Summary of S4-260200: Inclusion of NNC to AIML_IMS-MED

1. Introduction and Context

This contribution proposes the addition of Neural Network Coding (NNC) compression capabilities to the AIML_IMS-MED work item. The proposal is motivated by S4-260198, which demonstrates the necessity for compression of AI/ML data in IMS-based transport scenarios. The document presents changes to be incorporated into the common base Change Request for AIML_IMS-MED.

2. Main Technical Contributions

2.1 NNC Decoder Support Requirement

The proposal mandates that DCMTSI clients supporting AI/ML model download or incremental model download shall support NNC decoding as specified in ISO/IEC 15938-17. Specifically:

• NNC Edition 2 support is enabled by setting the general_profile_idc syntax element equal to 1
• This establishes a baseline compression capability for AI/ML model transport over IMS

2.2 Configuration for Full AI/ML Model Download

For DCMTSI clients supporting complete AI/ML model download, the following NNC parameter configuration is specified:

• Payload type: nnr_compressed_data_unit_payload_type = NNC_PT_BLOCK
• Compressed parameter types: compressed_parameter_types = NNR_CPT_LS | NNR_CPT_BN (enabling local scaling and batch normalization)
• Quantization options: Either dq_flag = 1 (dependent quantization) OR codebook_present_flag = 1 (codebook-based quantization)
• Probability estimation: shift_idx_minus_1_present_flag = 1 (optimal initialization)

Functionality enabled: This configuration supports local scaling adaptation, batch norm folding, flexible quantization approaches, and optimized probability estimation for entropy coding.

2.3 Configuration for Incremental AI/ML Model Data Exchange

For DCMTSI clients supporting incremental model updates, an extended parameter set is defined:

• Basic parameters: Same payload type (NNC_PT_BLOCK) and compressed parameter types (NNR_CPT_LS | NNR_CPT_BN) as full model download
• Update tree support: mps_parent_signalling_enabled_flag = 1 and parent_node_id_present_flag = 1
• Efficiency features:
• row_skip_enabled_flag = 1 (row skipping)
• nnr_pre_flag = 1 (predictive residual coding)
• hist_dep_sig_prob_enabled_flag = 1 (history-dependent significance probability)
• temporal_context_modeling_flag = 1 (temporal context adaptation)
• scan_order > 0 (parallel decoding support)

Functionality enabled: This configuration provides comprehensive support for efficient incremental updates through parameter update trees, spatial/temporal prediction, adaptive probability modeling, and parallel processing capabilities.

2.4 Normative Reference Addition

The proposal adds ISO/IEC 15938-17:2024 Edition 2 as a normative reference, establishing the technical foundation for NNC compression in the specification.

Technical Significance

The contribution establishes two distinct NNC profiles optimized for different AI/ML model transport scenarios in IMS networks:
1. A baseline profile for complete model downloads with essential compression features
2. An advanced profile for incremental updates with sophisticated prediction and adaptation mechanisms to minimize update payload sizes