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The Quality Indexing endpoints publish what was measured about your footage. They do not publish a verdict about it — you compose that yourself, from numbers you can re-total however you like. Every field’s type is in the API reference and its meaning is in the Metric glossary; this page is about how the pieces fit together and why they are shaped the way they are.

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Counts and seconds are the source of truth

A share commits you to a denominator we picked. Every per-clip figure is therefore a count of frames or a number of seconds, so any subset of clips you care about — one operator, one site, one week of collection — can be totalled without asking us to compute it. The corpus-level endpoints do publish shares, because at corpus level the denominator is unambiguous, and each one arrives beside the population it was computed over: how many clips were assessed, how much footage could not be, and how many clips were never decoded. A number without its denominator is not worth much, so they always travel together.

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An absent field is not a zero

A clip that carries no frame_quality object was never decoded. A clip whose frame_quality.active is 0 was decoded and found to contain no usable frames. Those are opposite facts, and averaging the first as if it were the second silently drags your numbers down. Nothing is filled in with zeros to make the rows uniform — check for the field’s presence before you aggregate it. Which fields this applies to, and what absence means for each, is in the Metric glossary. The Quickstart walks the endpoints in the order you would call them.

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Descriptive and directional metrics

Vocabulary diversity is descriptive. More environments, objects, or actions is not automatically better: a buyer should compare the returned terms, tiers, and coverage with the kind of footage they need. The same corpus therefore has one query-independent vocabulary, while different buyers can make different decisions from it. Cleanliness is directional: a larger usable share generally indicates more legible footage. jerky is the exception inside it, because it cannot separate camera shake from fast subject motion — see the glossary before you treat it as a defect.

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Reconciling the counts

Everything a section reports describes the same set of terms, so these hold and are worth asserting in your own pipeline:
  • The term table is exactly the published terms. Every one of them is either counted in distinct_terms or listed in untiered_terms.
  • No clip row names a term absent from the table.
  • rarefied_distinct_terms is the only count comparable against a different corpus, and comparable_depth_clips states the depth it was taken at. Setting two raw counts side by side compares sampling effort as much as content; the glossary says why.

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Access

Every endpoint here requires a credential and returns only datasets your organization is entitled to read. See Authentication.