Overview
ISO/IEC TR 23888-3:2026 is an international technical report developed by ISO and IEC that addresses the optimization of video encoders and receiving systems for machine analysis of coded video content. As machine learning and artificial intelligence (AI) methods become critical in multimedia applications, it is essential to optimize video pipelines not just for human viewing, but also for automated machine analysis. This document delivers a concept-level overview of recent practices and highlights important technical aspects and cautions to consider when evaluating encoder optimizations for video content consumed by AI systems. Technologies described in this standard have demonstrated benefits to coding efficiency for select machine analysis tasks.
Key Topics
- Video Pipeline Optimization: The report discusses end-to-end systems for processing coded video content, encompassing pre-processing, encoding, and post-processing optimizations targeted at machine analysis tasks.
- Evaluation Methodologies: It foregrounds recommended metrics like bit rate, peak signal-to-noise ratio (PSNR), mean average precision (mAP), multiple object tracking accuracy (MOTA), and Bjøntegaard delta rate (BD-rate) to benchmark optimizations for machine consumption.
- Pre-processing Techniques: Methods such as region of interest (RoI) detection, foreground/background differentiation, temporal and spatial subsampling, and noise filtering are covered for improving encoder input for AI analysis.
- Encoding Enhancements: Adaptive encoding strategies, including RoI-based quantization parameter adaptation and adjustments for temporal layers, are emphasized to balance efficiency with analysis accuracy.
- Post-processing and Metadata: Efficient post-processing methods and the use of metadata to support downstream machine analysis are detailed, including the use of supplemental enhancement information (SEI) messages relevant to machine vision applications.
Applications
The optimizations detailed in ISO/IEC TR 23888-3:2026 are relevant across several high-impact sectors:
- Surveillance Systems: Large-scale sensor networks benefit from encoding optimizations that reduce bandwidth while enabling reliable real-time object detection and tracking by AI algorithms.
- Intelligent Transportation: Connected vehicles and smart infrastructure rely on efficient coded video content for machine-based interpretation, facilitating interoperability and reduced network load.
- Industrial Automation: Automated inspection and visual analysis in manufacturing environments are streamlined using video coding tailored for machine consumption, enhancing throughput and reliability.
- General AI Multimedia Analysis: Machine learning pipelines that process video data for object detection, segmentation, or tracking can leverage these optimizations to accelerate workflows and reduce resource consumption.
Related Standards
ISO/IEC TR 23888-3:2026 is part of a broader standards ecosystem. Key related documents include:
- ISO/IEC 23090-3 (VVC) / ITU-T H.266: The Versatile Video Coding standard, relevant for the baseline decoding of video bitstreams.
- ISO/IEC 23008-2 (HEVC) / ITU-T H.265: High Efficiency Video Coding, widely adopted in modern streaming and broadcasting.
- ISO/IEC 14496-10 (AVC) / ITU-T H.264: Advanced Video Coding, foundational in digital video.
- ISO/IEC 23002-7 / ITU-T H.274: Specifies supplemental enhancement information (SEI) messages critical for improved machine analysis of coded bitstreams.
- ISO/IEC TR 23888-1: Offers more extensive guidance on use cases and machine-centered video analysis within this standards series.
Practical Value
Implementing the guidelines and technologies described in ISO/IEC TR 23888-3:2026 helps organizations:
- Maximize video coding efficiency for machine-oriented workflows;
- Enable reliable and accurate AI analysis of video streams in diverse deployment scenarios;
- Comply with evolving international standards in AI for multimedia content;
- Stay current with best practices, boosting system performance while managing computational and network costs.
By following this report, stakeholders in AI video analysis, intelligent transportation, surveillance, and industrial automation can optimize their systems for future-ready, machine-driven operations.