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29th Meeting: by teleconference, DE, January 2023 2023-01-11 17:20
JVET AHG report: Neural network-based video coding (AHG11)
Authors: Elena Alshina Shan Liu Andrew Segall Franck Galpin Jingxin Li Tong Shao H. Wang Zhikui Wang Mathias Wien Ping Wu Jens-Rainer Ohm
Abstract
This document summarizes the activities of AHG11: Neural network-based video coding between the 28th meeting (20 – 28 October 2022) held in Mainz, DE, and the 29th meeting (11 – 20 January 2023) held by teleconference.
Meeting Notes
JVET-AC0011 JVET AHG report: Neural network-based video coding (AHG11) [E. Alshina, S. Liu, A. Segall (co-chairs), F. Galpin, J. Li, T. Shao, H. Wang, Z. Wang, M. Wien, P. Wu (vice-chairs)]

Common Test Conditions

The AHG released revised common test conditions as decided at the 28th meeting. The final version was uploaded as document JVET-AB2016 on December 24, 2022.

Anchors for the NN-based video coding activity were made available on the Git repository used for the AHG activity: https://vcgit.hhi.fraunhofer.de/jvet-ahg-nnvc/nnvc-ctc/-/tree/master.

EE Coordination

The AHG finalized, conducted and discussed the EE on NN based video coding. The final version of the EE description was uploaded to the document repository on November 14, 2022.

A summary report for the EE is available at this meeting as:

ARCHIVE :
._JVET-AC0011-v3.docx JVET-AC0011-v3.docx
Decisions
The AHG recommended to: Review all input contributions. Continue investigating neural network-based video coding tools, including coding performance and complexity.
Citation