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You can find every near-black stretch in a video with FFmpeg’s blackdetect filter, which reports start time, end time, and duration for each interval that meets a minimum length. What it cannot do is decide whether a black stretch is a defect. A fade, a slate, or padding that aligns audio and video can all be correct. A useful quality-control (QC) gate therefore has three parts: a detector that produces timestamped events, a policy that sets acceptable limits for your own content, and a review step that runs before any check is allowed to block a release.
The forum question that often starts this search is simple: “Is there a way to find all the places where the image is black in a movie?” The answer is yes, with the caveats below.
What the detector measures
FFmpeg’s blackdetect filter looks for video intervals that are almost completely black. The FFmpeg Filters Documentation describes its use as detecting “video intervals that are (almost) completely black,” and notes that this can help find chapter transitions, commercials, or invalid recordings. The filter works on thresholds, not on a judgment of picture quality. Three options control its behavior:
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|---|---|---|
d |
2 seconds | Minimum length of a black interval before it is reported |
pic_th |
0.98 | Fraction of a frame’s pixels that must be below the luma threshold for the frame to count as black |
pix_th |
0.10 | Luma level below which an individual pixel counts as black |
The luma scale depends on whether the input uses full-range or limited-range YUV. Two files that look equally dark can therefore land on different sides of the same numeric cutoff. Confirm the range of your sources before you trust a threshold you have copied from documentation or another team’s pipeline.
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FFmpeg’s blackframe filter answers a different question: is this single frame black? Its documented defaults are 98 percent for the black-pixel amount and 32 for the threshold. For each frame it can report the frame number, the percentage of pixels considered black, the file position where known, and the timestamp.
Step 1: run the detector on the asset you will publish
Scan the exact rendition or output file that goes to viewers. Scanning the mezzanine master proves little about a later transcode, because encoding and padding can add or move black. The starting command is:
ffmpeg -hide_banner -i input.mp4 -vf "blackdetect=d=2:pix_th=0.10:pic_th=0.98" -an -f null -This command states the documented default values explicitly, so a later change to the defaults in a new FFmpeg build will not silently alter your results. The -an flag skips audio, and -f null - decodes the video for analysis without writing an output file. This is a working starting point derived from the filter syntax; it has not been validated against any particular library of content.
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Step 2: convert detector output into structured records
FFmpeg writes detected intervals to its info-level log, and it attaches lavfi.black_start and lavfi.black_end timestamps to frame metadata. Either route works. Capture the log, or read the metadata, and convert each event into a record that a later decision can reference. Each record should contain:
- The asset identifier and the exact file version that was scanned
- Start time, end time, and duration in seconds
- The
d,pix_th, andpic_thvalues used for that scan - The FFmpeg version and build, so results can be reproduced
- The date and time of the scan
Recording the threshold values with every event matters. Without them, a later change in policy makes old flags impossible to interpret.
Step 3: add a frame-level check where isolated frames matter
An interval detector ignores a single black frame if it is shorter than the minimum duration. If a flash frame between two cuts matters to your product, add blackframe and build a separate policy on its per-frame output. Keep this check separate from the interval gate so that a one-frame event does not trigger an interval rule, and an interval does not get counted twice.
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Step 4: separate defects from intentional black
Before any flag becomes a defect, sort it into one of these groups:
- Editorial fades. A fade to black at the end of a scene or at the start of a program is a deliberate creative choice.
- Slates and title cards. Holding on black with a text overlay or a logo is often intentional, though a slate with no overlay may need a second look.
- Alignment padding. Black may be added at the start or end of a file so that video duration matches audio duration. This is not automatically a defect.
- Source defects. Black from a failed capture, a dropped segment, or a broken edit is the case the gate exists to catch.
The three intentional categories should be exempted by policy, not by ignoring the report. Keep the flags, mark them as expected, and let reviewers see the reason.
Step 5: roll out the gate in stages
Report-only mode
Run the detector on representative content and record every event without affecting releases. Review the flags against the source and the edit. The goal is to learn how often your legitimate fades, slates, and padding trigger the rule, and how often real defects show up. The acceptable minimum duration and coverage are product decisions; the documentation does not establish a universal default for them.
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Warning mode
Once you have tuned the thresholds on your content, flag matches for human review but allow the release to continue. Track the review outcomes: how many flags were defects, how many were intentional, and how many were missed by the detector.
Blocking mode
Make the check blocking only for the classes of event your reviewers have confirmed as defects. Keep an override path with a named approver and a recorded reason, so that an intentional black sequence does not stop a release indefinitely.
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Comparing a self-managed FFmpeg gate with AWS MediaConvert
Teams that already run on AWS can compare the FFmpeg design with the metrics that AWS Elemental MediaConvert publishes. The two approaches expose different things, and their thresholds are not interchangeable.
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| Aspect | FFmpeg blackdetect |
MediaConvert black-video metrics |
|---|---|---|
| Control over detection | Explicit thresholds you set (d, pix_th, pic_th) |
Metric definitions set by AWS; you choose how to act on them |
| Output shape | Interval start, end, and duration, plus frame metadata | Job or output metrics: black-video duration, ratio, and longest continuous segment |
| Black inserted by the service | Not applicable; the tool scans the file you give it | The BlackVideoDetected metric covers black present in the input and excludes black frames that MediaConvert inserts |
| Operations | You run and maintain the workers and parse the output | Job events, CloudWatch alarms, and EventBridge actions |
| Cross-vendor comparison | Values are FFmpeg-specific | Values are AWS-specific; a threshold in one tool is not equal to a threshold in the other |
The AWS metric descriptions and the monitoring example come from an AWS technical blog post dated July 12, 2023. Check the current AWS documentation for the metric names, regional availability, and any service changes before relying on them in production.
Alerting in AWS
If you use MediaConvert, route the metrics into CloudWatch alarms for operational alerts, and use EventBridge to trigger a workflow when a job reports black video above your policy limit. This keeps the decision in the same place as the rest of your pipeline monitoring. The alarm and the workflow still need the same policy values as your FFmpeg gate if the two run side by side, because the metrics measure different things.
Amazon Rekognition is a different tool
Amazon Rekognition also has a black-frame filter. Its documented minimum black-pixel coverage default is 99 percent, and its threshold is described in terms of a maximum black pixel value rather than FFmpeg’s luma cutoff. It is a configurable API default, not an industry-wide standard, and its numbers should not be compared directly with the FFmpeg defaults.
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Quick Recap
What is and is not established
- The FFmpeg filter defaults and output fields above are taken from the FFmpeg Filters Documentation. Confirm that the FFmpeg build you deploy supports the same options.
- No published statistic that we could locate measures how often black-frame defects occur or how viewers respond to them. The figures in this article are filter and API defaults, not prevalence data.
- Detection accuracy, latency, and cost have not been measured here. Benchmark them on your own content before setting a blocking threshold.
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