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)