ISO/IEC 15938-17:2022

Overview

ISO/IEC 15938-17:2022 defines a standardized approach to compressing neural network parameters used for multimedia content description and analysis. Part of the ISO/IEC 15938 family, this document specifies Neural Network Coding (NNC) as a compressed representation of trained network parameters/weights and the required decoding behaviour. It complements existing exchange formats by focusing on parameter compression, bitstream syntax/semantics and integration/signalling - not on full transmission protocols or internal decoder implementation details.

Key topics and technical requirements

• Neural Network Coding (NNC / NNR bitstream): Syntax and semantics for NNR units and aggregate bitstreams, including headers, payloads and byte alignment.
• Decoding process requirements: Externally observable decoding behaviour, supported decompressed data formats and method-specific decoding flows (integer, float, raw float, block types).
• Parameter reduction techniques: Sparsification (including micro-structured pruning), combined pruning/sparsification, parameter unification, low-rank and low-displacement-rank approaches for convolutions/fully connected layers, batchnorm folding, and local scaling adaptation.
• Parameter quantization methods: Uniform quantization, codebook-based quantization, and dependent scalar quantization - with associated syntax and semantics for signalling quantization metadata.
• Entropy coding and DeepCABAC: Entropy coding methods (including DeepCABAC) and the corresponding bitstream syntax, initialization, binarization and decoding processes.
• Integration with exchange formats: Guidance and normative/informative implementations for carrying NNR bitstreams with existing model exchange formats (Annex A: NNEF normative; Annexes B–D: ONNX, PyTorch, TensorFlow informative), and recommendations for container carriage.
• Scope limits: The standard intentionally excludes pre-/post-processing, system signalling, multiplexing, data loss recovery, and internal decoder processing steps - it mandates only the external bitstream syntax/semantics and decoder output behaviour.

Applications

• Reducing model size for on-device multimedia analysis (e.g., visual/audio metadata extraction)
• Efficient packaging of trained models for streaming, storage, and update distribution
• Standardized interchange of compressed models between toolchains and platforms
• Improving runtime efficiency and bandwidth use for content description services

Who should use this standard

• ML engineers optimizing models for multimedia tasks
• Codec and multimedia system developers integrating model delivery
• Device manufacturers and streaming platforms seeking interoperable compressed models
• Tool and library vendors implementing model export/import and compression pipelines

Related standards

• ISO/IEC 15938 (MPEG-7) family - multimedia content description interfaces
• Model exchange formats referenced in annexes: NNEF, ONNX, PyTorch, TensorFlow

Keywords: ISO/IEC 15938-17:2022, Neural Network Coding, NNC, NNR bitstream, neural network compression, model quantization, entropy coding, DeepCABAC, multimedia content description.