Modern Multimedia Coding Standards: Crucial Tools for Scalable, Secure, and Efficient Digital Content

In today’s digital world, where immersive media, artificial intelligence (AI), and data-driven automation drive transformation, the standards that govern how audio, video, multimedia, and hypermedia information is coded are more essential than ever. Businesses, content creators, and technology providers rely on these frameworks not just for interoperability, but to ensure secure, efficient, and scalable deployment of their solutions. This guide explores four cutting-edge international standards shaping the realm of information technology media coding—including high-efficiency image formats, next-generation volumetric video, AI integration for multimedia, and machine-friendly video optimization. Whether you’re building cloud-based streaming platforms, embracing metaverse experiences, or automating AI-driven content workflows, adopting these standards can elevate productivity, future-proof operations, and strengthen your security posture.

Overview / Introduction

The rapid evolution of information technology is redefining how we capture, process, distribute, and consume digital media. Audio, video, and hypermedia are at the core of online collaboration, entertainment, industrial automation, and business intelligence. Yet the exponential growth of rich media—especially as we transition to immersive applications such as augmented/virtual reality (AR/VR) and AI-driven content analysis—brings unique technical and business challenges.

Standardization plays a vital role here. International standards ensure that different hardware, software, and platforms can communicate and process media seamlessly. Modern standards deliver:

• Interoperability across vendors and ecosystems

• Higher coding efficiency for reduced storage and bandwidth

• Enhanced security and data integrity

• Streamlined integration of AI and machine learning tools

• Simpler scale-up for massive or distributed deployments

This article breaks down four key international standards in multimedia coding and technology, showing their recommendations, who needs them, key requirements, and why they’re must-haves for forward-thinking organizations.

Detailed Standards Coverage

ISO/IEC 23008-12:2025/Amd 2:2026 – Low-overhead Image File Format

Information technology — High efficiency coding and media delivery in heterogeneous environments — Part 12: Image File Format — Amendment 2: Low-overhead image file format

This standard amendment introduces the Low-overhead Image File Format (LOIFF), an evolution aimed at applications requiring efficient storage and transmission of small or simple image files. By minimizing header and metadata overhead, LOIFF optimizes the storage footprint and speeds up the decoding and rendering process, making it highly suitable for scenarios ranging from real-time web graphics to embedded IoT systems.

What This Standard Covers

• Defines LOIFF as a streamlined alternative to traditional image file formats, reducing the file size and accelerating performance for small images.

• Specifies new file structures for condensed metadata.

• Standardizes parameters for tiled image items, enabling direct access and independent decoding of image tiles.

• Details pixel component arrangements, subsampling, and rich support for YUV and hyperspectral data.

Key Requirements and Specifications

• Use of the MinimizedImageBox for compact metadata and fast expansion into standard structures

• Detailed handling of tiled images (tili type), allowing efficient storage and random access for very large or multi-dimensional images

• Explicit support for color, alpha, and depth channels, as well as chroma subsampling and channel labelling

• MIME type registration for LOIFF, supporting interoperability in web and cloud environments

Who Needs to Comply

• Web platforms and mobile applications handling small, simple images

• Internet of Things (IoT) device ecosystems with limited resources

• Image processing libraries and content management solutions requiring efficient storage and transmission

• Organizations developing hyperspectral or multi-band imagery (e.g., satellite imaging, medical imaging)

Practical Implications for Implementation

Adopting LOIFF enables:

• Faster image loading and lower memory usage in bandwidth-constrained applications

• Easier management and scalable delivery of massive or multi-dimensional image datasets

• Direct access to individual image tiles, supporting efficient streaming and remote sensing operations

Notable Features / Requirements

• Dramatically reduced file header and metadata overhead

• Support for independently accessible tiled image regions

• Enhanced capabilities for storing color/alpha/depth channels and component metadata

Access the full standard: View ISO/IEC 23008-12:2025/Amd 2:2026 on iTeh Standards

ISO/IEC 23090-5:2026 – Visual Volumetric Video-Based Coding (V3C) and Video-Based Point Cloud Compression (V-PCC)

Information technology — Coded representation of immersive media — Part 5: Visual volumetric video-based coding (V3C) and video-based point cloud compression (V-PCC)

Immersive experiences—especially in virtual reality, gaming, and simulation—require coding, transmitting, and rendering vast amounts of 3D data. ISO/IEC 23090-5 sets out the frameworks for visual volumetric video-based coding (V3C) and video-based point cloud compression (V-PCC), efficiently handling enormous volumetric datasets and making interactive, real-time 3D content a reality.

What This Standard Covers

• Specifies how 3D volumetric data (such as point clouds, meshes, and immersive video) can be converted to video sequences in order to leverage established video coding infrastructure (e.g., HEVC, VVC)

• Complete definitions of bitstream structure, syntax, semantics, and decoding processes for immersive media

• Guidelines for post-decoding and adaptation steps to reconstruct 3D experiences from video-coded data

Key Requirements and Specifications

• Defines generic and extensible methods for coding visual volumetric video into 2D images plus additional atlas data

• Supports diverse media (point clouds, immersive video with depth, dynamic mesh)

• Specifies decoding and reconstruction operations, ensuring interoperability across content creators and playback platforms

Who Needs to Comply

• Developers and service providers working on VR/AR/MR content

• Providers streaming and storing large-scale 3D datasets (e.g., engineering, urban mapping, health diagnostics)

• Technology vendors in the metaverse and immersive entertainment sectors

• Systems integrators for industrial automation, smart cities, or education platforms requiring advanced geometry data

Practical Implications for Implementation

Implementing this standard results in:

• Major reductions in required storage and transmission bandwidth for immersive 3D content

• Interoperable pipelines, enabling content creation, editing, distribution, and playback between different vendors

• Flexible adaptation to new use cases as volumetric media expands

Notable Features / Requirements

• Compression of huge volumetric sequences to manageable sizes using mature video codecs

• Bitstream organization for efficient parsing, streaming, and memory management

• Adaptation hooks for rendering, post-processing, and downstream analytics

Access the full standard: View ISO/IEC 23090-5:2026 on iTeh Standards

ISO/IEC TR 23888-1:2026 – Artificial Intelligence for Multimedia: Vision and Scenarios

Information technology — Artificial intelligence for multimedia — Part 1: Vision and scenarios

The intersection of artificial intelligence and multimedia is rapidly reshaping expectations and architectural approaches across industries. This technical report acts as a foundational reference, outlining current and future uses of AI—including neural networks—in multimedia coding, content representation, and analysis.

What This Standard Covers

• Lays out the role of neural networks and advanced AI tools in multimedia coding and processing

• Provides a comprehensive overview of current working assumptions, challenges, and gaps in AI-enabled multimedia

• Describes technical scenarios, interoperability challenges, and ethical considerations for future standardization

Key Requirements and Specifications

• Identification and use of standardized neural network representations (e.g., ONNX, NNEF) for exchanging and deploying AI tools

• Scenarios for AI as both a coding tool (for compression, feature extraction, coding optimization) and as a consumer of coded content

• Frameworks for measuring complexity, efficiency, reproducibility, and integration with other coding standards

Who Needs to Comply

• AI researchers and solution architects integrating machine learning and computer vision into multimedia workflows

• Multimedia software vendors advancing toward AI-driven asset management, search, and production

• Standardization teams anticipating AI’s increasing relevance to the media landscape

• Operators planning for ethically robust, future-proof AI deployment

Practical Implications for Implementation

Implementing the perspectives and recommendations from TR 23888-1:

• Clarifies how best to incorporate AI in developing media tools and applications

• Helps define evaluation metrics for new AI-powered media processes

• Identifies interoperability requirements, ensuring seamless collaboration between conventional and AI-based components

Notable Features / Requirements

• Strategic vision for AI in multimedia, balancing efficiency and computational requirements

• In-depth explanation of neural network integration and emerging media representation methods

• Use cases for AI-based 3D coding, video analysis, model compression, and more

Access the full standard: View ISO/IEC TR 23888-1:2026 on iTeh Standards

ISO/IEC TR 23888-3:2026 – Optimization of Encoders and Receiving Systems for Machine Analysis of Coded Video Content

Information technology — Artificial intelligence for multimedia — Part 3: Optimization of encoders and receiving systems for machine analysis of coded video content

As more video data is being consumed and analyzed by machines (rather than— or in addition to—humans), encoding and processing strategies must adapt to maximize both computing performance and analytical accuracy. This technical report reviews recent optimization technologies for video encoders and receivers that impact machine-based analysis, such as object detection and segmentation in surveillance or industrial environments.

What This Standard Covers

• Surveys optimizations for encoding, pre-processing, and post-processing of video targeted at machine analysis tasks

• Details metrics and evaluation methodologies for coding efficiency (e.g., mAP, MOTA, BD-rate)

• Outlines metadata structures and enhancement messages that boost analytic fidelity while maintaining efficient data handling

Key Requirements and Specifications

• Specification of pre-processing strategies like region-of-interest (RoI) extraction, temporal and spatial subsampling, foreground/background processing, and noise filtering

• Encoding optimizations such as adaptive quantization and chroma QP adjustments tailored to machine-consumed video

• Use of post-processing steps (e.g., resampling, enhancement) that help AI algorithms perform better on compressed video

• Metadata embedding for model-aware analysis, object masks, and annotated regions

Who Needs to Comply

• Developers of AI video analytics (e.g., security, smart cities, intelligent transport, industrial monitoring)

• Video codec, encoder, and streaming technology vendors seeking to support both real-time human viewing and automated system processing

• Cloud platforms and edge devices involved in video-based automation or surveillance workflows

Practical Implications for Implementation

• Increased accuracy of AI systems on compressed, distributed, or low-resource video data

• Lower compute and bandwidth costs by optimizing what and how to encode/analyze

• Flexible adoption of multiple optimization technologies depending on use case and hardware

Notable Features / Requirements

• Comprehensive review of pre-, in-, and post-processing techniques for machine-friendly video

• Defined evaluation metrics supporting both human and machine quality assessment (e.g., mAP, PSNR, MOTA)

• Broad applicability across surveillance, intelligent industry, and transportation domains

Access the full standard: View ISO/IEC TR 23888-3:2026 on iTeh Standards

Industry Impact & Compliance

Modern organizations are operating in a world where digital media and data analysis are core business assets. Adopting and maintaining compliance with internationally recognized coding standards ensures:

• Interoperability and Compatibility: Facilitates seamless integration between diverse platforms, devices, and content providers worldwide, avoiding costly vendor lock-in or integration issues.

• Enhanced Productivity: Streamlined media workflows, optimized file formats, and AI-integrated processes save time for development, operations, and analytics teams.

• Security and Trust: Adhering to standardized formats and processing guidelines reduces the risk of vulnerabilities, enables better auditing, and fosters confidence among partners and customers.

• Scalability and Future-Proofing: Standards built for extensibility and efficiency make it easier to scale services as data volumes grow and to pivot to emerging immersive, AI, or data-driven applications.

• Market Access and Competitiveness: Many procurement processes, especially in regulated sectors, require conformance to international standards, so non-compliance can mean lost contracts.

Non-compliance, on the other hand, can lead to incompatible systems, higher development costs, analytical inaccuracies, or potential data insecurity.

Implementation Guidance

While each standard and technical report has detailed clauses with requirements, businesses seeking to align with these guidelines should consider the following best practices:

1. Assess Organizational Needs

• Conduct a gap analysis: What media types are processed? Are there AI-based analysis or immersive content pipelines?

• Identify critical workflows (e.g., streaming, archival, automated analysis).

2. Choose Standards and Integration Points

• Select image and video coding standards (e.g., LOIFF, V3C, V-PCC) appropriate for your content scale and type.

• For AI-focused implementations, align pipelines with recommendations in TR 23888-1 and TR 23888-3.

3. Ensure Technical Readiness

• Update infrastructure components (encoders, media storage, streaming services) to support new formats and metadata as required.

• Integrate reference implementations or certified libraries where available.

4. Train Staff and Stakeholders

• Train technical staff, operators, and managers on new features, workflows, and compliance requirements stemming from these standards.

• Document processes to ensure ongoing compliance and adaptability as specifications evolve.

5. Monitor and Optimize

• Use recommended evaluation metrics (e.g., PSNR, mAP, MOTA, BD-rate) to validate the performance of encoded video for both human viewers and AI consumers.

• Engage with international standards communities to anticipate updates and best practices.

6. Utilize Available Resources

• Access documentation, community forums, and support through platforms like iTeh Standards.

• Leverage sample files and open implementations (where available) for prototyping and testing.

Conclusion / Next Steps

The digital era demands agile, robust, and future-ready strategies for handling multimedia and hypermedia information. By adopting leading standards like ISO/IEC 23008-12’s LOIFF, ISO/IEC 23090-5’s volumetric and point cloud video coding, and the AI-guided frameworks showcased in ISO/IEC TR 23888-1 and TR 23888-3, organizations empower themselves to:

• Enhance their technical foundation for immersive, scalable, and intelligent media workflows

• Support greater IT security, interoperability, and operational efficiency

• Scale quickly across geographies and platforms while remaining compliant with global requirements

For businesses pushing the boundaries of digital innovation—whether streaming large-scale 3D content, analyzing video with AI, or deploying efficient image formats for billions of devices—these standards are not just technical tools; they are enablers of competitive advantage, faster time-to-market, and sustainable growth.

Ready to get started?

• Review the full specifications linked above

• Audit your current media and AI pipelines for compliance

• Engage with your technical teams to design your roadmap for scalable, secure, and efficient multimedia information handling

Explore all the latest standards, updates, and implementation resources today at iTeh Standards.