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The Best AI Video Codecs for Maximum File Size Savings (2025)

Why 2025 Is the Break-Out Year for AI Video Codecs

AI video codecs are finally production-ready in 2025, promising dramatic bitrate savings and file-size reduction for any streamer or storage team. The convergence of mature AI models, widespread NPU adoption, and proven deployments has created an inflection point for the industry.

Video is eating the internet—Cisco forecasts that it will represent 82% of all traffic, creating an urgent need to slash bitrate without denting quality. This explosive growth has made file size reduction and bitrate savings mission-critical for every organization handling video content.

Generative AI video models are advanced algorithms that enhance video quality by predicting and reconstructing details lost during compression. They work as a pre- and post-filter to encoders, saving bandwidth without sacrificing quality. The technology has matured rapidly: SimaBit achieved a 22% average reduction in bitrate, a 4.2-point VMAF quality increase, and a 37% decrease in buffering events in their tests.

AI-Enhanced Traditional Codecs: Getting More from H.264, HEVC & AV1

Rather than replacing existing infrastructure, AI preprocessing engines are supercharging traditional codecs to deliver unprecedented efficiency gains. This hybrid approach offers the best of both worlds: proven codec stability with AI-powered optimization.

AI-enhanced preprocessing engines are already demonstrating the ability to reduce video bandwidth requirements by 22% or more while boosting perceptual quality. These solutions work as intelligent filters that identify and remove perceptual redundancies before encoding, then reconstruct fine details during playback.

Generative AI video models act like a smart pre-filter in front of any encoder, predicting perceptual redundancies and reconstructing fine detail after compression. The result is 22%+ bitrate savings in Sima Labs benchmarks with visibly sharper frames. The technology achieves "20%+ Bitrate Savings" while maintaining compatibility with existing workflows.

SimaBit's Codec-Agnostic Engine

SimaBit represents a breakthrough in AI-powered video optimization, seamlessly integrating with all major codecs including H.264, HEVC, and AV1. Sima Labs today announced the seamless integration of SimaBit, its breakthrough AI-processing engine for bandwidth reduction, into Dolby Hybrik, one of the industry's widely used VOD transcoding platforms.

The platform slips in seamlessly, requiring no change to existing H.264, HEVC, or AV1 pipelines. The SDK is codec-agnostic, cloud-ready, and validated by VMAF/SSIM plus golden-eye studies across Netflix Open and YouTube UGC content. As the company reports, "20%+ Bitrate Savings" is achieved consistently across diverse content types.

SimaBit achieved a 22% average reduction in bitrate, a 4.2-point VMAF quality increase, and a 37% decrease in buffering events in their tests. This performance has been validated across multiple content types, establishing SimaBit as a production-ready solution for immediate deployment.

Learned & Neural Codecs Enter Production

While AI preprocessing enhances traditional codecs, a new generation of fully neural codecs is emerging from research labs into commercial deployment. These learned video compression systems represent a fundamental shift in how we approach video encoding.

GIViC (Generative Implicit Video Compression) exemplifies the cutting-edge of learned compression. The proposed model has been benchmarked against state-of-the-art conventional and neural codecs using a Random Access configuration, yielding BD-rate savings of 15.94%, 22.46% and 8.52% over VVC VTM, DCVC-FM and NVRC, respectively.

Deep Render recently introduced the first AI codec into FFmpeg and VLC, marking a watershed moment for neural codec adoption. AI codecs hold far more promise and opportunities than the initial 40% compression efficiency gains they have already achieved.

LVC is poised to be the next major advancement in video compression. The technology leverages end-to-end optimized neural models that learn compression strategies directly from data, rather than relying on hand-crafted algorithms.

Deep Render's Specialized AI Codec

Deep Render provides a 70% improvement over AV1 in talking heads, and a per-person specialized codec would add another 30%, leading to 5x improvement in video conferencing compared to AV1. This dramatic efficiency gain comes from training models on specific content domains.

Deep Render recently shipped the world's first AI codec to its customers. Their specialization approach can already provide a 30% efficiency gain on top of the 40% gain that Deep Render's codec already provides, leading to a total of approximately 50% compression efficiency gains.

How Do We Measure "Best"? 2025 Benchmarks & Metrics

Determining the best AI video codec requires rigorous, standardized testing across multiple quality metrics and use cases. The industry relies on several key benchmarking initiatives to provide objective comparisons.

The FullHD Objective and Subjective comparison 2025 represents the gold standard for codec evaluation. MSU's eighteenth annual comparison has become the definitive reference, with the team conducting video codec analysis, testing and optimization since 2004.

MSU Video Codecs Comparisons 2025 stands as the "#1 codecs comparisons in the world," providing comprehensive analysis across multiple dimensions. The comparison uses objective metrics including SSIM, PSNR, and VMAF to ensure thorough evaluation.

For subjective quality assessment, the latest comparison involved 10,800+ participants who provided 529,171 valid answers. This massive dataset ensures statistical significance and real-world relevance for the benchmark results.

Deployment Realities: Hardware, Edge GPUs & Workflow Integrations

Translating AI codec performance from benchmarks to production requires careful consideration of hardware requirements, integration complexity, and operational costs.

The AV1 and VVC reference encoders required averaged run times relative to HEVC reference encoder by a factor of more than three for AV1 and more than nine for VVC. However, decoder complexity remains manageable with averaged decoding run time relative to HEVC reference decoder of about 66% for AV1 and 182% for VVC.

The embedded Neural Processing Unit (NPU) market is experiencing robust growth, with market size in 2025 estimated at $15 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033. This hardware proliferation makes AI codec deployment increasingly practical.

The global Edge NPU Module market size is valued at USD 2.42 billion in 2024, and it is expected to reach USD 10.37 billion by 2033, expanding at a robust CAGR of 17.6%. This growth enables distributed AI processing for video workflows at scale.

Looking Ahead: AV2, VVC & H.267 on the Horizon

While current AI codecs deliver impressive results, the next generation of standards promises even greater efficiency gains through deeper AI integration.

AV2 could achieve 30-40% better compression than AV1 while maintaining comparable encoding complexity. The first AV2 silicon is likely to arrive in 2026, followed by software decoders the year after and hardware in 2028.

The H.267 standard is currently projected to be finalized between July and October 2028, with meaningful deployment likely not occurring until around 2034-2036. This timeline reflects the careful standardization process required for widespread adoption.

The Enhanced Compression Model (ECM) project has reached version 15, demonstrating roughly 25% bitrate savings over VVC in random-access configurations and up to 40% for screen-content sequences. These advances show the potential of AI-enhanced compression even within traditional codec frameworks.

Key Takeaways for Streaming, Storage & Beyond

The AI video codec revolution is here, delivering tangible benefits today while promising even greater advances tomorrow. Organizations can realize immediate gains through AI preprocessing while preparing for the neural codec future.

Our Technology Delivers Better Video Quality, Lower Bandwidth Requirements, and Reduced CDN Costs—all verified with industry standard quality metrics and Golden-eye subjective analysis. These benefits translate directly to improved user experience and reduced operational costs.

For teams ready to implement AI video optimization, SimaBit offers a proven path forward. The solution integrates seamlessly with existing workflows, requires no codec replacement, and delivers consistent 20%+ bitrate savings across diverse content types. Whether you're streaming live events, managing VOD libraries, or optimizing user-generated content, AI video codecs represent the most significant advancement in compression technology this decade.

The convergence of mature AI models, widespread hardware support, and production-ready solutions makes 2025 the ideal time to adopt AI video codecs. Start with hybrid approaches like SimaBit to gain immediate benefits while positioning your infrastructure for the fully neural future ahead.

Frequently Asked Questions

What is an AI video codec and how does it reduce file size?

AI video codecs and AI enhancements use learned models to predict and reconstruct details, allowing encoders to remove perceptual redundancies. In hybrid deployments, an AI preprocessor sits before H.264, HEVC, or AV1 to save bitrate while preserving perceived quality. Fully neural codecs learn compression end to end and are starting to reach production.

How does SimaBit integrate with existing H.264, HEVC, or AV1 workflows?

SimaBit operates as a codec-agnostic AI preprocessing engine that plugs into existing pipelines without replacing the encoder. It is available via SDK and integrates with Dolby Hybrik for VOD transcoding. According to Sima Labs resources, it is validated with VMAF and SSIM plus golden-eye reviews and consistently delivers 20 percent or more bitrate savings across diverse content.

What performance gains did Sima Labs report for SimaBit?

In tests, SimaBit achieved an average 22 percent bitrate reduction, a 4.2 point VMAF increase, and a 37 percent drop in buffering events. Results were observed across multiple content types including open datasets and UGC, indicating production readiness.

Which benchmarks and metrics should teams use to evaluate AI codecs in 2025?

The MSU Video Codecs Comparisons 2025 remains a leading reference, combining objective and large scale subjective testing. Key metrics include VMAF, SSIM, and PSNR, complemented by panel based studies with more than ten thousand participants and hundreds of thousands of ratings. These methods provide statistically robust, real world quality signals.

What hardware and cost considerations impact deploying AI codecs at scale?

Encoding complexity varies, with AV1 and VVC reference encoders taking roughly three times and nine times HEVC respectively in some studies, while decoder complexity remains manageable. Growing availability of NPUs and edge modules makes on device or near edge AI preprocessing increasingly practical. Teams should balance GPU and cloud costs against bandwidth savings and viewer experience gains.

What is the outlook for AV2, VVC enhancements, and H.267 timelines?

AV2 is expected to deliver roughly 30 to 40 percent better compression than AV1, with initial silicon anticipated around 2026 and wider hardware support by 2028. The Enhanced Compression Model shows about 25 percent bitrate savings over VVC in random access and up to 40 percent for screen content. H.267 is projected to be finalized in 2028, with broad deployment likely in the mid 2030s.

Sources

The Best AI Video Codecs for Maximum File Size Savings (2025)

Why 2025 Is the Break-Out Year for AI Video Codecs

AI video codecs are finally production-ready in 2025, promising dramatic bitrate savings and file-size reduction for any streamer or storage team. The convergence of mature AI models, widespread NPU adoption, and proven deployments has created an inflection point for the industry.

Video is eating the internet—Cisco forecasts that it will represent 82% of all traffic, creating an urgent need to slash bitrate without denting quality. This explosive growth has made file size reduction and bitrate savings mission-critical for every organization handling video content.

Generative AI video models are advanced algorithms that enhance video quality by predicting and reconstructing details lost during compression. They work as a pre- and post-filter to encoders, saving bandwidth without sacrificing quality. The technology has matured rapidly: SimaBit achieved a 22% average reduction in bitrate, a 4.2-point VMAF quality increase, and a 37% decrease in buffering events in their tests.

AI-Enhanced Traditional Codecs: Getting More from H.264, HEVC & AV1

Rather than replacing existing infrastructure, AI preprocessing engines are supercharging traditional codecs to deliver unprecedented efficiency gains. This hybrid approach offers the best of both worlds: proven codec stability with AI-powered optimization.

AI-enhanced preprocessing engines are already demonstrating the ability to reduce video bandwidth requirements by 22% or more while boosting perceptual quality. These solutions work as intelligent filters that identify and remove perceptual redundancies before encoding, then reconstruct fine details during playback.

Generative AI video models act like a smart pre-filter in front of any encoder, predicting perceptual redundancies and reconstructing fine detail after compression. The result is 22%+ bitrate savings in Sima Labs benchmarks with visibly sharper frames. The technology achieves "20%+ Bitrate Savings" while maintaining compatibility with existing workflows.

SimaBit's Codec-Agnostic Engine

SimaBit represents a breakthrough in AI-powered video optimization, seamlessly integrating with all major codecs including H.264, HEVC, and AV1. Sima Labs today announced the seamless integration of SimaBit, its breakthrough AI-processing engine for bandwidth reduction, into Dolby Hybrik, one of the industry's widely used VOD transcoding platforms.

The platform slips in seamlessly, requiring no change to existing H.264, HEVC, or AV1 pipelines. The SDK is codec-agnostic, cloud-ready, and validated by VMAF/SSIM plus golden-eye studies across Netflix Open and YouTube UGC content. As the company reports, "20%+ Bitrate Savings" is achieved consistently across diverse content types.

SimaBit achieved a 22% average reduction in bitrate, a 4.2-point VMAF quality increase, and a 37% decrease in buffering events in their tests. This performance has been validated across multiple content types, establishing SimaBit as a production-ready solution for immediate deployment.

Learned & Neural Codecs Enter Production

While AI preprocessing enhances traditional codecs, a new generation of fully neural codecs is emerging from research labs into commercial deployment. These learned video compression systems represent a fundamental shift in how we approach video encoding.

GIViC (Generative Implicit Video Compression) exemplifies the cutting-edge of learned compression. The proposed model has been benchmarked against state-of-the-art conventional and neural codecs using a Random Access configuration, yielding BD-rate savings of 15.94%, 22.46% and 8.52% over VVC VTM, DCVC-FM and NVRC, respectively.

Deep Render recently introduced the first AI codec into FFmpeg and VLC, marking a watershed moment for neural codec adoption. AI codecs hold far more promise and opportunities than the initial 40% compression efficiency gains they have already achieved.

LVC is poised to be the next major advancement in video compression. The technology leverages end-to-end optimized neural models that learn compression strategies directly from data, rather than relying on hand-crafted algorithms.

Deep Render's Specialized AI Codec

Deep Render provides a 70% improvement over AV1 in talking heads, and a per-person specialized codec would add another 30%, leading to 5x improvement in video conferencing compared to AV1. This dramatic efficiency gain comes from training models on specific content domains.

Deep Render recently shipped the world's first AI codec to its customers. Their specialization approach can already provide a 30% efficiency gain on top of the 40% gain that Deep Render's codec already provides, leading to a total of approximately 50% compression efficiency gains.

How Do We Measure "Best"? 2025 Benchmarks & Metrics

Determining the best AI video codec requires rigorous, standardized testing across multiple quality metrics and use cases. The industry relies on several key benchmarking initiatives to provide objective comparisons.

The FullHD Objective and Subjective comparison 2025 represents the gold standard for codec evaluation. MSU's eighteenth annual comparison has become the definitive reference, with the team conducting video codec analysis, testing and optimization since 2004.

MSU Video Codecs Comparisons 2025 stands as the "#1 codecs comparisons in the world," providing comprehensive analysis across multiple dimensions. The comparison uses objective metrics including SSIM, PSNR, and VMAF to ensure thorough evaluation.

For subjective quality assessment, the latest comparison involved 10,800+ participants who provided 529,171 valid answers. This massive dataset ensures statistical significance and real-world relevance for the benchmark results.

Deployment Realities: Hardware, Edge GPUs & Workflow Integrations

Translating AI codec performance from benchmarks to production requires careful consideration of hardware requirements, integration complexity, and operational costs.

The AV1 and VVC reference encoders required averaged run times relative to HEVC reference encoder by a factor of more than three for AV1 and more than nine for VVC. However, decoder complexity remains manageable with averaged decoding run time relative to HEVC reference decoder of about 66% for AV1 and 182% for VVC.

The embedded Neural Processing Unit (NPU) market is experiencing robust growth, with market size in 2025 estimated at $15 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033. This hardware proliferation makes AI codec deployment increasingly practical.

The global Edge NPU Module market size is valued at USD 2.42 billion in 2024, and it is expected to reach USD 10.37 billion by 2033, expanding at a robust CAGR of 17.6%. This growth enables distributed AI processing for video workflows at scale.

Looking Ahead: AV2, VVC & H.267 on the Horizon

While current AI codecs deliver impressive results, the next generation of standards promises even greater efficiency gains through deeper AI integration.

AV2 could achieve 30-40% better compression than AV1 while maintaining comparable encoding complexity. The first AV2 silicon is likely to arrive in 2026, followed by software decoders the year after and hardware in 2028.

The H.267 standard is currently projected to be finalized between July and October 2028, with meaningful deployment likely not occurring until around 2034-2036. This timeline reflects the careful standardization process required for widespread adoption.

The Enhanced Compression Model (ECM) project has reached version 15, demonstrating roughly 25% bitrate savings over VVC in random-access configurations and up to 40% for screen-content sequences. These advances show the potential of AI-enhanced compression even within traditional codec frameworks.

Key Takeaways for Streaming, Storage & Beyond

The AI video codec revolution is here, delivering tangible benefits today while promising even greater advances tomorrow. Organizations can realize immediate gains through AI preprocessing while preparing for the neural codec future.

Our Technology Delivers Better Video Quality, Lower Bandwidth Requirements, and Reduced CDN Costs—all verified with industry standard quality metrics and Golden-eye subjective analysis. These benefits translate directly to improved user experience and reduced operational costs.

For teams ready to implement AI video optimization, SimaBit offers a proven path forward. The solution integrates seamlessly with existing workflows, requires no codec replacement, and delivers consistent 20%+ bitrate savings across diverse content types. Whether you're streaming live events, managing VOD libraries, or optimizing user-generated content, AI video codecs represent the most significant advancement in compression technology this decade.

The convergence of mature AI models, widespread hardware support, and production-ready solutions makes 2025 the ideal time to adopt AI video codecs. Start with hybrid approaches like SimaBit to gain immediate benefits while positioning your infrastructure for the fully neural future ahead.

Frequently Asked Questions

What is an AI video codec and how does it reduce file size?

AI video codecs and AI enhancements use learned models to predict and reconstruct details, allowing encoders to remove perceptual redundancies. In hybrid deployments, an AI preprocessor sits before H.264, HEVC, or AV1 to save bitrate while preserving perceived quality. Fully neural codecs learn compression end to end and are starting to reach production.

How does SimaBit integrate with existing H.264, HEVC, or AV1 workflows?

SimaBit operates as a codec-agnostic AI preprocessing engine that plugs into existing pipelines without replacing the encoder. It is available via SDK and integrates with Dolby Hybrik for VOD transcoding. According to Sima Labs resources, it is validated with VMAF and SSIM plus golden-eye reviews and consistently delivers 20 percent or more bitrate savings across diverse content.

What performance gains did Sima Labs report for SimaBit?

In tests, SimaBit achieved an average 22 percent bitrate reduction, a 4.2 point VMAF increase, and a 37 percent drop in buffering events. Results were observed across multiple content types including open datasets and UGC, indicating production readiness.

Which benchmarks and metrics should teams use to evaluate AI codecs in 2025?

The MSU Video Codecs Comparisons 2025 remains a leading reference, combining objective and large scale subjective testing. Key metrics include VMAF, SSIM, and PSNR, complemented by panel based studies with more than ten thousand participants and hundreds of thousands of ratings. These methods provide statistically robust, real world quality signals.

What hardware and cost considerations impact deploying AI codecs at scale?

Encoding complexity varies, with AV1 and VVC reference encoders taking roughly three times and nine times HEVC respectively in some studies, while decoder complexity remains manageable. Growing availability of NPUs and edge modules makes on device or near edge AI preprocessing increasingly practical. Teams should balance GPU and cloud costs against bandwidth savings and viewer experience gains.

What is the outlook for AV2, VVC enhancements, and H.267 timelines?

AV2 is expected to deliver roughly 30 to 40 percent better compression than AV1, with initial silicon anticipated around 2026 and wider hardware support by 2028. The Enhanced Compression Model shows about 25 percent bitrate savings over VVC in random access and up to 40 percent for screen content. H.267 is projected to be finalized in 2028, with broad deployment likely in the mid 2030s.

Sources

The Best AI Video Codecs for Maximum File Size Savings (2025)

Why 2025 Is the Break-Out Year for AI Video Codecs

AI video codecs are finally production-ready in 2025, promising dramatic bitrate savings and file-size reduction for any streamer or storage team. The convergence of mature AI models, widespread NPU adoption, and proven deployments has created an inflection point for the industry.

Video is eating the internet—Cisco forecasts that it will represent 82% of all traffic, creating an urgent need to slash bitrate without denting quality. This explosive growth has made file size reduction and bitrate savings mission-critical for every organization handling video content.

Generative AI video models are advanced algorithms that enhance video quality by predicting and reconstructing details lost during compression. They work as a pre- and post-filter to encoders, saving bandwidth without sacrificing quality. The technology has matured rapidly: SimaBit achieved a 22% average reduction in bitrate, a 4.2-point VMAF quality increase, and a 37% decrease in buffering events in their tests.

AI-Enhanced Traditional Codecs: Getting More from H.264, HEVC & AV1

Rather than replacing existing infrastructure, AI preprocessing engines are supercharging traditional codecs to deliver unprecedented efficiency gains. This hybrid approach offers the best of both worlds: proven codec stability with AI-powered optimization.

AI-enhanced preprocessing engines are already demonstrating the ability to reduce video bandwidth requirements by 22% or more while boosting perceptual quality. These solutions work as intelligent filters that identify and remove perceptual redundancies before encoding, then reconstruct fine details during playback.

Generative AI video models act like a smart pre-filter in front of any encoder, predicting perceptual redundancies and reconstructing fine detail after compression. The result is 22%+ bitrate savings in Sima Labs benchmarks with visibly sharper frames. The technology achieves "20%+ Bitrate Savings" while maintaining compatibility with existing workflows.

SimaBit's Codec-Agnostic Engine

SimaBit represents a breakthrough in AI-powered video optimization, seamlessly integrating with all major codecs including H.264, HEVC, and AV1. Sima Labs today announced the seamless integration of SimaBit, its breakthrough AI-processing engine for bandwidth reduction, into Dolby Hybrik, one of the industry's widely used VOD transcoding platforms.

The platform slips in seamlessly, requiring no change to existing H.264, HEVC, or AV1 pipelines. The SDK is codec-agnostic, cloud-ready, and validated by VMAF/SSIM plus golden-eye studies across Netflix Open and YouTube UGC content. As the company reports, "20%+ Bitrate Savings" is achieved consistently across diverse content types.

SimaBit achieved a 22% average reduction in bitrate, a 4.2-point VMAF quality increase, and a 37% decrease in buffering events in their tests. This performance has been validated across multiple content types, establishing SimaBit as a production-ready solution for immediate deployment.

Learned & Neural Codecs Enter Production

While AI preprocessing enhances traditional codecs, a new generation of fully neural codecs is emerging from research labs into commercial deployment. These learned video compression systems represent a fundamental shift in how we approach video encoding.

GIViC (Generative Implicit Video Compression) exemplifies the cutting-edge of learned compression. The proposed model has been benchmarked against state-of-the-art conventional and neural codecs using a Random Access configuration, yielding BD-rate savings of 15.94%, 22.46% and 8.52% over VVC VTM, DCVC-FM and NVRC, respectively.

Deep Render recently introduced the first AI codec into FFmpeg and VLC, marking a watershed moment for neural codec adoption. AI codecs hold far more promise and opportunities than the initial 40% compression efficiency gains they have already achieved.

LVC is poised to be the next major advancement in video compression. The technology leverages end-to-end optimized neural models that learn compression strategies directly from data, rather than relying on hand-crafted algorithms.

Deep Render's Specialized AI Codec

Deep Render provides a 70% improvement over AV1 in talking heads, and a per-person specialized codec would add another 30%, leading to 5x improvement in video conferencing compared to AV1. This dramatic efficiency gain comes from training models on specific content domains.

Deep Render recently shipped the world's first AI codec to its customers. Their specialization approach can already provide a 30% efficiency gain on top of the 40% gain that Deep Render's codec already provides, leading to a total of approximately 50% compression efficiency gains.

How Do We Measure "Best"? 2025 Benchmarks & Metrics

Determining the best AI video codec requires rigorous, standardized testing across multiple quality metrics and use cases. The industry relies on several key benchmarking initiatives to provide objective comparisons.

The FullHD Objective and Subjective comparison 2025 represents the gold standard for codec evaluation. MSU's eighteenth annual comparison has become the definitive reference, with the team conducting video codec analysis, testing and optimization since 2004.

MSU Video Codecs Comparisons 2025 stands as the "#1 codecs comparisons in the world," providing comprehensive analysis across multiple dimensions. The comparison uses objective metrics including SSIM, PSNR, and VMAF to ensure thorough evaluation.

For subjective quality assessment, the latest comparison involved 10,800+ participants who provided 529,171 valid answers. This massive dataset ensures statistical significance and real-world relevance for the benchmark results.

Deployment Realities: Hardware, Edge GPUs & Workflow Integrations

Translating AI codec performance from benchmarks to production requires careful consideration of hardware requirements, integration complexity, and operational costs.

The AV1 and VVC reference encoders required averaged run times relative to HEVC reference encoder by a factor of more than three for AV1 and more than nine for VVC. However, decoder complexity remains manageable with averaged decoding run time relative to HEVC reference decoder of about 66% for AV1 and 182% for VVC.

The embedded Neural Processing Unit (NPU) market is experiencing robust growth, with market size in 2025 estimated at $15 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033. This hardware proliferation makes AI codec deployment increasingly practical.

The global Edge NPU Module market size is valued at USD 2.42 billion in 2024, and it is expected to reach USD 10.37 billion by 2033, expanding at a robust CAGR of 17.6%. This growth enables distributed AI processing for video workflows at scale.

Looking Ahead: AV2, VVC & H.267 on the Horizon

While current AI codecs deliver impressive results, the next generation of standards promises even greater efficiency gains through deeper AI integration.

AV2 could achieve 30-40% better compression than AV1 while maintaining comparable encoding complexity. The first AV2 silicon is likely to arrive in 2026, followed by software decoders the year after and hardware in 2028.

The H.267 standard is currently projected to be finalized between July and October 2028, with meaningful deployment likely not occurring until around 2034-2036. This timeline reflects the careful standardization process required for widespread adoption.

The Enhanced Compression Model (ECM) project has reached version 15, demonstrating roughly 25% bitrate savings over VVC in random-access configurations and up to 40% for screen-content sequences. These advances show the potential of AI-enhanced compression even within traditional codec frameworks.

Key Takeaways for Streaming, Storage & Beyond

The AI video codec revolution is here, delivering tangible benefits today while promising even greater advances tomorrow. Organizations can realize immediate gains through AI preprocessing while preparing for the neural codec future.

Our Technology Delivers Better Video Quality, Lower Bandwidth Requirements, and Reduced CDN Costs—all verified with industry standard quality metrics and Golden-eye subjective analysis. These benefits translate directly to improved user experience and reduced operational costs.

For teams ready to implement AI video optimization, SimaBit offers a proven path forward. The solution integrates seamlessly with existing workflows, requires no codec replacement, and delivers consistent 20%+ bitrate savings across diverse content types. Whether you're streaming live events, managing VOD libraries, or optimizing user-generated content, AI video codecs represent the most significant advancement in compression technology this decade.

The convergence of mature AI models, widespread hardware support, and production-ready solutions makes 2025 the ideal time to adopt AI video codecs. Start with hybrid approaches like SimaBit to gain immediate benefits while positioning your infrastructure for the fully neural future ahead.

Frequently Asked Questions

What is an AI video codec and how does it reduce file size?

AI video codecs and AI enhancements use learned models to predict and reconstruct details, allowing encoders to remove perceptual redundancies. In hybrid deployments, an AI preprocessor sits before H.264, HEVC, or AV1 to save bitrate while preserving perceived quality. Fully neural codecs learn compression end to end and are starting to reach production.

How does SimaBit integrate with existing H.264, HEVC, or AV1 workflows?

SimaBit operates as a codec-agnostic AI preprocessing engine that plugs into existing pipelines without replacing the encoder. It is available via SDK and integrates with Dolby Hybrik for VOD transcoding. According to Sima Labs resources, it is validated with VMAF and SSIM plus golden-eye reviews and consistently delivers 20 percent or more bitrate savings across diverse content.

What performance gains did Sima Labs report for SimaBit?

In tests, SimaBit achieved an average 22 percent bitrate reduction, a 4.2 point VMAF increase, and a 37 percent drop in buffering events. Results were observed across multiple content types including open datasets and UGC, indicating production readiness.

Which benchmarks and metrics should teams use to evaluate AI codecs in 2025?

The MSU Video Codecs Comparisons 2025 remains a leading reference, combining objective and large scale subjective testing. Key metrics include VMAF, SSIM, and PSNR, complemented by panel based studies with more than ten thousand participants and hundreds of thousands of ratings. These methods provide statistically robust, real world quality signals.

What hardware and cost considerations impact deploying AI codecs at scale?

Encoding complexity varies, with AV1 and VVC reference encoders taking roughly three times and nine times HEVC respectively in some studies, while decoder complexity remains manageable. Growing availability of NPUs and edge modules makes on device or near edge AI preprocessing increasingly practical. Teams should balance GPU and cloud costs against bandwidth savings and viewer experience gains.

What is the outlook for AV2, VVC enhancements, and H.267 timelines?

AV2 is expected to deliver roughly 30 to 40 percent better compression than AV1, with initial silicon anticipated around 2026 and wider hardware support by 2028. The Enhanced Compression Model shows about 25 percent bitrate savings over VVC in random access and up to 40 percent for screen content. H.267 is projected to be finalized in 2028, with broad deployment likely in the mid 2030s.