparison of shortlisted videos | Configurable thresholds are implementation options, not accuracy guarantees or universal recommendations. FFmpeg filter documentation |
This order is an architectural proposal: inexpensive byte hashing first, then visual candidate generation, then signature comparison on a reduced set. No benchmark establishes the speed or accuracy of the full cascade. Measure it with your codecs, hardware, workload, and archive.
Stage 1: use SHA-256 for exact-content candidates
Stream each complete file through SHA-256 and store its digest alongside stable asset metadata. SHA-256 produces a 256-bit message digest of the input data; it is not a visual-content descriptor. A re-encode, resize, or other byte change means the input is different, even if the video appears the same. RFC 6234
Keep the fingerprint record separate from the media and include at least:
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- The digest and algorithm identifier.
- A stable path or object key and byte length.
- Ingest time and the fingerprint computation version.
Use matching digests to create strong exact-duplicate candidates. If the worker will delete anything, verify the bytes themselves or adopt and document an explicit collision-safe policy; do not make irreversible decisions from a digest match alone.
Stage 2: generate candidates with a perceptual hash
Choose a perceptual-hash implementation, a frame-sampling policy, and a distance rule. The evidence does not establish a specific algorithm, sampling interval, or threshold, so these are decisions to test against the transformations your ingestion path actually produces.
Choose what to sample and compare
Decide whether to sample frames at fixed time intervals, at scene changes, or with another repeatable policy. Denser sampling may capture more of a video’s content but requires more frame processing and fingerprint storage. Sparse sampling costs less but can miss short scenes or edits. These are trade-offs to measure, not established performance results.
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Evaluate the hash against recompression, resolution changes, crops, borders or letterboxing, frame-rate changes, overlays, and edits separately. Scene cuts and added graphics can affect sampled frames; a distance cutoff that works for one kind of change may behave differently for another.
Record parameters and route cautiously
Store the implementation and version, sampling policy, and comparison rule with each fingerprint. Send only candidates that meet your validated rule to the signature stage. Do not treat a near hash as proof that two files are interchangeable.
Stage 3: compare shortlisted videos with FFmpeg’s signature filter
FFmpeg describes its signature filter as “Calculate the MPEG-7 video signature.” The filter source includes both signature calculation and lookup logic. Its documented lookup modes are off, full, and fast; it also exposes binary and XML output formats and options for detection mode, output filename, and matching thresholds. FFmpeg filter documentation
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The source documents defaults of th_d=9000, th_dc=60000, th_xh=116, th_di=0, and th_it=0.5. These are FFmpeg implementation defaults, not recommended settings, accuracy guarantees, or a standard for every archive. Test threshold choices on labeled pairs before using them to classify videos.
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The filter’s documented supported pixel formats include gray, several planar YUV formats, NV12, and NV21. Check the exact FFmpeg build you deploy and normalize inputs when needed. Because the filter and its options can vary by version, consult that installed version’s documentation for invocation syntax rather than assuming a command copied from another build will apply. The source exposes dynamic inputs and a filename option for the signature workflow. FFmpeg filter documentation
Build the cascade as a measurable worker
- Ingest and fingerprint. Stream the full file through SHA-256. Persist the digest, asset metadata, and algorithm marker.
- Group exact candidates. Find matching digests and, before destructive action, verify equality or apply your documented risk policy.
- Generate visual fingerprints. For assets not resolved as exact duplicates, run the chosen perceptual-hash implementation with a fixed, versioned sampling policy.
- Shortlist by measured distance. Apply the cutoff established by validation and pass only near-match candidates onward.
- Calculate or look up signatures. Use the deployed FFmpeg build’s documented filter options and selected thresholds on the reduced candidate set.
- Report or quarantine. Record why each pair was flagged and keep near matches reviewable or reversible until end-to-end behavior has been measured.
Store fingerprints independently of the media so they can be recomputed without changing source assets. Version the FFmpeg build, hash implementation, frame sampling, and all thresholds; otherwise results from different runs may not be interpretable.
Validate against the collection before automating decisions
Create a labeled set containing true duplicate pairs and hard negatives: videos with the same subject, scene, or title that are nevertheless different content. Include the transformations expected in real ingestion, such as recompression, resizing, borders, overlays, frame-rate changes, and edits.
Measure false positives and false negatives at each stage and across the complete cascade. Set different consequences for exact matches, likely matches, and review candidates: the cost of missing a duplicate is not the same as the cost of merging or deleting distinct videos. Start with candidate reports or reversible quarantine. Enable automatic deletion only after representative validation demonstrates that the end-to-end behavior meets your risk tolerance.
There are no established comparative timing, precision, recall, or storage benchmarks for this proposed three-stage worker. Measure candidate volume, processing time, storage, and error rates on the codecs, hardware, and archive you intend to use. Recheck the documentation for the installed FFmpeg version; the online trunk documentation can change.
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