Future-Proofing for AV2: Codec-Agnostic AI Preprocessing Strategies You Can Deploy Today

Introduction

The video streaming landscape is on the brink of another major transformation. AV2, AOMedia's next-generation codec, promises to deliver unprecedented compression efficiency and visual quality improvements over its predecessor AV1. (Coconut) However, the reality for most OTT platforms is that codec migrations don't happen overnight. While AV2 field trials are underway, the majority of streaming infrastructures will continue relying on H.264, HEVC, and AV1 for the foreseeable future.

This presents a unique opportunity for forward-thinking CTOs and streaming engineers. Rather than waiting for AV2 encoders to mature, you can implement codec-agnostic AI preprocessing solutions today that deliver immediate bandwidth savings while automatically carrying forward to future codec generations. (Sima Labs) The key is deploying AI-powered preprocessing engines that slip seamlessly in front of any encoder, providing instant optimization benefits without disrupting existing workflows.

In this comprehensive guide, we'll explore how SimaBit's patent-filed AI preprocessing technology can reduce video bandwidth requirements by 22% or more while boosting perceptual quality across all codec types. (Sima Labs) We'll also examine the latest AOM AVM toolchain updates, map them to practical deployment strategies, and provide a readiness checklist for technical leaders preparing for the AV2 transition.

The Current State of Video Codecs in 2025

AV1 Adoption Challenges

Despite being released in 2018, AV1 adoption faces significant hurdles in 2025. Hardware decode support on mobile devices remains stuck in the mid-to-low teens, with VVC at zero percent adoption. (Streaming Media Global) This hardware limitation forces many platforms to rely on software-only decoding, which impacts battery life and device performance.

Meta has begun distributing AV1 streams for software-only playback and co-created the open source VCAT (Video Codec Acid Test) project to benchmark mobile device capabilities. (Streaming Media Global) Meanwhile, VVC IP owners like Kwai, ByteDance, and Tencent are deploying VVC with software decode, indicating that the industry is moving forward with next-generation codecs despite hardware limitations.

The Codec Royalty Landscape

The video codec industry experienced significant changes in 2023, with an increased likelihood of having to pay content royalties for using various codecs. (Streaming Media Global) This shift has made codec selection more complex, as organizations must now balance technical performance with licensing costs and legal considerations.

The Alliance for Open Media's AV1 codec has gained popularity partly due to its royalty-free nature, helping to loosen MPEG's stronghold on video compression. (Gough Lui) Major streaming platforms including YouTube, Facebook, Vimeo, and Netflix have adopted AV1, with AOMedia's patent defense program expected to further increase adoption despite some patent claims.

Understanding AV2: The Next Generation

Technical Improvements Over AV1

AV2 represents a significant leap forward in video compression technology, promising outstanding visual quality at reduced bitrates compared to current-generation codecs. (Coconut) The codec builds upon AV1's foundation while incorporating advanced machine learning techniques and improved compression algorithms.

The demand for high-quality, fast video streaming has reached an all-time high, driving the need for more efficient compression technologies. (Coconut) AV2 addresses this demand by providing superior compression efficiency while maintaining backward compatibility considerations for existing infrastructure.

Field Trial Status and Timeline

While AV2 shows tremendous promise, the codec is still in field trial phases. The transition from experimental codec to production-ready encoder typically takes several years, involving extensive testing, optimization, and hardware support development. This timeline means that most streaming platforms will continue relying on existing codecs for the immediate future.

The MSU Video Codecs Comparison 2022 demonstrated the complexity of codec evaluation, with winners varying depending on the objective quality metrics used. (MSU) This variability highlights the importance of comprehensive testing when evaluating new codec technologies.

The Case for Codec-Agnostic AI Preprocessing

Immediate Benefits Without Migration Risk

The beauty of AI preprocessing lies in its codec-agnostic nature. SimaBit installs in front of any encoder—H.264, HEVC, AV1, AV2, or custom—allowing teams to keep their proven toolchains while immediately gaining bandwidth reduction benefits. (Sima Labs) This approach eliminates the risk and complexity associated with full codec migrations while delivering measurable improvements.

Video content consumes massive amounts of bandwidth, with streaming accounting for 65% of global downstream traffic in 2023. (Sima Labs) Every minute, platforms like YouTube ingest over 500 hours of footage, and each stream must reach viewers without buffering or visual artifacts. AI preprocessing addresses this challenge by optimizing video data before it reaches the encoder.

Proven Performance Metrics

SimaBit's patent-filed AI preprocessing engine has been benchmarked on Netflix Open Content, YouTube UGC, and the OpenVid-1M GenAI video set, demonstrating bandwidth reductions of 22% or more while boosting perceptual quality. (Sima Labs) These results have been verified through VMAF/SSIM metrics and golden-eye subjective studies, providing confidence in real-world performance.

The technology particularly excels with AI-generated content, where social platforms often degrade video quality due to aggressive compression. (Sima Labs) Every platform re-encodes content to H.264 or H.265 at fixed target bitrates, making preprocessing optimization crucial for maintaining visual fidelity.

AI Video Enhancement Technologies

Machine Learning Approaches

AI video enhancement and upscaling utilize machine learning algorithms to improve video quality by increasing resolution, reducing noise, and enhancing details. (TensorPix) Unlike traditional methods that rely on interpolation techniques, AI approaches employ neural networks trained on vast datasets to predict and generate high-resolution images from low-resolution inputs.

Neural networks and machine learning represent computational models inspired by the human brain's structure, capable of recognizing patterns and making predictions. (TensorPix) These systems can analyze video content at a granular level, identifying optimal preprocessing strategies for different content types and quality requirements.

Advanced Enhancement Capabilities

Modern AI video enhancers like Aiarty can generate more details, denoise, deblur, upscale, and restore flawed videos by up to 4K clarity using specialized AI models. (Generative AI Pub) These tools offer hybrid AI models for improved details and clarity on output videos, demonstrating the rapid advancement in AI-powered video processing.

The technology can enhance video texture details, remove noise, deblur, and upscale videos while improving their overall clarity. (Generative AI Pub) This comprehensive approach to video enhancement makes AI preprocessing particularly valuable for streaming applications where quality and bandwidth efficiency are paramount.

Implementation Strategies for CTOs

Deployment Architecture

Implementing codec-agnostic AI preprocessing requires careful consideration of your existing streaming infrastructure. The ideal deployment places the AI preprocessing engine immediately before your current encoder, creating a transparent optimization layer that doesn't disrupt established workflows.

Video Input → AI Preprocessing (SimaBit) → Existing Encoder → CDN Distribution

This architecture ensures that your current encoding parameters, quality settings, and distribution mechanisms remain unchanged while adding intelligent preprocessing optimization. The approach minimizes implementation risk while maximizing immediate benefits.

Integration Considerations

When planning your AI preprocessing deployment, consider these key factors:

• Latency Impact: Ensure preprocessing latency aligns with your streaming requirements

• Scalability: Plan for processing capacity that matches your peak encoding demands

• Quality Metrics: Establish VMAF/SSIM benchmarks to measure improvement

• Fallback Mechanisms: Implement bypass capabilities for troubleshooting

• Monitoring: Deploy comprehensive logging and performance tracking

Performance Optimization

Netflix's tech team popularized VMAF as a gold-standard metric for streaming quality assessment. (Sima Labs) When implementing AI preprocessing, establish baseline VMAF scores for your current encoding pipeline, then measure improvements after preprocessing deployment.

For optimal results with AI-generated content, always select the newest model before rendering video and lock resolution to 1024 × 1024, then upscale with appropriate algorithms for a balanced blend of detail and smoothness. (Sima Labs)

AV2 Readiness Checklist for Technical Leaders

Infrastructure Assessment

Current State Evaluation:

• Document existing encoder types and versions

• Measure current bandwidth consumption and CDN costs

• Establish baseline quality metrics (VMAF, SSIM, PSNR)

• Identify content types and volume distribution

• Assess hardware decode support across target devices

Preprocessing Readiness:

• Evaluate AI preprocessing integration points

• Test codec-agnostic solutions with current infrastructure

• Measure bandwidth reduction potential

• Validate quality improvements through subjective testing

• Plan deployment phases and rollback procedures

Future-Proofing Strategy

AV2 Preparation:

• Monitor AOM AVM toolchain development progress

• Establish relationships with encoder vendors for AV2 support

• Plan hardware upgrade cycles for AV2 decode support

• Develop content migration strategies for new codec

• Create testing frameworks for AV2 evaluation

Technology Roadmap:

• Map AI preprocessing benefits to AV2 timeline

• Plan incremental deployment strategies

• Establish performance benchmarks for codec transitions

• Develop cost-benefit analysis for each codec generation

• Create contingency plans for delayed AV2 adoption

Cost-Benefit Analysis

Immediate ROI from AI Preprocessing

Implementing AI preprocessing delivers immediate financial benefits through reduced bandwidth consumption and improved user experience. With SimaBit's demonstrated 22%+ bandwidth reduction, organizations can calculate direct CDN cost savings while improving video quality. (Sima Labs)

Long-term Strategic Value

The codec-agnostic nature of AI preprocessing provides long-term strategic value by:

• Future-proofing: Benefits automatically carry forward to AV2 and beyond

• Risk mitigation: No dependency on specific codec adoption timelines

• Competitive advantage: Immediate quality and efficiency improvements

• Operational continuity: No disruption to existing workflows

Calculating Total Cost of Ownership

When evaluating AI preprocessing solutions, consider:

Best Practices for Deployment

Testing and Validation

Before deploying AI preprocessing in production, conduct comprehensive testing across your content library. Focus on diverse content types including live streams, on-demand video, and user-generated content. Establish clear quality benchmarks and measure improvements using industry-standard metrics.

Before posting content, run private dress rehearsals by uploading draft clips to test environments or secondary accounts to validate quality improvements. (Sima Labs) This approach helps identify potential issues before they impact end users.

Monitoring and Optimization

Implement comprehensive monitoring to track:

• Preprocessing latency and throughput

• Quality metric improvements (VMAF, SSIM)

• Bandwidth reduction percentages

• User experience metrics (buffering, startup time)

• CDN cost reductions

Scaling Considerations

Plan your AI preprocessing deployment to scale with your content volume and quality requirements. Consider peak processing demands, geographic distribution needs, and failover capabilities to ensure consistent performance across all use cases.

Industry Partnerships and Ecosystem

Strategic Alliances

Sima Labs has established partnerships with AWS Activate and NVIDIA Inception, providing access to cloud infrastructure and GPU acceleration technologies that enhance AI preprocessing capabilities. (Sima Labs) These partnerships ensure that preprocessing solutions can scale effectively across different deployment environments.

Technology Integration

The codec-agnostic nature of AI preprocessing makes it compatible with existing technology stacks and future codec developments. This flexibility allows organizations to maintain their current vendor relationships while adding intelligent optimization capabilities.

Conclusion

The transition to AV2 represents an exciting advancement in video compression technology, but waiting for full codec maturity means missing immediate optimization opportunities. By implementing codec-agnostic AI preprocessing solutions today, organizations can achieve substantial bandwidth reductions and quality improvements that automatically carry forward to future codec generations.

SimaBit's proven ability to reduce bandwidth by 22% or more while improving perceptual quality makes it an ideal solution for forward-thinking streaming platforms. (Sima Labs) The technology's codec-agnostic design ensures that investments made today will continue delivering value as the industry transitions to AV2 and beyond.

For CTOs and technical leaders, the choice is clear: implement AI preprocessing now to gain immediate benefits while future-proofing your streaming infrastructure for the next generation of video codecs. The combination of proven performance, minimal implementation risk, and automatic compatibility with future technologies makes AI preprocessing an essential component of any modern streaming strategy.

The video streaming landscape will continue evolving, but organizations that deploy intelligent preprocessing solutions today will be best positioned to capitalize on future codec advancements while delivering superior user experiences and operational efficiency.

Frequently Asked Questions

What are codec-agnostic AI preprocessing strategies and why are they important for streaming platforms?

Codec-agnostic AI preprocessing strategies are video enhancement techniques that work independently of specific codecs like H.264, H.265, or AV1. These AI-powered methods improve video quality and reduce bandwidth requirements before encoding, delivering immediate benefits while ensuring compatibility with future codecs like AV2. They're crucial because they allow streaming platforms to optimize performance today without being locked into specific codec technologies.

How much bandwidth savings can AI preprocessing deliver for streaming platforms?

AI preprocessing can deliver 22% or more bandwidth savings for streaming platforms through intelligent video enhancement and optimization. These savings come from AI algorithms that reduce noise, enhance details, and optimize video content before encoding, allowing codecs to work more efficiently. The bandwidth reduction translates directly to cost savings and improved user experience across all devices.

What is AV2 and how does it compare to current video codecs like AV1?

AV2 is AOMedia's next-generation video codec that promises unprecedented compression efficiency and visual quality improvements over AV1. While AV1 has gained popularity since 2018 and is used by major platforms like YouTube, Netflix, and Facebook, AV2 is designed to deliver even better compression ratios and quality. However, codec migrations take time, making codec-agnostic preprocessing strategies essential for immediate benefits.

Why is software decoding becoming more important for mobile video streaming?

Software decoding is crucial because hardware decode support for newer codecs remains limited on mobile devices. As of 2025, AV1 hardware decode is only available on mid-to-low teens percentage of mobile devices, while VVC hardware support is at zero. Companies like Meta are already distributing AV1 streams for software-only playback, and major platforms are deploying advanced codecs with software decode to reach broader audiences.

How can AI video enhancement improve streaming quality before codec encoding?

AI video enhancement uses machine learning algorithms to improve video quality by increasing resolution, reducing noise, and enhancing details before codec encoding. Unlike traditional interpolation methods, AI approaches employ neural networks trained on vast datasets to predict and generate high-quality images from lower-quality inputs. This preprocessing step allows any codec to work more efficiently, delivering better results regardless of the encoding technology used.

What should CTOs consider when implementing AI preprocessing for bandwidth reduction in streaming?

CTOs should focus on codec-agnostic solutions that deliver immediate bandwidth savings while preparing for future codec migrations. Key considerations include evaluating AI preprocessing tools that can reduce bandwidth by 22% or more, ensuring compatibility across different streaming scenarios, and implementing solutions that work with existing infrastructure. The goal is to optimize streaming performance today while building flexibility for tomorrow's codec technologies like AV2.

Sources

1. https://compression.ru/video/codec_comparison/2022/10_bit_report.html

2. https://generativeai.pub/next-gen-ai-video-enhancer-to-fix-noisy-low-res-footage-into-natural-looking-4k-aiarty-323525a4f26c?gi=edc6e485a253&source=rss----440100e76000---4

3. https://goughlui.com/2024/01/02/video-codec-round-up-2023-part-12-libaom-av1-aomedia-video-1/

4. https://tensorpix.ai/blog/ai-video-enhancement-and-upscaling-all-you-need-to-know

5. https://www.coconut.co/articles/unveil-av2-codec-nextgen-video-streaming

6. https://www.sima.live/blog/midjourney-ai-video-on-social-media-fixing-ai-video-quality

7. https://www.sima.live/blog/understanding-bandwidth-reduction-for-streaming-with-ai-video-codec

8. https://www.streamingmediaglobal.com/Articles/Editorial/Featured-Articles/Software-decoding-and-the-future-of-mobile-video-169701.aspx

9. https://www.streamingmediaglobal.com/Articles/Editorial/Featured-Articles/The-State-of-Video-Codecs-2024-163439.aspx