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ZPE Video Masthead

ZPE Video

Perception receipts for AI video pipelines. Cross-writer bit-exact under default settings. Zero runtime dependencies.

  • Package: zpe-video v0.1.0
  • Python: 3.11 / 3.12 / 3.13
  • Repository: https://github.com/Zer0pa/ZPE-Video
  • Contact: architects@zer0pa.ai

What This Is

Perception-receipt video codec. Staged v0.1.0 receipt surface with explicit non-claims for C2PA and end-to-end latency.

Codec Mechanics

Field Value
Architecture VIDEO_RECEIPT_STREAM
Encoding VIDEO_PERCEPTION_RECEIPT_V1
Mechanics Asset Not assigned

Key Metrics

Metric Value Baseline
CROSS_WRITER_HASH_STABLE TRUE vs default-Parquet FALSE
STORAGE_VS_DEFAULT_PARQUET 0.18× vs default-Parquet 1.00×
CACHE_READ_VS_DEFAULT_PARQUET 0.067× vs default-Parquet 1.00×
RECEIPT_MANIFEST_BINDING TRUE C2PA-style hash reference; receipt bytes unchanged

Source: docs/transparency/phase9_4_1_1_2_1_candidate_b_video_llm_object_memory/summary.json and docs/transparency/phase09_4_1_1_2_2_receipt_core_provenance_benchmark/summary.json

Repo Identity

Field Value
Identifier ZPE-Video
Repository https://github.com/Zer0pa/ZPE-Video
Section encoding
Visibility PRIVATE
Architecture VIDEO_RECEIPT_STREAM
Encoding VIDEO_PERCEPTION_RECEIPT_V1
Commit SHA 07187909
License SAL-7.1
Authority Source docs/WEDGE.md

Readiness

Field Value
Verdict STAGED
Checks 12/12
Anchors 6 display anchors
Commit 07187909
Authority docs/WEDGE.md

Honest Blocker

Repo PRIVATE during initial crafting; full proof anchors will surface on visibility flip. Phase 1 perception-receipts schema is the active surface.

What We Prove

  • Bit-exact cross-writer output under default settings — pinned by tests/test_receipt.py::test_cross_writer_independent_implementation_matches, which hand-rolls an independent from-spec encoder and asserts byte-identical output.
  • Per-frame CRC32 integrity on every decode; any corruption raises ReceiptCorrupted — pinned by tests/test_receipt.py::test_crc_mismatch_raises.
  • Deterministic hash: receipt_hash(blob) = SHA-256 of the encoded bytes, stable across conforming writers — pinned by tests/test_receipt.py::test_receipt_hash_is_stable.
  • Round-trip fidelity: decode_receipt(encode_receipt(r)) recovers the same frame content up to canonical sort order — pinned by tests/test_receipt.py::test_round_trip_small.
  • Zero runtime dependencies for the core zpe_video.receipt module — confirmed by installing the built wheel into a clean venv with no extras and running the import surface check (.github/workflows/verify-package.yml).
  • Full wire-format specification in docs/WIRE_FORMAT.md sufficient for third-party re-implementation.
  • Full falsification history preserved alongside the defended wedge — every kill verdict is recorded in docs/TRANSPARENCY_JOURNEY.md and docs/transparency/.

What We Don't Claim

  • We do not claim universal video-codec superiority. Falsified at Phase 08 against AV1, VVC, and learned baselines on both detector families; record preserved.
  • We do not claim the archive-query-on-box-state surface is a ZPE-specific wedge. Falsified at Phase 09.4.1.1.2 against a 60-line raw-struct+zlib baseline; raw-struct+zlib strictly dominates ZPE on both storage and query latency for that workload.
  • We do not claim the ROI/foveated-sidecar produces a packet-specific lift. Killed at Phase 09.4.1.1.2.1-A when a spatially-uninformed mean-importance control lane produced the identical +7.93% matched-bitrate mAP@50 lift.
  • We do not claim Parquet cannot be configured to match. The wedge is specifically about default settings; Parquet tuned with enforced encoding and sorting can close the gap.
  • We do not claim buyer-visible end-to-end latency win in video-LLM pipelines. LLM generation dominates ~97% of end-to-end time; the receipt's speed advantage is a storage/memory property.
  • We do not claim the Compass-8 / 8-primitive directional-encoding architecture. This codec does not use it. The Compass-8 substrate is a research thesis that was tested in pre-0.1.0 phases and did not produce a defended product closure; the v0.1.0 product is the perception receipt, which stands on its own.

Verification Status

Code Check Verdict
V_01 Round-trip fidelity (small / empty / single-empty-frame) PASS
V_02 Same input yields byte-identical bytes PASS
V_03 Reordered input yields byte-identical bytes PASS
V_04 Independent from-spec encoder matches byte-for-byte PASS
V_05 receipt_hash stable across encodings PASS
V_06 CRC32 mismatch raises ReceiptCorrupted PASS
V_07 Bad magic / unsupported version / truncation / trailing bytes rejected PASS
V_08 verify_receipt hash + peer-blob match PASS
V_09 File write + read round-trip PASS
V_10 Frame-count mismatch / 255-box cap / signed-16 delta overflow raise PASS
V_11 Seed changes bytes but not decoded content PASS
V_12 Legacy codec smoke (tests/test_codec.py) PASS

Proof Anchors

Path State
docs/WEDGE.md VERIFIED
docs/WIRE_FORMAT.md VERIFIED
docs/TRANSPARENCY_JOURNEY.md VERIFIED
docs/QUICKSTART.md VERIFIED
docs/ARCHITECTURE.md VERIFIED
docs/STATUS.md VERIFIED

Repo Shape

Field Value
Proof Anchors 6 display anchors
Modality Lanes 1
Architecture VIDEO_RECEIPT_STREAM
Encoding VIDEO_PERCEPTION_RECEIPT_V1
Verification 12/12 checks
Authority Source docs/WEDGE.md

Competitive Benchmarks

Format Cross-Writer Hash (default) Per-Frame CRC32 Storage / video Notes
ZPE-Video TRUE (byte-identical) YES ~1.1 KB Zero runtime deps, schema-enforced
Parquet (pyarrow, defaults) FALSE NO ~5.4 KB Diverges vs fastparquet on same input
Parquet (fastparquet, defaults) FALSE NO ~5.0 KB Diverges vs pyarrow on same input
JSON + gzip Stable if key-sorted NO ~2.4 KB No framing, larger, tool-dependent
raw struct + zlib Stable NO ~0.3 KB No CRC, no schema, no versioning

Source: docs/transparency/phase9_4_1_1_2_1_candidate_b_video_llm_object_memory/cross_writer_hashes.json and docs/transparency/phase9_4_1_1_2_fair_baseline/summary.json

Raw struct + zlib is smaller in raw bytes but has no per-frame CRC32, no schema, no versioning — unsuitable as a receipt. ZPE is the smallest format that is also cross-writer stable under defaults AND carries integrity.

Quick Start

git clone https://github.com/Zer0pa/ZPE-Video.git
cd ZPE-Video
python3.11 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip uv
uv sync --extra dev
uv run pytest tests -v
uv run python examples/02_cross_writer.py   # expected: "cross-writer wedge: VERIFIED"

Expected: 29/29 tests pass; the cross-writer example prints cross-writer wedge: VERIFIED with byte-identical output from the library and an independent from-spec encoder.


Ecosystem

ZPE Video is one lane in a broader family of ZPE repositories. Each lane follows the same evidence-first discipline: narrow wedges, preserved kill verdicts, cross-writer or cross-implementation determinism where it's the load-bearing product property.

  • ZPE-Neuro — neural-recording spike-sorting and breadth adjudication.
  • ZPE-Prosody — speech prosody and rhythm analysis.

Common conventions: stdlib-heavy cores, deterministic binary artifacts, explicit non-claims, and falsification ledgers shipped alongside defended wedges.

Who This Is For

  • Platform teams building AI perception pipelines who need an auditable "what was seen" record that survives platform migration without re-running the detector.
  • Regulated video workflows (police body-worn cameras, insurance fraud, legal discovery) that need to disclose track state without disclosing pixels.
  • C2PA implementers who want a tiny, stable, binary artifact to reference by hash from a Content Credentials manifest.
  • Video-LLM / VideoRAG infrastructure needing integrity-guaranteed, cross-platform object-memory caches.
  • ML teams redistributing detection output as training data across languages and toolchains.

Not for teams that need full-fidelity video compression, perceptual video quality, or a VMS replacement. See docs/WEDGE.md for the full buyer-shape list and non-claims.

Upcoming Workstreams

Active lane priorities under the 0.1.0 perception-receipt wedge. Cadence is continuous, not milestoned. Open items, falsification routes, and decision criteria live in docs/_reorientation/2026-04-17/OPEN_QUESTIONS.md and the synthesis docs under docs/transparency/research_ledger/.


About

Perception receipts for AI video pipelines. Cross-writer bit-exact under default settings (SHA-256 stable across writers in any language). Zero runtime dependencies; pure stdlib core. ~1.1 KB per video; per-frame CRC32 + schema + versioning. Useful now, improving continuously.

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