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41st Meeting: by teleconference, CH, January 2026 2026-02-10 18:43
Exploration experiment on neural network-based video coding (EE1)
Abstract
This document summarizes Exploration Experiment 1 (EE1) tests to be performed between the JVET-AO and JVET-AP meetings to evaluate Neural Network-based Video Coding (NNVC) technologies, analyze their performances and complexity aspects.
JVET-AO2023 Exploration experiment on neural network-based video coding (EE1) [E. Alshina, R. Chang, F. Galpin, Yue Li, Yun Li, M. Santamaria, T. Shao, J. Ström, Z. Xie (EE coordinators)] (2026-02-06)

An initial draft of this document was reviewed and approved at 1310-1335 on Friday 23 January.

This round of EE1 tests includes:

  • EE1-1: LOP in-loop filter
    • EE1-1.1 – Dynamic convolution for LOP7 neural in-loop filtering JVET-AO0055 (KHU, KBS).
    • EE1-1.2 – Combination test of EE1-1.1 and Backbone Block Enhancement of LOP In-Loop Filter with Over-Parameterized Training and Variable Channels JVET-AO0074 (Xidian Univ., UESTC, Transsion)
    • EE1-1.3 – AHG11: Enhancing LOP7 with Re-Exploited Boundary Strength Guidance JVET-AO0144 (HUST)
  • EE1-2: VLOP in-loop filter
    • EE1-2.1 – VLOP with new backbone block based on Spatial-Channel Mixing JVET-AO0129
  • EE1-3 NN-Inter
    • EE1-3.1 – Very Small Deep Reference Frame Generation Network for Inter Prediction Enhancement JVET-AO0267
  • EE1-4: Framework for externally coded pictures
    • EE1-4.1 - Multi-layer framework (as it is in NNVC-16, with residual coding)
    • EE1-4.2 - Multi-layer framework with frame level control (full replacement of VVC I-frame with E2E AI coded picture depending on picture level flag)
    • EE1-4.3 - Single-layer framework as proposed in JVET-AO0173 with different E2E AI codec
    • EE1-4.4 - Single-layer framework as proposed in JVET-AO0173 with frame level control (full replacement of VVC I-frame with E2E AI coded picture depending on picture level flag)
  • EE1-5: operational bit-exact reproducibility
    • EE1-5.1 – [AHG11] Stable float convolution for neural network inference JVET-AO0178 (Nokia)

JVET-AO2023 Exploration experiment on neural network-based video coding (EE1) [E. Alshina, R. Chang, F. Galpin, Yue Li, Yun Li, M. Santamaria, T. Shao, J. Ström, Z. Xie (EE coordinators)] (2026-02-06)

An initial draft of this document was reviewed and approved at 1310-1335 on Friday 23 January.

This round of EE1 tests includes:

EE1-1: LOP in-loop filter

EE1-1.1 – Dynamic convolution for LOP7 neural in-loop filtering JVET-AO0055 (KHU, KBS).

EE1-1.2 – Combination test of EE1-1.1 and Backbone Block Enhancement of LOP In-Loop Filter with Over-Parameterized Training and Variable Channels JVET-AO0074 (Xidian Univ., UESTC, Transsion)

EE1-1.3 – AHG11: Enhancing LOP7 with Re-Exploited Boundary Strength Guidance JVET-AO0144 (HUST)

EE1-2: VLOP in-loop filter

EE1-2.1 – VLOP with new backbone block based on Spatial-Channel Mixing JVET-AO0129

EE1-3 NN-Inter

EE1-3.1 – Very Small Deep Reference Frame Generation Network for Inter Prediction Enhancement JVET-AO0267

EE1-4: Framework for externally coded pictures

EE1-4.1 - Multi-layer framework (as it is in NNVC-16, with residual coding)

EE1-4.2 - Multi-layer framework with frame level control (full replacement of VVC I-frame with E2E AI coded picture depending on picture level flag)

EE1-4.3 - Single-layer framework as proposed in JVET-AO0173 with different E2E AI codec

EE1-4.4 - Single-layer framework as proposed in JVET-AO0173 with frame level control (full replacement of VVC I-frame with E2E AI coded picture depending on picture level flag)

EE1-5: operational bit-exact reproducibility

EE1-5.1 – [AHG11] Stable float convolution for neural network inference JVET-AO0178 (Nokia)

Decisions
JVET-AO2023 Exploration experiment on neural network-based video coding (EE1)
EE1-5.1 – [AHG11] Stable float convolution for neural network inference JVET-AO0178 (Nokia)
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