Getting Ready for AV2: Why Codec-Agnostic AI Pre-Processing Beats Waiting for New Hardware

Introduction

The video streaming industry stands at a crossroads. AV2, the next-generation codec promising substantial compression gains, has captured headlines with its laboratory performance improvements. However, the reality of widespread hardware support won't arrive until 2027 or later, leaving streaming providers in a challenging position: wait for future gains or optimize today's infrastructure. (Deep Video Precoding)

While the industry debates AV2's timeline, a more immediate solution exists. AI-powered preprocessing engines like SimaBit from Sima Labs are delivering measurable bandwidth reductions of 22% or more on existing H.264, HEVC, and AV1 stacks without requiring hardware upgrades or workflow changes. (Sima Labs Blog) This codec-agnostic approach offers streaming providers a practical path forward, extending the life of current encoders while preparing for future codec transitions.

The global media streaming market is projected to reach $285.4 billion by 2034, growing at a CAGR of 10.6% from 2024's $104.2 billion. (Media Streaming Market) With video traffic expected to comprise 82% of all IP traffic by mid-decade, the pressure to optimize bandwidth efficiency has never been greater. The question isn't whether to optimize, but how to do it most effectively while maintaining flexibility for future codec adoption.

The AV2 Promise vs. Reality Gap

Laboratory Performance vs. Real-World Deployment

AV2 has demonstrated impressive compression gains in controlled laboratory environments, with some studies showing significant improvements over AV1. However, the path from laboratory benchmarks to mass deployment involves numerous challenges that extend far beyond codec performance metrics. (Frame-Type Sensitive RDO Control for Content-Adaptive Encoding)

The video transcoding industry has learned valuable lessons from previous codec transitions. Cloud-based deployment of content production and broadcast workflows has continued to disrupt the industry, particularly after the pandemic accelerated digital transformation. (Filling the gaps in video transcoder deployment in the cloud) However, the key tools required for unlocking cloud workflows, such as transcoding, metadata parsing, and streaming playback, are increasingly commoditized, making the timing of codec adoption critical for cost optimization.

Hardware Support Timeline Challenges

The reality of AV2 hardware support presents a significant timeline challenge. Unlike software-only solutions that can be deployed immediately, hardware-accelerated codec support requires:

• Chip Design Cycles: 18-24 months for silicon development

• Device Manufacturing: 6-12 months for integration into consumer devices

• Market Penetration: 2-3 years for meaningful adoption rates

• Legacy Device Support: 5-7 years for complete transition

This timeline pushes meaningful AV2 hardware support well into 2027 and beyond, creating a substantial gap between codec availability and practical deployment. (Deep Render: An AI Codec That Encodes in FFmpeg, Plays in VLC, and Outperforms SVT-AV1)

The Cost of Waiting

Streaming providers face mounting pressure from multiple directions:

• Bandwidth Costs: CDN expenses continue to rise with traffic growth

• Quality Expectations: Viewers demand higher resolutions and frame rates

• Competition: Platforms compete on streaming quality and reliability

• Infrastructure Investment: Hardware refresh cycles require significant capital

Waiting for AV2 hardware support means accepting these escalating costs for potentially three more years. For a streaming service handling petabytes of monthly traffic, even a 10% bandwidth reduction translates to millions in annual savings. (Sima Labs Blog)

The Immediate Solution: Codec-Agnostic AI Preprocessing

Understanding AI Preprocessing Technology

AI preprocessing represents a fundamentally different approach to video optimization. Instead of replacing existing codecs, it enhances their performance by intelligently preparing video content before encoding. This preprocessing stage can include denoising, deinterlacing, super-resolution, and saliency masking to remove up to 60% of visible noise and optimize bit allocation. (Sima Labs Blog)

The key advantage of this approach lies in its compatibility with existing infrastructure. SimaBit from Sima Labs exemplifies this philosophy by slipping in front of any encoder—H.264, HEVC, AV1, AV2, or custom solutions—without requiring changes to existing workflows. (Sima Labs Blog)

Proven Performance Metrics

The effectiveness of AI preprocessing has been validated across multiple content types and quality metrics:

Content Validation:

• Netflix Open Content benchmarks

• YouTube UGC datasets

• OpenVid-1M GenAI video collections

Quality Metrics:

• VMAF (Video Multi-method Assessment Fusion) scores

• SSIM (Structural Similarity Index) measurements

• Golden-eye subjective studies

These comprehensive evaluations demonstrate consistent bandwidth reductions of 22% or more while maintaining or improving perceptual quality. (Sima Labs Blog) The technology has been verified through partnerships with AWS Activate and NVIDIA Inception, providing additional validation of its enterprise readiness.

Real-Time Performance Capabilities

One critical advantage of modern AI preprocessing solutions is their real-time performance. SimaBit processes 1080p frames in under 16 milliseconds, making it suitable for live streaming applications as well as video-on-demand workflows. (Sima Labs Blog) This performance level ensures that preprocessing doesn't become a bottleneck in high-throughput encoding pipelines.

Comparative Analysis: AV2 Laboratory Gains vs. Immediate AI Benefits

Quantifying the Opportunity Cost

To understand the true value proposition, we need to compare the theoretical future benefits of AV2 against the immediate gains available through AI preprocessing:

The analysis reveals that while AV2 may offer superior theoretical performance, the immediate availability and lower risk profile of AI preprocessing make it a compelling interim solution. (Rate-Perception Optimized Preprocessing for Video Coding)

Industry Validation Examples

Several major streaming providers have already demonstrated the effectiveness of AI-enhanced video optimization:

• Netflix: Reports 20-50% bit reduction for many titles through per-title ML optimization

• Dolby: Achieves 30% compression improvement for Dolby Vision HDR using neural compression

• Google: Documents 15% visual quality score improvements in user studies comparing AI versus H.264 streams

• Intel: Demonstrates 28% compression ratio improvements over H.265 with AI codecs

These real-world implementations validate the practical benefits of AI preprocessing across different content types and viewing scenarios. (Sima Labs Blog)

Environmental Impact Considerations

The environmental implications of bandwidth optimization extend beyond cost savings. Researchers estimate that global streaming generates more than 300 million tons of CO₂ annually, making bandwidth reduction a sustainability imperative. (Sima Labs Blog) A 22% bandwidth reduction through AI preprocessing directly translates to proportional energy savings across data centers and last-mile networks.

Technical Implementation: How Codec-Agnostic Preprocessing Works

Architecture Overview

The codec-agnostic approach to AI preprocessing operates on a simple but powerful principle: optimize the input to any encoder rather than replacing the encoder itself. This architecture provides several key advantages:

Raw Video Input → AI Preprocessing Engine → Enhanced Video → Existing Encoder → Optimized Output

This pipeline design ensures compatibility with existing infrastructure while providing immediate performance benefits. The preprocessing stage can be implemented as:

• Software SDK: Integrated directly into encoding applications

• API Service: Called remotely for cloud-based processing

• Hardware Acceleration: Leveraging GPU or specialized AI chips

• Hybrid Deployment: Combining multiple approaches for optimal performance

Key Preprocessing Techniques

Modern AI preprocessing engines employ multiple optimization techniques:

Noise Reduction:

• Temporal and spatial denoising algorithms

• Content-aware filtering that preserves important details

• Adaptive processing based on content complexity

Perceptual Optimization:

• Saliency mapping to identify visually important regions

• Bit allocation optimization based on human visual perception

• Dynamic quality adjustment for different content types

Resolution Enhancement:

• Super-resolution for upscaling lower-quality source material

• Detail preservation during resolution changes

• Adaptive sharpening based on content characteristics

These techniques work together to create video content that encodes more efficiently while maintaining or improving perceived quality. (Sima Labs Blog)

Integration Considerations

Successful implementation of AI preprocessing requires careful consideration of several factors:

Performance Requirements:

• Processing latency must align with encoding pipeline requirements

• Throughput capacity should match or exceed encoding capabilities

• Resource utilization should be optimized for cost-effectiveness

Quality Control:

• Automated quality assessment to ensure consistent output

• Fallback mechanisms for challenging content types

• Continuous monitoring and optimization

Scalability Planning:

• Horizontal scaling for increased throughput

• Load balancing across multiple processing nodes

• Integration with existing orchestration systems

Business Case: Extending Encoder Life and Reducing CapEx

Capital Expenditure Optimization

The financial benefits of codec-agnostic AI preprocessing extend beyond operational cost savings to significant capital expenditure optimization. By enhancing the performance of existing encoding infrastructure, organizations can defer major hardware refresh cycles while maintaining competitive streaming quality.

Hardware Lifecycle Extension:

• Current H.264/HEVC encoders can achieve AV1-like efficiency

• Existing AV1 hardware gains additional performance headroom

• Specialized encoding appliances maintain relevance longer

• GPU-based encoding farms see improved utilization

This lifecycle extension translates to substantial capital savings. A typical enterprise encoding infrastructure representing millions in hardware investment can continue operating effectively for additional years, spreading the depreciation cost over a longer period. (Sima Labs Blog)

Operational Cost Reduction

The immediate operational benefits of AI preprocessing create compelling ROI scenarios:

CDN Cost Reduction:

• 22% bandwidth reduction directly reduces CDN bills

• Improved cache efficiency through consistent quality

• Reduced peak bandwidth requirements during high-traffic events

• Lower costs for global content distribution

Infrastructure Efficiency:

• Higher effective throughput from existing encoding hardware

• Reduced storage requirements for encoded content

• Lower network utilization for content distribution

• Decreased cooling and power requirements

For streaming services handling significant traffic volumes, these savings can reach millions annually. The immediate nature of these benefits provides cash flow improvements that can fund future infrastructure investments. (Sima Labs Blog)

Risk Mitigation Strategy

AI preprocessing serves as an effective hedge against codec transition risks:

Technology Risk Reduction:

• No dependency on unproven hardware support timelines

• Compatibility with multiple codec standards

• Gradual adoption without infrastructure disruption

• Reversible implementation if needed

Competitive Risk Management:

• Immediate quality improvements maintain competitive position

• Cost savings enable investment in other differentiating features

• Flexibility to adopt new codecs when hardware support matures

• Protection against competitor advantages during codec transitions

Future-Proofing: Preparing for AV2 and Beyond

The Codec-Agnostic Advantage

The most significant long-term benefit of AI preprocessing lies in its codec-agnostic nature. This approach provides a future-proof foundation that adapts to new codec standards as they emerge. When AV2 hardware support finally arrives, organizations using AI preprocessing can simply toggle their encoder settings without rebuilding their entire optimization pipeline. (Deep Video Precoding)

Seamless Codec Transitions:

• Preprocessing optimizations apply equally to new codecs

• No retraining or reconfiguration required

• Consistent quality improvements across codec generations

• Reduced complexity during technology transitions

Continuous Improvement Through AI

Unlike static codec implementations, AI preprocessing engines can continuously improve through machine learning advances:

Adaptive Learning:

• Content-specific optimization based on historical performance

• Automatic parameter tuning for different content types

• Integration of new AI research and techniques

• Performance optimization based on user feedback

Evolving Capabilities:

• New preprocessing techniques can be added without infrastructure changes

• Model updates can be deployed remotely

• Performance improvements compound over time

• Integration with emerging AI technologies

This continuous improvement capability ensures that the investment in AI preprocessing technology continues to deliver value long after initial deployment. (Towards Holistic Visual Quality Assessment of AI-Generated Videos: A LLM-Based Multi-Dimensional Evaluation Model)

Strategic Positioning for Next-Generation Codecs

By implementing AI preprocessing now, organizations position themselves advantageously for future codec adoption:

Technical Readiness:

• Established AI processing pipelines

• Optimized content preparation workflows

• Proven quality assessment methodologies

• Scalable infrastructure architecture

Operational Experience:

• Team expertise in AI-enhanced video processing

• Established performance monitoring and optimization practices

• Integration experience with existing systems

• Change management processes for technology adoption

Implementation Roadmap: Getting Started with AI Preprocessing

Phase 1: Assessment and Planning

Successful AI preprocessing implementation begins with thorough assessment of current infrastructure and requirements:

Current State Analysis:

• Inventory existing encoding infrastructure

• Analyze current bandwidth utilization and costs

• Assess quality metrics and user satisfaction

• Identify integration points and dependencies

Requirements Definition:

• Define performance targets and success metrics

• Establish quality standards and acceptance criteria

• Determine scalability and throughput requirements

• Plan integration with existing workflows

Technology Evaluation:

• Compare available AI preprocessing solutions

• Conduct proof-of-concept testing with representative content

• Validate performance claims with actual workloads

• Assess vendor support and partnership opportunities

Phase 2: Pilot Implementation

A controlled pilot implementation allows organizations to validate benefits and refine processes before full deployment:

Pilot Scope Definition:

• Select representative content types and volumes

• Choose appropriate encoding workflows for testing

• Define success criteria and measurement methods

• Establish rollback procedures if needed

Technical Integration:

• Implement AI preprocessing in selected workflows

• Configure monitoring and quality assessment tools

• Establish performance baselines and comparison metrics

• Train operations teams on new processes

Performance Validation:

• Measure bandwidth reduction and quality improvements

• Assess operational impact and resource utilization

• Gather user feedback and satisfaction metrics

• Document lessons learned and optimization opportunities

Phase 3: Production Deployment

Successful pilot results enable confident production deployment across the organization:

Scaled Implementation:

• Roll out AI preprocessing to additional workflows

• Implement automated deployment and configuration management

• Establish production monitoring and alerting

• Create operational procedures and documentation

Optimization and Tuning:

• Fine-tune preprocessing parameters for different content types

• Optimize resource allocation and utilization

• Implement automated quality control and validation

• Establish continuous improvement processes

Future Planning:

• Plan for capacity expansion and scaling

• Evaluate new AI preprocessing capabilities

• Prepare for future codec integration

• Develop long-term technology roadmap

Measuring Success: KPIs and ROI Metrics

Technical Performance Metrics

Successful AI preprocessing implementation requires comprehensive measurement across multiple dimensions:

Bandwidth Efficiency:

• Percentage reduction in bitrate requirements

• Consistency of savings across different content types

• Peak bandwidth reduction during high-traffic periods

• Cumulative bandwidth savings over time

Quality Metrics:

• VMAF score improvements or maintenance

• SSIM measurements for structural similarity

• Subjective quality assessments from user studies

• Buffering and playback quality improvements

Performance Metrics:

• Processing latency and throughput measurements

• Resource utilization and efficiency metrics

• System reliability and uptime statistics

• Scalability and capacity utilization

These technical metrics provide the foundation for understanding the effectiveness of AI preprocessing implementation. (Sima Labs Blog)

Business Impact Measurement

The ultimate success of AI preprocessing must be measured in business terms:

Cost Savings:

• CDN cost reduction from bandwidth savings

• Infrastructure efficiency improvements

• Operational cost reductions

• Capital expenditure deferrals

Revenue Impact:

• Improved user satisfaction and retention

• Reduced churn from quality issues

• Enhanced competitive positioning

• New market opportunities from cost advantages

Strategic Benefits:

• Risk mitigation from codec transition delays

• Flexibility for future technology adoption

• Competitive advantage from early AI adoption

• Environmental impact improvements

Long-Term Value Assessment

The full value of AI preprocessing extends beyond immediate cost savings:

Technology Investment Protection:

• Extended useful life of existing infrastructure

• Reduced risk from codec transition timing

• Improved return on encoding hardware investments

• Future-proofing against technology changes

Organizational Capabilities:

• AI expertise development within the organization

• Enhanced technical capabilities and competencies

• Improved operational efficiency and processes

• Strategic positioning for future innovations

Conclusion: The Smart Hedge Against Codec Uncertainty

The video streaming industry faces a critical decision point. While AV2 promises significant compression improvements, the reality of widespread hardware support remains years away. Organizations that wait for this future technology risk missing immediate opportunities for substantial cost savings and quality improvements.

AI preprocessing represents the optimal strategy for navigating this transition period. With proven bandwidth reductions of 22% or more, immediate deployment capability, and codec-agnostic compatibility, solutions like SimaBit from Sima Labs offer a practical path forward. (Sima Labs Blog) The technology extends the useful life of existing encoding infrastructure while preparing organizations for seamless codec transitions when new hardware becomes available.

The business case for AI preprocessing is compelling across multiple dimensions. Immediate cost savings from reduced bandwidth requirements provide measurable ROI, while capital expenditure deferrals improve cash flow and financial flexibility. The codec-agnostic approach ensures that investments in AI preprocessing technology continue to deliver value regardless of future codec adoption timelines. (Sima Labs Blog)

Perhaps most importantly, AI preprocessing serves as an effective hedge against the uncertainties inherent in codec transitions. Rather than betting on specific timeline predictions or hardware availability, organizations can implement proven technology that delivers immediate benefits while maintaining flexibility for future changes. When AV2 hardware support finally arrives, the transition becomes a simple software configuration change rather than a complex infrastructure overhaul.

The streaming industry's growth trajectory, with the global market expected to reach $285.4 billion by 2034, demands proactive optimization strategies. (Media Streaming Market) Organizations that implement AI preprocessing now position themselves advantageously for this growth, with lower operational costs, improved quality metrics, and greater technical flexibility.

The choice is clear: wait for uncertain future benefits or capture immediate, measurable improvements today. AI preprocessing offers the best of both worlds—immediate optimization of current infrastructure and seamless preparation for future codec adoption. In an industry where bandwidth costs and quality expectations continue to rise, this technology represents not just an optimization opportunity, but a strategic imperative for long-term success.

Frequently Asked Questions

What is codec-agnostic AI preprocessing and how does it work?

Codec-agnostic AI preprocessing is a technology that optimizes video content before it enters traditional encoding pipelines like H.264, HEVC, or AV1. Unlike codec-specific solutions, it works with existing infrastructure by intelligently analyzing and preparing video frames to achieve better compression efficiency. This approach delivers immediate bandwidth savings without requiring new hardware or decoder changes on the client side.

How much bandwidth reduction can AI preprocessing achieve compared to waiting for AV2?

AI preprocessing solutions like SimaBit can deliver up to 22% bandwidth reduction on existing codecs today, while AV2 hardware support won't be widely available until 2027 or later. This means streaming providers can achieve significant cost savings and performance improvements immediately, rather than waiting 2-3 years for new hardware deployment across the ecosystem.

Why is AV2 hardware support taking so long to arrive?

AV2 hardware support faces the typical codec adoption timeline challenges: chip manufacturers need time to design and fabricate new silicon, device manufacturers must integrate these chips, and consumers need to upgrade their devices. Historical patterns show this process takes 3-5 years from codec finalization, putting widespread AV2 hardware availability around 2027-2028 at the earliest.

What are the key advantages of codec-agnostic solutions over waiting for AV2?

Codec-agnostic AI preprocessing offers immediate deployment on existing infrastructure, compatibility with all current devices and players, and proven bandwidth savings today. Unlike AV2, which requires new hardware throughout the entire delivery chain, these solutions work with your current H.264, HEVC, and AV1 workflows while future-proofing your streaming pipeline for when AV2 eventually arrives.

How does AI video preprocessing maintain compatibility with existing streaming infrastructure?

AI preprocessing solutions maintain full compatibility by working as a pre-encoding step that doesn't modify the actual codec standards. The processed video still encodes to standard H.264, HEVC, or AV1 formats that play on any existing device or player. This approach ensures immediate deployment benefits without requiring changes to CDNs, players, or end-user devices.

What makes SimaBit's approach different from other bandwidth reduction solutions?

SimaBit's codec-agnostic AI preprocessing delivers consistent bandwidth reduction across multiple codec standards while maintaining full compatibility with existing streaming infrastructure. Unlike codec-specific optimizations that lock you into particular encoding standards, SimaBit's solution works with H.264, HEVC, AV1, and will be ready for AV2 when it arrives, providing a future-proof approach to streaming optimization.

Sources

1. https://arxiv.org/abs/1908.00812?context=cs.MM

2. https://arxiv.org/abs/2506.04715

3. https://arxiv.org/pdf/2206.11976.pdf

4. https://arxiv.org/pdf/2304.08634.pdf

5. https://export.arxiv.org/pdf/2301.10455v1.pdf

6. https://market.us/report/media-streaming-market/

7. https://streaminglearningcenter.com/codecs/deep-render-an-ai-codec-that-encodes-in-ffmpeg-plays-in-vlc-and-outperforms-svt-AV1.html

8. https://www.sima.live/blog/5-must-have-ai-tools-to-streamline-your-business

9. https://www.sima.live/blog/boost-video-quality-before-compression

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