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pith:YJCNZ7CT

pith:2025:YJCNZ7CTLVHNWRFVSLYTGLEH5U

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New VVC profiles targeting Feature Coding for Machines

Ashan Perera, Hari Kalva, Juan Merlos, Md Eimran Hossain Eimon, Velibor Adzic

Three simplified VVC profiles deliver up to 95% faster encoding of neural network features for machines with minimal rate penalty.

arxiv:2512.08227 v1 · 2025-12-09 · cs.CV

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Claims

C1strongest claim

We propose three lightweight essential VVC profiles-Fast, Faster, and Fastest. The Fast profile provides 2.96% BD-Rate gain while reducing encoding time by 21.8%. Faster achieves a 1.85% BD-Rate gain with a 51.5% speedup. Fastest reduces encoding time by 95.6% with only a 1.71% loss in BD-Rate.

C2weakest assumption

That the tool-level impact observed on the tested features and tasks will hold for the full range of FCM use cases and that the proposed profile simplifications do not introduce unmeasured accuracy drops on unseen models or datasets.

C3one line summary

Three lightweight VVC profiles for feature coding achieve up to 2.96% BD-Rate gain and 95.6% encoding speedup while preserving downstream task accuracy under the MPEG-AI FCM framework.

References

18 extracted · 18 resolved · 3 Pith anchors

[1] New VVC profiles targeting Feature Coding for Machines 2025 · arXiv:2512.08227

[2] FEATURE CODING FOR MACHINES Fig. 2 outlines the encoding and decoding process for in- termediate features computed by a neural network parti- tioned into NN Part-1 and NN Part-2.Xrepresents the set of

[3] LOW-COMPLEXITY VVC PROFILES We evaluate VVC/H.266 for compressing intermediate fea- tures extracted from neural networks, with a focus on iden- tifying a lightweight yet effective tool-set for Feature

[4] CONCLUSION We present a comprehensive analysis of VVC coding tools for compressing intermediate features in split-inference sys- tems. By profiling encoder decisions and conducting targeted ablation s

[5] Deep feature com- pression for collaborative object detection 2018

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New VVC profiles targeting Feature Coding for Machines

Receipt and verification

First computed	2026-07-15T00:21:15.233972Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

c244dcfc535d4edb44b592f1332c87ed28ff274abd3112d3a8775f67e1ac1648

Aliases

arxiv: 2512.08227 · arxiv_version: 2512.08227v1 · doi: 10.48550/arxiv.2512.08227 · pith_short_12: YJCNZ7CTLVHN · pith_short_16: YJCNZ7CTLVHNWRFV · pith_short_8: YJCNZ7CT

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/YJCNZ7CTLVHNWRFVSLYTGLEH5U \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: c244dcfc535d4edb44b592f1332c87ed28ff274abd3112d3a8775f67e1ac1648

Canonical record JSON

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    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2025-12-09T04:13:07Z",
    "title_canon_sha256": "ccfc3ff335ad8bf383f1c58f490861311398826ecdc4a9522f7ac6702be7e2c4"
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