WAKEFIELD, Mass. July 30, 2026 — The Alliance for Open Media (AOMedia) today announced the publication of the Open Audio Renderer (OAR) v1, a major milestone in the development of open immersive audio technologies. Developed by AOMedia's Audio Codec Working Group, in collaboration with the Storage and Transport Formats Working Group, OAR v1 provides a reference implementation for rendering spatial audio across a wide range of playback environments.

As demand grows for immersive experiences across streaming, communications, gaming, extended reality (XR), automotive, and wearable devices, spatial audio has become an increasingly important part of modern media delivery. OAR provides developers with an open, interoperable foundation for bringing those experiences to users.

OAR complements AOMedia's Immersive Audio Model and Formats (IAMF) specification, which defines how immersive audio content is packaged and delivered. While IAMF standardizes the representation of spatial audio, it does not specify how that content should be rendered for individual playback systems. OAR fills that role by converting IAMF content into audio signals optimized for the listener's playback environment.

The renderer supports two primary modes:

• Loudspeaker rendering, adapting immersive audio for playback systems ranging from stereo speakers to surround sound and home theater configurations.

• Binaural rendering, producing immersive spatial audio over headphones by generating distinct signals for each ear.

OAR also supports channel-based, scene-based (Ambisonics), and object-based audio representations. In object-based audio, individual sounds are accompanied by metadata describing their position and movement within a three-dimensional space. OAR interprets this information during playback, creating an optimized listening experience regardless of the playback hardware.

"By providing a high-quality reference renderer for IAMF, we're making it easier for developers to build interoperable spatial audio experiences while giving implementers the flexibility to innovate," said Jan Skoglund and Remi Audfray, co-chairs of AOMedia's Audio Codec Working Group, in a joint statement.

Development of OAR drew on contributions from experts across the global technology community, including researchers, streaming platforms, semiconductor companies, device manufacturers, and audio specialists working collaboratively under AOMedia's open development model.

Professor Gavin Kearney of the University of York's AudioLab noted, "OAR represents a huge step forward for open, interoperable spatial audio rendering. Being able to contribute our work on immersive and interactive audio directly into a standard that the whole industry can build on is exactly the kind of real-world impact we strive for."

By publishing a reference implementation, AOMedia enables developers to integrate IAMF-compatible spatial audio into products without building a renderer from the ground up. Implementers remain free to develop proprietary rendering solutions while relying on OAR as a common baseline for interoperability, consistency, and quality.

OAR v1 is available now. To learn more about the Open Audio Renderer, the IAMF specification, and AOMedia's work on open media technologies, visit www.aomedia.org.

About AOMedia

Launched in 2015, the Alliance for Open Media (AOMedia) develops open standards for media — spanning video, audio, still images, and immersive technologies. AOMedia brings together 49 global innovators — including tech leaders with decades of media tech experience and some of the world’s largest patent holders — to support this mission. Its steering committee consists of Amazon, Apple, Cisco, Google, Intel, Meta, Microsoft, Mozilla, Netflix, NVIDIA, Samsung Electronics, and Tencent. Learn more at www.aomedia.org.

Media Contact Lillian Guinther
AOMedia Public Relations
aomediamarcom@virtualinc.com

To explore what this release means in practice and what lies ahead, we spoke with Andrey Norkin (Principal Architect, Video, NVIDIA) and Adrian Grange (Senior Technical Program Manager, Google), Co-Chairs of AOMedia’s Video Codec Working Group.

What does it mean that the AV2 specification is now “final”?

This initial release of the AV2 specification signifies that the coding tools are complete. It provides a stable foundation that implementers can use to immediately begin developing and finalizing their products.

While future versions are expected—including additional profiles and metadata to better support both existing and new use cases—the core coding tools will not change, ensuring long-term stability for developers.

What changed between earlier AV2 previews and this release?

The preview release was deliberately issued as a tool-complete version to give hardware implementers a head start on their design process before the final specification was locked in, thereby accelerating time to market. With those foundational designs underway, subsequent development focused on resolving open issues and refining the high-level syntax. This final release delivers the stable, comprehensive specification required to bring production-ready products across the finish line.

What does this enable for developers and implementers right now?

With the specification finalized, development can now advance across the entire ecosystem. This enables full-scale work on both decoders and encoders, encompassing both software and hardware implementations.

Software implementations typically represent the initial step, while hardware support—which is essential for widespread device adoption—follows. Reference software is already available, and efforts to develop optimized implementations are well underway.

AV2 delivers improved compression efficiency. What does that mean in practice?

Based on objective quality metrics, including VMAF and PSNR, AV2 delivers the same quality with approximately 30% better compression efficiency compared to AV1 across a wide range of content types and resolutions. Visual quality gains are expected to be even higher, reaching closer to 40% in bitrate savings. In practice, this means video providers can deliver the same visual quality using fewer bits, or significantly enhance quality while maintaining the same bitrate.

These improvements have direct benefits for both providers and viewers by reducing bandwidth requirements at scale and enhancing video quality for users, particularly in environments with limited connectivity.

The specification supports multi-stream and multi-view scenarios. How should we think about those capabilities?

AV2 enables multiple video streams to be combined into a single, synchronized bitstream, expanding how video content can be structured and delivered. This natively supports multi-view or composite video experiences where several related streams must be delivered together.

Furthermore, it facilitates advanced scenarios where the final video is composed from multiple components, such as texture plus depth, or background and foreground combined with an alpha channel.

Does AV2 introduce entirely new use cases, or improve existing ones?

Rather than introducing entirely new applications, AV2 primarily focuses on significantly improving existing ones. Enhancements in compression efficiency and stream flexibility directly translate to better performance across common use cases like streaming and video conferencing, positively impacting quality, responsiveness, and overall delivery efficiency.

That said, AV2 also natively supports more complex scenarios, such as multi-view video, which previously demanded complicated synchronization at the application level.

How will AV1 and AV2 coexist as adoption grows?

AV1 and AV2 are expected to coexist for a significant period. AV1 is already widely deployed across devices, and its adoption continues to expand. Meanwhile, AV2 adoption will grow gradually as implementations mature and hardware support becomes more broadly available, following the typical transition pattern observed in previous generations of video codecs.

What happens next following the release of the AV2 specification?

With the specification finalized, the focus shifts squarely to ecosystem implementation and adoption. This path involves the continued refinement of software implementations, followed by the development of broader hardware support. As these solutions mature, AV2 will increasingly be integrated into commercial products and services.

What Key New Features Does This Release Include?

Pinkie Pie boasts significant compression efficiency and perceptual quality improvements for layered image encoding. We delve into layered image encoding improvements in the next section.

Low complexity decode mode (the encoder optimizations that produce encoded bitstreams that can be decoded at low computational complexity) now supports good-quality encoding (speed 1 to 3) for not only vertical videos (608p to 1080p) but also horizontal videos (720p to 1080p). It achieves close to 30% decoding cost savings with a tightly constrained compression efficiency loss.

Encoder algorithms in partition/mode search, loop restoration, CDEF, temporal filter, and rate control were tuned or reworked in this release, achieving a massive encoder speedup alongside significant coding efficiency gains.

• Delivered ~40% encoder time reduction for speed 0 and 1.
• Delivered compression efficiency gains across presets: speeds 0 and 1 see a 1.5%+ SSIM gain, a ~0.2% PSNR gain, and a 7% VMAF gain, while speeds 2 and 3 yield a 2.6%+ SSIM gain, a 1.4 -1.7% PSNR gain, and a 7 - 8% VMAF improvement.

The improvements in this release provide substantial egress savings and computational cost reduction for YouTube.

Realtime (RTC) encoding continues to be an active area of development. This release extended encoding with lookahead (also known as lagged encoding) to RTC mode, added support for speculative encoding (encoding a frame without updating encoder state), and improved the quality and scroll detection for high-resolution screen content.

Since February we have been receiving increasingly high-quality reports of software security issues found by AI tools. These powerful AI tools are a great addition to the arsenal of security scanners and complement fuzzing, static analysis, and sanitizers. This release contains several fixes of security issues, such as integer overflows, excessive memory allocations, and memory errors, typically triggered by high bitrates or dynamic changes to SVC encoder settings. (See the release notes for details.) This critical work is ongoing and the next libaom release will include additional security fixes.

Layered Image Encoding Improvements in This Release

The AV1 Image File Format (AVIF) allows a still image to use multiple (up to four) AV1 spatial layers. Given a layered AVIF image, an application can either render only the highest spatial layer, or render all the intermediate layers and the highest spatial layer, resulting in a form of progressive decoding. A visual example of progressive decoding is provided at the end of this section.

The new releases of libavif (v1.4.2) and libaom (v3.14) contain new layered image encoding features and tweaks to address high and unpredictable file size overhead because older versions did not handle this use case properly. In libaom, these improvements mainly come from the following two changes:

• Extended the AOM_TUNE_IQ (Image Quality) tuning mode to work with inter-frame encoding modes (good-quality and realtime), improving the visual quality and compression efficiency of layered images compared with AOM_TUNE_SSIM. When encoding 2-layered images, AOM_TUNE IQ improves compression efficiency by up to 15% in good-quality mode and by up to 30% in realtime mode.
• Added a new encoder option (use_fixed_qp_offsets=2) to disable quality boosting of the key frame, allowing full control of each layer's quality level. In general, a high-quality key frame helps reduce error propagation, so it is advantageous to boost the quality of a key frame. In a layered image, however, the key frame (the base layer) can have a lower quality than the rest of the frames, so quality boosting is counter-productive.

The libavif v1.4.2 release can leverage these two improvements to achieve better quality at the same size, with less file size overhead. Additionally, libavif's 2-layer image encoding (avifenc --progressive) downscales the first layer by a half for further size savings, with a faster time to first render.

We illustrate the improvements with an example. The original image is a JPEG image of size 2,790,013 bytes. To showcase file size differences better, we strip the Exif and XMP metadata from the encoded AVIF images.

As a baseline, we first encode the image as a regular, non-layered image at speed 6 and quality level 60 (using the command line avifenc -s 6 -q 60 --ignore-exif --ignore-xmp photo.jpg photo.avif). The file size is 785,269 bytes.

We then encode the image as a layered image at speed 6 and quality level 60 (using the command line avifenc --progressive -s 6 -q 60 --ignore-exif --ignore-xmp photo.jpg photo.avif) with different versions of libavif.

• With libavif v1.3.0 and libaom v3.12.1, the file size is 1,542,974 bytes, nearly twice the size of the baseline!
• With libavif v1.4.1 and libaom v3.13.2, all intra encoding is used as an interim workaround, and the file size is 833,672 bytes. Compared with the baseline, the file size overhead is (833,672 - 785,269) / 785,269 = 6%.
• Finally, with libavif v1.4.2 and libaom v3.14.1, improved inter frame encoding is used, and the file size is 801,338 bytes. Compared with the baseline, the file size overhead is (801,338 - 785,269) / 785,269 = 2%.

In the final layered image of 801,338 bytes, the base layer (layer 0) is 26,061 bytes and the enhancement layer (layer 1) is 771,821 bytes. When an application decodes this image progressively, it can render the base layer immediately after receiving just 4% of the image (29,517 bytes), and then render the enhancement layer after receiving the whole image.

Where can the libaom v3.14 Pinkie Pie release be found?

As of this writing, the latest release in the libaom v3.14.x series is libaom v3.14.1. The source code of the release can be checked out from the git repository https://aomedia.googlesource.com/aom using the release tag v3.14.1. Alternatively, the release tarball, along with the signature file, can be downloaded from https://storage.googleapis.com/aom-releases/libaom-3.14.1.tar.gz and https://storage.googleapis.com/aom-releases/libaom-3.14.1.tar.gz.asc respectively.

Thank you to the Video Codec Working Group for their efforts, and a special thanks to the Arm team for their contributions to the AArch64 optimizations in this release.

Building on the success and broad adoption of the Emmy Award-winning AV1 codec, AV2 delivers significant advances in video compression efficiency and flexibility. This increased efficiency enables higher-quality visual experiences at the same bitrate or significant bandwidth savings for equivalent quality, making it ideal for a wide range of applications from traditional streaming to emerging media.

“AV2 was developed through a rigorous, collaborative process involving some of the world's leading technology companies,” said Adrian Grange and Andrey Norkin, Co-Chairs of AOMedia’s Video Codec Working Group. “Proposed tools were evaluated for implementation complexity and tested under common test conditions to ensure that not only is the specification high-performing but also well-optimized for efficient hardware implementation.”

Beyond improved efficiency gains, AV2 was designed to support a wide range of use cases, including traditional streaming workloads. The specification enables scalable bitstreams and supports multi-stream and multi-view scenarios, including stereoscopic video, multiple camera angles, and composite video experiences delivered within a single bitstream, as well as more traditional VOD and video conferencing workflows. These capabilities enable more efficient bandwidth usage, improving viewing experiences across a range of network conditions, and support advanced applications across streaming, video conferencing, and emerging media.

“The publication of the AV2 specification reflects years of collaborative and innovative work by the AOMedia community and provides a strong foundation for continued innovation in video delivery,” said Dr. Pierre-Anthony Lemieux, Executive Director of AOMedia. “This publication is a first essential step towards the broad adoption of AV2: efforts are already underway to integrate AV2 with popular streaming protocols and to define new profiles of AV2 targeting professional applications.”

Looking Ahead: The AV2 Ecosystem and Extensions

With the specification finalized, AOMedia is shifting focus toward ecosystem integration and future extensions:

• Container Formats: An AV2 binding specification for ISO BMFF is planned for release.
• Software and Tools: While libavm remains the reference software, the dav2d decoder project is making steady progress toward a high-performance software implementation.
• Conformance and Testing: Commercial conformance stream packages from Allegro and HDR Nova are already available. AOMedia is also planning to produce example test streams representative of real-world "in-the-wild" scenarios.
• Professional Profiles: The Video Codec Working Group has already initiated a project for a new 12-bit profile to support professional cinema workflows and high-dynamic-range (HDR) applications.

The AV2 specification and related technical resources are available to the public at av2.aomedia.org.

About the Alliance for Open Media

Launched in 2015, the Alliance for Open Media (AOMedia) was formed to define and develop technologies to address marketplace demand for open standards for media compression and delivery. Board-level, Founding Members include Amazon, Apple, Cisco, Google, Intel, Meta, Microsoft, Mozilla, Netflix, NVIDIA, Samsung Electronics and Tencent. AOMedia’s open-source, royalty-free video codec AV1 is a significant milestone in the ability to deliver a next-generation video format that is interoperable, open, optimized for internet delivery and scalable to any modern device at any bandwidth. Visit www.aomedia.org or follow AOMedia on LinkedIn.

This post has been updated to correct the list of Founding Members in the "About" section.

Press contact:
AOMedia
Lillian Guinther
Aomediamarcom@virtualinc.com

• Use by default of the new tune IQ (Image Quality) tuning mode of libaom. The tune IQ mode, which debuted in libaom v3.12.0 and matured in libaom v3.13.0, is a suite of encoder settings that aim to improve the perceptual quality of images. After a rigorous evaluation, tune IQ graduated and became the default tuning mode in libavif v1.4.0.
• Support of the Sample Transforms feature in AVIF specification v1.2.0, a feature that allows extending the bit depth of AVIF images to 16 bits and beyond. For backward compatibility, Sample Transforms are restricted to the alternate version of an image so that the base version of the image can still be decoded by old AVIF decoders. For example, a 16-bit AVIF image must contain a 12-bit or 8-bit base image.
• Numerous improvements to color space and gain map handling
• Key bug fixes accumulated over the past 10 months

The Tune IQ (Image Quality) Tuning Mode

Tune IQ (Image Quality) is a new tuning mode in libaom for encoding still images. Many of the ideas in tune IQ were incubated in the SVT-AV1-PSY fork of SVT-AV1. Tune IQ was developed in libaom from late 2024 through the first half of 2025, and has since been ported to SVT-AV1.

Tune IQ has four major goals

• Improve compression efficiency on specific image quality metrics (e.g., SSIMULACRA 2)
• Improve perceptual quality by humans
• Maintain more consistent quality within each image
• Encode images with quality consistently close to the specified target quality

Tune IQ achieves these goals by tuning the encoder's decisions in bit allocation and rate-distortion optimization, as well as making better use of underutilized AV1 coding tools to reach their full potential. In addition, tune IQ uses a more reliable method to detect screen content and determine whether screen content coding tools such as intra block copy would be profitable. During the development process we also profiled the performance and sped up bottlenecks in both the encoder and decoder.

Encouraged by the enthusiastic feedback of early adopters, the libavif team conducted several rounds of evaluation and subjective testing to guide further enhancement. After a year of continuous bug fixes and quality refinements, tune IQ became the default tuning mode for images in libavif v1.4.0 in March 2026.

Visual Comparison

The following is a great example of how tune IQ maintains more consistent quality within an image. Notice that tune IQ retains more detail around the body of water and the sky, while still retaining enough detail around the trees and scenery. The libavif team is really excited to share the perceptual quality improvements tune IQ achieves!

Original (3.4 MB)

Tune SSIM (261 KB)

Tune IQ (256 KB)

Screen Content Coding Example

The following is a great example of how tune IQ significantly improves on the previous default tuning mode, tune SSIM, for an image containing screen content. The new screen content detection algorithm in tune IQ allows us to leverage screen content coding tools for more content, improving the compression efficiency further.

Original (255 KB)

Tune SSIM (54 KB)

Tune IQ (51 KB)

The new release is available in github, where binary distributions for Linux, macOS, and Windows can be downloaded now: https://github.com/AOMediaCodec/libavif/releases/tag/v1.4.1

The STFWG encourages users to upgrade to the new release and provide feedback: https://github.com/AOMediaCodec/libavif/issues. Your input helps drive continuous improvements to the technology, so all comments are welcome.

It solves the problem of how to make AV1 video usable everywhere, even on low-end mobile phones. By delivering fast, efficient, and portable decoding, it has helped AV1 move from a promising standard to a widely deployed reality — across devices, platforms, and use cases.

The name reflects its purpose: “dav1d” is a recursive accronym and stands for “dav1d is an AV1 decoder,” with the “AV1” referencing the AV1 video codec.

Understanding the Basics: What Is AV1?

To understand dav1d, it helps to start with AV1 (AOMedia Video 1) — a modern video codec developed by the Alliance for Open Media.

A video codec is a method for compressing and decompressing video data. Compression reduces file size so video can be streamed or stored efficiently. Decompression restores the video for playback.

AV1 stands out because it is:

• Open source, meaning anyone can use and implement it.
• Highly efficient, delivering similar visual quality at 30%–50% lower bitrate (the amount of data transmitted per second) compared to older codecs.

This efficiency is especially important for streaming, where bandwidth and data costs matter.

Why dav1d Was Needed

When AV1 was introduced, there was an important technology gap. Many devices did not yet include hardware decoders — specialized chips designed to decode video efficiently.

Without hardware support, AV1 playback depended on software decoding, where the main processor (central processing unit, or CPU) handles the work. This can be slow and power-intensive if not optimized.

That is where AOMedia member VideoLAN stepped in. The team — known for creating the widely used VLC media player — developed dav1d to solve this problem. Their goal was clear: create a software decoder that is:

• As fast and performant as possible
• Small in size
• Very cross-platform (able to run on many operating systems and hardware types)
• Correctly threaded (able to split work efficiently across multiple CPU cores)

This made AV1 usable even on devices without dedicated hardware support.

How dav1d Works: From Data to Video

A decoder like dav1d takes compressed video data and reconstructs it into frames — individual images that, when played in sequence, form video.

This process involves several steps:

• Entropy decoding: Interpreting compressed data into usable signals.
• Motion compensation: Rebuilding frames using differences between them.
• Filtering and restoration: Improving visual quality and removing artifacts.

Each step must be performed quickly and accurately to ensure smooth playback.

Dav1d is designed to handle these steps efficiently, even for high-resolution video such as 4K (ultra-high-definition video with roughly 4,000 horizontal pixels).

Performance at Scale: Speed, Efficiency, and Threading

One of dav1d’s defining features is performance. It is widely regarded as one of the fastest AV1 software decoders available.

Several technical choices make this possible:

• Threading: dav1d divides decoding tasks across multiple CPU cores. This parallel processing improves speed and reduces playback lag.
• SIMD (single instruction, multiple data): A technique that allows a single instruction to process multiple data points at once, improving efficiency on modern processors.
• Architecture-specific optimizations: dav1d includes tailored code paths for platforms such as x86, ARM (the CPU types of mobile phones and Apple devices), and RISC-V (an open instruction set architecture).

These optimizations allow dav1d to play video smoothly while keeping CPU usage and power consumption low — a key requirement for mobile and other battery-powered devices.

Real-World Impact: Where dav1d Is Used

Dav1d is now widely deployed across the video landscape:

• Major web browsers, including Google Chrome, Safari, Mozilla Firefox, and Microsoft Edge.
• Operating systems such as Android, iOS, Windows, macOS, and Linux.
• Media frameworks like FFmpeg.
• Video Players like VLC.

Its adoption by companies like Google means that even mid-range or budget devices can play AV1 video smoothly without specialized hardware.

Why It Matters for Developers and Users

For developers, dav1d provides a reliable, high-performance decoder that can be integrated into applications without licensing barriers.

For users, it enables:

• Better video quality at lower data usage.
• Smooth playback on a wide range of devices.
• Access to modern video features such as HDR (high dynamic range), which improves contrast and color.

It also supports advanced formats like 10-bit and 12-bit video, which allow for greater color precision.

A Mature Project — and What Comes Next

Recent releases, such as the “Sonic” series, show that dav1d has reached a high level of maturity. Core optimizations are complete, and the focus is shifting to maintenance, compatibility, and incremental improvements.

At the same time, development continues for emerging processor architectures and future codecs. As AV1 adoption grows — and as new standards like AV2 begin to emerge — dav1d sets a strong foundation for software-based video decoding.

Wakefield, Mass. — April 28, 2026 — The Alliance for Open Media (AOMedia) today announced that the reference software for its upcoming Polygonal Mesh Coding (PMC) standard is now publicly available. The software can be accessed on GitLab and is ready for developers to use, evaluate, and contribute feedback.

Polygonal Mesh Coding (PMC) is a compression technology designed for polygonal meshes — a common method for representing 3D objects using vertices, edges, and faces. These meshes are widely used in applications such as gaming, augmented reality (AR) for e-commerce, virtual reality (VR) social spaces, and industrial design. Efficient compression reduces the amount of data required to store and transmit these assets, improving performance across networks and devices.

The newly released reference software is a complete codec developed by AOMedia’s Volumetric Visual Media (VVM) Working Group. It reflects more than three years of collaborative development and standardization work.

“This release marks an important step toward making volumetric media practical at scale,” said Khaled Mammou, co-chair of AOMedia’s VVM Working Group. “The reference software provides a working implementation of the PMC standard that developers can use today to evaluate performance, test integrations, and help shape the final specification.”

The PMC standard introduces new compression tools, including advanced connectivity coding algorithms and improved methods for predicting geometry and attributes. These approaches address limitations in existing mesh compression technologies, which often focus on simpler mesh structures and do not fully optimize compression efficiency for streaming and complex 3D content.

Early expectations indicate that PMC may achieve 20 to 40 percent better compression efficiency compared to established codecs such as Draco, though comprehensive benchmarking is still in progress.

The reference software has already been used to support integration efforts with OpenUSD (Universal Scene Description), a framework for describing complex 3D scenes. A proposed update to OpenUSD introduces PMC encoding and decoding through a plugin, along with tooling to simplify testing. That proposal remains under review.

“The availability of this reference implementation gives developers a concrete foundation to build on,” said Shan Liu, distinguished scientist and general manager of Tencent Media Lab and co-chair of the VVM Working Group. “It enables early experimentation with next-generation mesh compression and helps ensure that the standard meets real-world requirements across a range of applications.”

AOMedia is inviting developers, researchers, and companies to download the software, test it with their own content, and provide feedback to the VVM Working Group. This input will help refine the specification as it moves toward finalization.

“The work on PMC reflects strong collaboration across the industry,” said Igor Vytyaz, software engineer at Google and a software coordinator for the VVM Working Group. “By making the reference software available early, we can accelerate adoption and ensure the standard supports a broad set of use cases.”

The PMC standard is part of AOMedia’s broader effort to develop open technologies for next-generation media. The VVM Working Group continues to finalize the PMC specification, with further updates expected as testing and validation progress.

Developers can access the reference software and begin testing today at: https://gitlab.com/AOMediaVVM/reference-software/aomedia-pmc

About AOMedia

Launched in 2015, the Alliance for Open Media (AOMedia) develops open standards for media — spanning video, audio, still images, and immersive technologies. AOMedia brings together 49 global innovators — including tech leaders with decades of media tech experience and some of the world’s largest patent holders — to support this mission. Its steering committee consists of Amazon, Apple, Cisco, Google, Intel, Meta, Microsoft, Mozilla, Netflix, NVIDIA, Samsung Electronics, and Tencent. Learn more at www.aomedia.org.

Media Contact Lillian Guinther
AOMedia Public Relations
lguinther@virtualinc.com

Immersive Machines was founded in 2023 to address the need for intelligent batch processing and file management within audio software workflows.

Over the past year, the team at Immersive Machines has seen growing interest in immersive audio experiences extending to major distribution platforms, and they identified AOMedia membership as an opportunity to support emerging open standards such as the Immersive Audio Model & Format (IAMF). With expanding support for IAMF, the company’s software, Immersive Master Pro, aims to simplify workflows for artists and producers delivering immersive audio content.

“The response from the creative community to immersive audio has been more enthusiastic and more enduring than anyone imagined a decade ago,” said Nathaniel Reichman, Co-Founder and CEO of Immersive Machines. “Our role as technologists is to give artists, engineers and producers the tools to deliver immersive music and soundtracks to more platforms and audiences. AOMedia provides the framework for advancing open, forward-looking formats like IAMF and AV2, and we’re excited to bring IAMF workflows to the professionals using Immersive Master Pro in their daily work.”

AOMedia’s codec-agnostic IAMF enables immersive audio experiences across a range of applications, including streaming, gaming, augmented and virtual reality, and traditional broadcasting.

“AOMedia is pleased to welcome Immersive Machines as a new member, and we look forward to their contributions,” said Dr. Pierre-Anthony Lemieux, Executive Director, AOMedia. “As immersive audio technologies continue to evolve, members like Immersive Machines help us advance our work on open audio standards and enhance experiences for both content creators and consumers across the ecosystem.”

About Immersive Machines

Immersive Machines develops professional software tools for immersive audio mastering and delivery. Founded in 2023, the company’s flagship application, Immersive Master Pro, helps audio professionals manage and deliver immersive audio formats efficiently and at high quality.

Used by artists, engineers, producers and post-production teams, Immersive Master Pro supports emerging immersive media workflows through reference audio playback, format conversion, intelligent batch processing and file management. For more information, go to: https://immersive-machines.com/

About the Alliance for Open Media

Launched in 2015, the Alliance for Open Media (AOMedia) was formed to define and develop technologies to address marketplace demand for open standards for media compression and delivery. Founding Members include Amazon, Apple, Cisco, Google, Intel, Meta, Microsoft, Mozilla, Netflix, NVIDIA, Samsung Electronics and Tencent. AOMedia’s open-source, royalty-free video codec AV1 is a significant milestone in the ability to deliver a next-generation video format that is interoperable, open, optimized for internet delivery and scalable to any modern device at any bandwidth. Visit www.aomedia.org or follow AOMedia on LinkedIn.

This post has been updated to correct the list of Founding Members in the "About" section.

Press contact:
AOMedia
Lillian Guinther
Aomediamarcom@virtualinc.com

Immersive Machines
Al Houghton
info@immersive-machines.com

Now that the AV2 specification is publicly available after five years of hard work, AOMedia members are already demonstrating it in real-world playback environments. At CES 2026, AOMedia members Google, and VideoLAN participated in AV2 decoding demonstrations, with support from THX. They showed real-time AV2 playback on laptops using current reference implementations, spanning both native applications and browser-based streaming pipelines.

Together, these efforts showed that AV2 decoding can operate end-to-end in realistic playback scenarios and on widely available laptop-class systems.

Native application decoding with VLC

VideoLAN demonstrated real-time AV2 decoding using VLC 4 running on a macOS laptop. The playback occurred in a native desktop media player rather than in a web browser. The same approach also ran on other platforms, reflecting VLC’s cross-platform design.

The demonstration used a VLC plugin integrating the AVM reference decoder. The plugin was built against the latest AV2 development branch and executed on an ARM-based macOS system.

This work represents the first known demonstration of AV2 decoding in a native desktop media player on laptops. It showed that AV2 decoding can be integrated into an existing media player architecture and run in real time on standard consumer hardware.

Web-based decoding and streaming pipeline

A second demonstration showed AV2 decoding in a browser-based streaming environment. Google demonstrated AV2 playback using Chrome to play AV2 videos streamed from YouTube to a gaming laptop, with real time playback at 1080p and 24 frames per second.

For this demonstration, a set of videos was encoded using a pre-release AV2 bitstream generated with the libavm reference encoder. The content was delivered via an existing streaming platform and played back using a custom browser build that incorporated the libavm reference decoder.

This demonstration exercised a complete encode-to-decode pipeline and showed that AV2 can function within a browser-based playback environment on consumer laptop hardware.

Technical observations

Across both demonstrations, several technical observations emerged:

• AV2 decoding is already functional in real playback environments using reference implementations.
• Real-time decoding was achieved at common resolutions on laptop-class hardware.
• AV2 can be integrated through multiple implementation paths, including native applications and web-based streaming stacks.

The demonstrations focused on feasibility and integration rather than on optimized performance or benchmarking.

Context and next steps

Both demonstrations relied on reference decoders and pre-release AV2 code. The results should not be interpreted as final performance characteristics, but as an indication of current implementation progress.

As AV2 development continues, further work will focus on optimization, additional platform support, and production-grade implementations. AOMedia and its members will continue to share technical updates as AV2 advances toward broader adoption.

Follow AOMedia on LinkedIn to stay up to date on AV2 development, implementation updates, and future technical demonstrations.

SVT-AV1 4.0, released earlier this year, represents a major software update that features a whole host of API improvements, including:

• Extended CRF range to 70, which reduces the impact or QP scaling, allowing the encoder to reach lower bitrates
• Added quarter steps between CRF increments to allow for further granularity in QP selection
• Added support for setting a custom global logger for library consumers
• Cleaned up public API headers including removal of deprecated macros, structs, and fields
• Added ability to calculate per-frame PSNR and SSIM metrics
• Added tune Image Quality (IQ) and MS-SSIM for Still Image coding mode
• This release boasts notable improvements of compute vs. compression efficiency across all modes, but in particular the AVIF encoding mode. It’s also worth noting that this latest version of the spec adds AC Bias, a psychovisual feature that improves detail preservation and film grain retention.

SVT-AV1 4.0 can be accessed on GitLab, and the SIWG requests that users try it out and report back on their experiences to allow for continuous improvements in SVT-AV1.

A Closer Look at SVT-AV1

The SIWG has been the steward of the SVT-AV1 since 2020, continually evolving and advancing it.

As SVT-AV1 continues to improve, adoption continues to increase. Meta has noted that all its AV1 streams (which cover more than 75% of the watch time of videos on Facebook and Instagram) are produced using SVT-AV1. SVT-AV1 also allowed Meta to stream high-quality short-form videos (Reels) for the first time.

In September 2025, SVT-AV1 won an IBC 2025 Innovation Award in the “Content Everywhere” category. Check out our recent SVT-AV1 highlight video, and take a moment to test version 4.0 on GitLab.

Many thanks to the members of the SIWG for their work advancing this spec.