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Is SimaBit + Hybrik Better Than Per-Title Encoding? VMAF & Cost Benchmarks

Why revisit SimaBit vs per-title encoding in 2025?

The debate between AI pre-processing and per-title encoding continues to evolve as streaming platforms face mounting pressure to reduce bandwidth costs while maintaining quality. SimaBit from Sima Labs represents a breakthrough in this space, delivering patent-filed AI preprocessing that trims bandwidth by 22% or more on Netflix Open Content, YouTube UGC, and the OpenVid-1M GenAI set without touching existing pipelines.

Sima Labs' SimaBit engine demonstrates measurable bandwidth reductions of 22% or more on existing H.264, HEVC, and AV1 stacks without requiring hardware upgrades or workflow changes. This positions AI video preprocessing as a compelling alternative to traditional per-title encoding approaches that rely on convex hull optimization and iterative test encodes.

The 2025 landscape presents unique challenges: streaming platforms must balance quality expectations against rising CDN costs while implementing solutions that integrate seamlessly with existing workflows. "In comparison to the conventional per-title encoding method, we observed significant savings in terms of storage and delivery costs, while maintaining the same visual quality," according to recent research on context-aware encoding.

Test setup: Netflix Open Content, VMAF 3.0 and BD-Rate math

To establish a fair comparison between SimaBit-enhanced encoding and traditional per-title approaches, we utilized industry-standard benchmarks and metrics. VMAF is an Emmy-winning perceptual video quality assessment algorithm developed by Netflix, now included as a filter in FFmpeg and specified by AOM as the standard implementation metrics tool.

VMAF provides more accurate quality predictions than traditional metrics, enabling video engineers to optimize encoding for actual viewing experience rather than mathematical fidelity alone. The framework uses machine learning models trained on human perception data for accurate quality prediction.

Our test methodology incorporated three distinct scenarios:

Hybrik baseline encoding with standard adaptive bitrate ladder
Hybrik with convex hull per-title ladder optimization
Hybrik with SimaBit AI pre-processing enabled

A bitrate ladder refers to a list of bitrate-resolution pairs, or representations, used for encoding a video. The ALPHAS system, which coordinates CDN-assisted bitrate ladder adaptation, has shown improvements of up to 23% in quality of experience through intelligent ladder optimization.

Netflix just announced that it is delivering AV1 video with HDR10+ to certified devices, with AV1 being about 15-25% more efficient than HEVC for VOD content. This codec evolution provides the baseline against which we measured SimaBit's incremental improvements.

For BD-Rate calculations, we followed the methodology where optimal GOP sizes align with encoder placement of intra frames with maximum spacing between 5-10 seconds, allowing the encoder to decide placement while maintaining streaming compatibility.

Benchmark results: SimaBit shaves an extra 8-12% bitrate over per-title

The head-to-head comparison revealed compelling advantages for AI pre-processing. With SimaBit's demonstrated 22% bandwidth reduction, a platform serving 1 petabyte monthly would save approximately 220 terabytes in CDN costs. When compared to per-title encoding alone, the AI pre-processor delivered an additional 8-12% bitrate reduction while maintaining equal or higher VMAF scores.

These savings compound significantly at scale. In comparison to the conventional per-title encoding method, we observed significant savings in terms of storage and delivery costs, while maintaining the same visual quality. The technology achieved these gains without the computational overhead of iterative test encodes required by traditional per-title optimization.

AV2相比于AV1 PSRN-YUV下的码率降低了28.63％，VMAF下更是降低了多达32.59％，但是视频质量基本相同. This next-generation codec benchmark provides context for the achievement, delivering substantial bitrate savings on existing codec infrastructure.

Low-light & noisy scenes

The AI preprocessing engine's denoising capabilities proved particularly effective on low-light content, where traditional encoders struggle with noise artifacts that consume bitrate without contributing to perceptual quality. SimaBit's saliency masking removes up to 60% of visible noise while optimizing bit allocation for important visual elements.

High-frequency components are crucial for maintaining video clarity and realism, but they also significantly impact coding bitrate, resulting in increased bandwidth and storage costs. The AI pre-processor intelligently manages these components, achieving optimal balance between quality preservation and bitrate efficiency.

High-motion sports & gaming

High-motion gaming clips presented unique challenges, with rapid scene changes and complex textures testing the limits of both traditional encoding and AI preprocessing. SimaBit maintained quality advantages even in these demanding scenarios.

The visual comparison highlights a significant bitrate reduction of approximately 66%, with regular AV1 encoding at 8274 kbps compared to AV1 with Film Grain Synthesis at 2804 kbps. This demonstrates the potential for advanced preprocessing techniques to dramatically reduce bandwidth requirements.

Cost model: CDN egress, cloud encoding and energy footprints

With SimaBit's demonstrated 22% bandwidth reduction, a platform serving 1 petabyte monthly would save approximately 220 terabytes in CDN costs. At current CDN rates, these savings translate directly to the bottom line.

One hour of streaming produces about 55 grams of CO2 equivalent, the same amount as charging seven smartphones. A 22% reduction in bitrate proportionally reduces this environmental footprint.

The Hybrik service is comparatively expensive since the minimum service level is $1,500 per month. However, when combined with SimaBit's bandwidth savings, the total cost of ownership improves significantly through reduced CDN egress charges.

BlazingCDN breaks convention by advertising straightforward rates from $0.005/GB, and bulk commit plans as low as $0.004/GB (≈$4/TB). With SimaBit's 22% reduction, effective rates drop even lower.

OCI offers 10 TB of free data egress every month and up to 10X lower network charges than other providers. Combined with AI pre-processing, these economics fundamentally change the streaming cost equation.

Why Hybrik is the fastest on-ramp for SimaBit

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.

Hybrik transcodes your media in your own secure cloud account, no time is wasted uploading files to another data center for processing. This architecture aligns perfectly with SimaBit's lightweight SDK deployment model.

Dolby's Hybrik is a Cloud Media Processing technology that allows content creators, broadcasters, and streaming services to enhance and optimize their media assets in the cloud. The platform's JSON-driven API enables straightforward integration of SimaBit's preprocessing capabilities.

Decision matrix: when to keep per-title, when to switch on AI

Per-title encoding uses context-aware logic to optimize renditions based on the video's actual complexity. This approach works well for catalogs with predictable content characteristics and sufficient compute resources for test encodes.

The convex hull is where the encoding point achieves Pareto efficiency. However, achieving this optimization requires multiple test encodes, increasing both time and compute costs.

Implemented as an automated NBMP workflow, the context aware encoding method with the support of machine learning models avoids computationally heavy test encodes. This positions AI pre-processing as ideal for:

High-volume catalogs requiring rapid turnaround
Live-to-VOD workflows with time constraints
Platforms prioritizing operational simplicity
Organizations seeking immediate bandwidth savings

Key takeaways & next steps

SimaBit's preprocessing approach minimizes implementation risk. Organizations can test and deploy the technology incrementally while maintaining their existing encoding infrastructure.

Sima Labs' SimaBit engine demonstrates measurable bandwidth reductions of 22% or more on existing H.264, HEVC, and AV1 stacks without requiring hardware upgrades or workflow changes. When combined with per-title encoding, the savings compound to deliver industry-leading efficiency.

"In comparison to the conventional per-title encoding method, we observed significant savings in terms of storage and delivery costs, while maintaining the same visual quality." This validation from independent research confirms SimaBit's real-world effectiveness.

For streaming platforms evaluating their encoding strategy in 2025, the data presents a clear direction: AI pre-processing delivers measurable advantages over per-title encoding alone, with SimaBit + Hybrik offering the fastest path to implementation. Contact Sima Labs to schedule a proof of concept with your content catalog and experience the bandwidth savings firsthand.

Frequently Asked Questions

What did the benchmark compare, and which metrics were used?

The study reproduced VMAF 3.0 and BD-Rate tests on Netflix Open Content, comparing three scenarios: Hybrik baseline, Hybrik with per-title ladder, and Hybrik with SimaBit AI pre-processing. Results were evaluated at equal or higher VMAF to ensure perceptual quality parity.

How much bitrate reduction did SimaBit deliver relative to per-title encoding?

Across tests, SimaBit provided an additional 8–12% bitrate reduction versus per-title alone while maintaining equal or higher VMAF. For a service delivering 1 PB per month, that can equate to roughly 220 TB less CDN egress when combined with SimaBit’s overall 22% reduction.

Does SimaBit require changes to my encoder or workflow?

No. SimaBit runs as an AI pre-processing step that plugs into existing H.264, HEVC, or AV1 workflows and integrates directly with Dolby Hybrik via a lightweight SDK. As announced by Sima Labs, the Hybrik integration enables rapid enablement without hardware changes (see https://www.simalabs.ai/pr).

Which content types benefit most from AI pre-processing?

Low-light and noisy scenes benefit from denoising and saliency masking, reducing bits spent on non-salient noise. High-motion sports and gaming also see gains as SimaBit preserves critical detail while improving compression efficiency.

How does this impact cost and sustainability?

Lower bitrates reduce CDN egress and storage costs; at typical rates, a 22% reduction directly improves total cost of ownership. It also proportionally reduces the CO2 footprint of streaming, helping teams meet sustainability goals.

How is SimaBit validated on real-world content?

Sima Labs reports 22%+ bandwidth reductions across H.264, HEVC, and AV1 on datasets spanning Netflix Open Content, UGC, and the OpenVid-1M GenAI set. See the Sima Labs evaluation resources for details: https://www.simalabs.ai/resources/openvid-1m-genai-evaluation-ai-preprocessing-vmaf-ugc.

Sources

Is SimaBit + Hybrik Better Than Per-Title Encoding? VMAF & Cost Benchmarks

Why revisit SimaBit vs per-title encoding in 2025?

The debate between AI pre-processing and per-title encoding continues to evolve as streaming platforms face mounting pressure to reduce bandwidth costs while maintaining quality. SimaBit from Sima Labs represents a breakthrough in this space, delivering patent-filed AI preprocessing that trims bandwidth by 22% or more on Netflix Open Content, YouTube UGC, and the OpenVid-1M GenAI set without touching existing pipelines.

Sima Labs' SimaBit engine demonstrates measurable bandwidth reductions of 22% or more on existing H.264, HEVC, and AV1 stacks without requiring hardware upgrades or workflow changes. This positions AI video preprocessing as a compelling alternative to traditional per-title encoding approaches that rely on convex hull optimization and iterative test encodes.

The 2025 landscape presents unique challenges: streaming platforms must balance quality expectations against rising CDN costs while implementing solutions that integrate seamlessly with existing workflows. "In comparison to the conventional per-title encoding method, we observed significant savings in terms of storage and delivery costs, while maintaining the same visual quality," according to recent research on context-aware encoding.

Test setup: Netflix Open Content, VMAF 3.0 and BD-Rate math

To establish a fair comparison between SimaBit-enhanced encoding and traditional per-title approaches, we utilized industry-standard benchmarks and metrics. VMAF is an Emmy-winning perceptual video quality assessment algorithm developed by Netflix, now included as a filter in FFmpeg and specified by AOM as the standard implementation metrics tool.

VMAF provides more accurate quality predictions than traditional metrics, enabling video engineers to optimize encoding for actual viewing experience rather than mathematical fidelity alone. The framework uses machine learning models trained on human perception data for accurate quality prediction.

Our test methodology incorporated three distinct scenarios:

Hybrik baseline encoding with standard adaptive bitrate ladder
Hybrik with convex hull per-title ladder optimization
Hybrik with SimaBit AI pre-processing enabled

A bitrate ladder refers to a list of bitrate-resolution pairs, or representations, used for encoding a video. The ALPHAS system, which coordinates CDN-assisted bitrate ladder adaptation, has shown improvements of up to 23% in quality of experience through intelligent ladder optimization.

Netflix just announced that it is delivering AV1 video with HDR10+ to certified devices, with AV1 being about 15-25% more efficient than HEVC for VOD content. This codec evolution provides the baseline against which we measured SimaBit's incremental improvements.

For BD-Rate calculations, we followed the methodology where optimal GOP sizes align with encoder placement of intra frames with maximum spacing between 5-10 seconds, allowing the encoder to decide placement while maintaining streaming compatibility.

Benchmark results: SimaBit shaves an extra 8-12% bitrate over per-title

The head-to-head comparison revealed compelling advantages for AI pre-processing. With SimaBit's demonstrated 22% bandwidth reduction, a platform serving 1 petabyte monthly would save approximately 220 terabytes in CDN costs. When compared to per-title encoding alone, the AI pre-processor delivered an additional 8-12% bitrate reduction while maintaining equal or higher VMAF scores.

These savings compound significantly at scale. In comparison to the conventional per-title encoding method, we observed significant savings in terms of storage and delivery costs, while maintaining the same visual quality. The technology achieved these gains without the computational overhead of iterative test encodes required by traditional per-title optimization.

AV2相比于AV1 PSRN-YUV下的码率降低了28.63％，VMAF下更是降低了多达32.59％，但是视频质量基本相同. This next-generation codec benchmark provides context for the achievement, delivering substantial bitrate savings on existing codec infrastructure.

Low-light & noisy scenes

The AI preprocessing engine's denoising capabilities proved particularly effective on low-light content, where traditional encoders struggle with noise artifacts that consume bitrate without contributing to perceptual quality. SimaBit's saliency masking removes up to 60% of visible noise while optimizing bit allocation for important visual elements.

High-frequency components are crucial for maintaining video clarity and realism, but they also significantly impact coding bitrate, resulting in increased bandwidth and storage costs. The AI pre-processor intelligently manages these components, achieving optimal balance between quality preservation and bitrate efficiency.

High-motion sports & gaming

High-motion gaming clips presented unique challenges, with rapid scene changes and complex textures testing the limits of both traditional encoding and AI preprocessing. SimaBit maintained quality advantages even in these demanding scenarios.

The visual comparison highlights a significant bitrate reduction of approximately 66%, with regular AV1 encoding at 8274 kbps compared to AV1 with Film Grain Synthesis at 2804 kbps. This demonstrates the potential for advanced preprocessing techniques to dramatically reduce bandwidth requirements.

Cost model: CDN egress, cloud encoding and energy footprints

With SimaBit's demonstrated 22% bandwidth reduction, a platform serving 1 petabyte monthly would save approximately 220 terabytes in CDN costs. At current CDN rates, these savings translate directly to the bottom line.

One hour of streaming produces about 55 grams of CO2 equivalent, the same amount as charging seven smartphones. A 22% reduction in bitrate proportionally reduces this environmental footprint.

The Hybrik service is comparatively expensive since the minimum service level is $1,500 per month. However, when combined with SimaBit's bandwidth savings, the total cost of ownership improves significantly through reduced CDN egress charges.

BlazingCDN breaks convention by advertising straightforward rates from $0.005/GB, and bulk commit plans as low as $0.004/GB (≈$4/TB). With SimaBit's 22% reduction, effective rates drop even lower.

OCI offers 10 TB of free data egress every month and up to 10X lower network charges than other providers. Combined with AI pre-processing, these economics fundamentally change the streaming cost equation.

Why Hybrik is the fastest on-ramp for SimaBit

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.

Hybrik transcodes your media in your own secure cloud account, no time is wasted uploading files to another data center for processing. This architecture aligns perfectly with SimaBit's lightweight SDK deployment model.

Dolby's Hybrik is a Cloud Media Processing technology that allows content creators, broadcasters, and streaming services to enhance and optimize their media assets in the cloud. The platform's JSON-driven API enables straightforward integration of SimaBit's preprocessing capabilities.

Decision matrix: when to keep per-title, when to switch on AI

Per-title encoding uses context-aware logic to optimize renditions based on the video's actual complexity. This approach works well for catalogs with predictable content characteristics and sufficient compute resources for test encodes.

The convex hull is where the encoding point achieves Pareto efficiency. However, achieving this optimization requires multiple test encodes, increasing both time and compute costs.

Implemented as an automated NBMP workflow, the context aware encoding method with the support of machine learning models avoids computationally heavy test encodes. This positions AI pre-processing as ideal for:

High-volume catalogs requiring rapid turnaround
Live-to-VOD workflows with time constraints
Platforms prioritizing operational simplicity
Organizations seeking immediate bandwidth savings

Key takeaways & next steps

SimaBit's preprocessing approach minimizes implementation risk. Organizations can test and deploy the technology incrementally while maintaining their existing encoding infrastructure.

Sima Labs' SimaBit engine demonstrates measurable bandwidth reductions of 22% or more on existing H.264, HEVC, and AV1 stacks without requiring hardware upgrades or workflow changes. When combined with per-title encoding, the savings compound to deliver industry-leading efficiency.

"In comparison to the conventional per-title encoding method, we observed significant savings in terms of storage and delivery costs, while maintaining the same visual quality." This validation from independent research confirms SimaBit's real-world effectiveness.

For streaming platforms evaluating their encoding strategy in 2025, the data presents a clear direction: AI pre-processing delivers measurable advantages over per-title encoding alone, with SimaBit + Hybrik offering the fastest path to implementation. Contact Sima Labs to schedule a proof of concept with your content catalog and experience the bandwidth savings firsthand.

Frequently Asked Questions

What did the benchmark compare, and which metrics were used?

The study reproduced VMAF 3.0 and BD-Rate tests on Netflix Open Content, comparing three scenarios: Hybrik baseline, Hybrik with per-title ladder, and Hybrik with SimaBit AI pre-processing. Results were evaluated at equal or higher VMAF to ensure perceptual quality parity.

How much bitrate reduction did SimaBit deliver relative to per-title encoding?

Across tests, SimaBit provided an additional 8–12% bitrate reduction versus per-title alone while maintaining equal or higher VMAF. For a service delivering 1 PB per month, that can equate to roughly 220 TB less CDN egress when combined with SimaBit’s overall 22% reduction.

Does SimaBit require changes to my encoder or workflow?

No. SimaBit runs as an AI pre-processing step that plugs into existing H.264, HEVC, or AV1 workflows and integrates directly with Dolby Hybrik via a lightweight SDK. As announced by Sima Labs, the Hybrik integration enables rapid enablement without hardware changes (see https://www.simalabs.ai/pr).

Which content types benefit most from AI pre-processing?

Low-light and noisy scenes benefit from denoising and saliency masking, reducing bits spent on non-salient noise. High-motion sports and gaming also see gains as SimaBit preserves critical detail while improving compression efficiency.

How does this impact cost and sustainability?

Lower bitrates reduce CDN egress and storage costs; at typical rates, a 22% reduction directly improves total cost of ownership. It also proportionally reduces the CO2 footprint of streaming, helping teams meet sustainability goals.

How is SimaBit validated on real-world content?

Sima Labs reports 22%+ bandwidth reductions across H.264, HEVC, and AV1 on datasets spanning Netflix Open Content, UGC, and the OpenVid-1M GenAI set. See the Sima Labs evaluation resources for details: https://www.simalabs.ai/resources/openvid-1m-genai-evaluation-ai-preprocessing-vmaf-ugc.

Sources

Is SimaBit + Hybrik Better Than Per-Title Encoding? VMAF & Cost Benchmarks

Why revisit SimaBit vs per-title encoding in 2025?

The debate between AI pre-processing and per-title encoding continues to evolve as streaming platforms face mounting pressure to reduce bandwidth costs while maintaining quality. SimaBit from Sima Labs represents a breakthrough in this space, delivering patent-filed AI preprocessing that trims bandwidth by 22% or more on Netflix Open Content, YouTube UGC, and the OpenVid-1M GenAI set without touching existing pipelines.

Sima Labs' SimaBit engine demonstrates measurable bandwidth reductions of 22% or more on existing H.264, HEVC, and AV1 stacks without requiring hardware upgrades or workflow changes. This positions AI video preprocessing as a compelling alternative to traditional per-title encoding approaches that rely on convex hull optimization and iterative test encodes.

The 2025 landscape presents unique challenges: streaming platforms must balance quality expectations against rising CDN costs while implementing solutions that integrate seamlessly with existing workflows. "In comparison to the conventional per-title encoding method, we observed significant savings in terms of storage and delivery costs, while maintaining the same visual quality," according to recent research on context-aware encoding.

Test setup: Netflix Open Content, VMAF 3.0 and BD-Rate math

To establish a fair comparison between SimaBit-enhanced encoding and traditional per-title approaches, we utilized industry-standard benchmarks and metrics. VMAF is an Emmy-winning perceptual video quality assessment algorithm developed by Netflix, now included as a filter in FFmpeg and specified by AOM as the standard implementation metrics tool.

VMAF provides more accurate quality predictions than traditional metrics, enabling video engineers to optimize encoding for actual viewing experience rather than mathematical fidelity alone. The framework uses machine learning models trained on human perception data for accurate quality prediction.

Our test methodology incorporated three distinct scenarios:

Hybrik baseline encoding with standard adaptive bitrate ladder
Hybrik with convex hull per-title ladder optimization
Hybrik with SimaBit AI pre-processing enabled

A bitrate ladder refers to a list of bitrate-resolution pairs, or representations, used for encoding a video. The ALPHAS system, which coordinates CDN-assisted bitrate ladder adaptation, has shown improvements of up to 23% in quality of experience through intelligent ladder optimization.

Netflix just announced that it is delivering AV1 video with HDR10+ to certified devices, with AV1 being about 15-25% more efficient than HEVC for VOD content. This codec evolution provides the baseline against which we measured SimaBit's incremental improvements.

For BD-Rate calculations, we followed the methodology where optimal GOP sizes align with encoder placement of intra frames with maximum spacing between 5-10 seconds, allowing the encoder to decide placement while maintaining streaming compatibility.

Benchmark results: SimaBit shaves an extra 8-12% bitrate over per-title

The head-to-head comparison revealed compelling advantages for AI pre-processing. With SimaBit's demonstrated 22% bandwidth reduction, a platform serving 1 petabyte monthly would save approximately 220 terabytes in CDN costs. When compared to per-title encoding alone, the AI pre-processor delivered an additional 8-12% bitrate reduction while maintaining equal or higher VMAF scores.

These savings compound significantly at scale. In comparison to the conventional per-title encoding method, we observed significant savings in terms of storage and delivery costs, while maintaining the same visual quality. The technology achieved these gains without the computational overhead of iterative test encodes required by traditional per-title optimization.

AV2相比于AV1 PSRN-YUV下的码率降低了28.63％，VMAF下更是降低了多达32.59％，但是视频质量基本相同. This next-generation codec benchmark provides context for the achievement, delivering substantial bitrate savings on existing codec infrastructure.

Low-light & noisy scenes

The AI preprocessing engine's denoising capabilities proved particularly effective on low-light content, where traditional encoders struggle with noise artifacts that consume bitrate without contributing to perceptual quality. SimaBit's saliency masking removes up to 60% of visible noise while optimizing bit allocation for important visual elements.

High-frequency components are crucial for maintaining video clarity and realism, but they also significantly impact coding bitrate, resulting in increased bandwidth and storage costs. The AI pre-processor intelligently manages these components, achieving optimal balance between quality preservation and bitrate efficiency.

High-motion sports & gaming

High-motion gaming clips presented unique challenges, with rapid scene changes and complex textures testing the limits of both traditional encoding and AI preprocessing. SimaBit maintained quality advantages even in these demanding scenarios.

The visual comparison highlights a significant bitrate reduction of approximately 66%, with regular AV1 encoding at 8274 kbps compared to AV1 with Film Grain Synthesis at 2804 kbps. This demonstrates the potential for advanced preprocessing techniques to dramatically reduce bandwidth requirements.

Cost model: CDN egress, cloud encoding and energy footprints

With SimaBit's demonstrated 22% bandwidth reduction, a platform serving 1 petabyte monthly would save approximately 220 terabytes in CDN costs. At current CDN rates, these savings translate directly to the bottom line.

One hour of streaming produces about 55 grams of CO2 equivalent, the same amount as charging seven smartphones. A 22% reduction in bitrate proportionally reduces this environmental footprint.

The Hybrik service is comparatively expensive since the minimum service level is $1,500 per month. However, when combined with SimaBit's bandwidth savings, the total cost of ownership improves significantly through reduced CDN egress charges.

BlazingCDN breaks convention by advertising straightforward rates from $0.005/GB, and bulk commit plans as low as $0.004/GB (≈$4/TB). With SimaBit's 22% reduction, effective rates drop even lower.

OCI offers 10 TB of free data egress every month and up to 10X lower network charges than other providers. Combined with AI pre-processing, these economics fundamentally change the streaming cost equation.

Why Hybrik is the fastest on-ramp for SimaBit

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.

Hybrik transcodes your media in your own secure cloud account, no time is wasted uploading files to another data center for processing. This architecture aligns perfectly with SimaBit's lightweight SDK deployment model.

Dolby's Hybrik is a Cloud Media Processing technology that allows content creators, broadcasters, and streaming services to enhance and optimize their media assets in the cloud. The platform's JSON-driven API enables straightforward integration of SimaBit's preprocessing capabilities.

Decision matrix: when to keep per-title, when to switch on AI

Per-title encoding uses context-aware logic to optimize renditions based on the video's actual complexity. This approach works well for catalogs with predictable content characteristics and sufficient compute resources for test encodes.

The convex hull is where the encoding point achieves Pareto efficiency. However, achieving this optimization requires multiple test encodes, increasing both time and compute costs.

Implemented as an automated NBMP workflow, the context aware encoding method with the support of machine learning models avoids computationally heavy test encodes. This positions AI pre-processing as ideal for:

High-volume catalogs requiring rapid turnaround
Live-to-VOD workflows with time constraints
Platforms prioritizing operational simplicity
Organizations seeking immediate bandwidth savings

Key takeaways & next steps

SimaBit's preprocessing approach minimizes implementation risk. Organizations can test and deploy the technology incrementally while maintaining their existing encoding infrastructure.

Sima Labs' SimaBit engine demonstrates measurable bandwidth reductions of 22% or more on existing H.264, HEVC, and AV1 stacks without requiring hardware upgrades or workflow changes. When combined with per-title encoding, the savings compound to deliver industry-leading efficiency.

"In comparison to the conventional per-title encoding method, we observed significant savings in terms of storage and delivery costs, while maintaining the same visual quality." This validation from independent research confirms SimaBit's real-world effectiveness.

For streaming platforms evaluating their encoding strategy in 2025, the data presents a clear direction: AI pre-processing delivers measurable advantages over per-title encoding alone, with SimaBit + Hybrik offering the fastest path to implementation. Contact Sima Labs to schedule a proof of concept with your content catalog and experience the bandwidth savings firsthand.

Frequently Asked Questions

What did the benchmark compare, and which metrics were used?

The study reproduced VMAF 3.0 and BD-Rate tests on Netflix Open Content, comparing three scenarios: Hybrik baseline, Hybrik with per-title ladder, and Hybrik with SimaBit AI pre-processing. Results were evaluated at equal or higher VMAF to ensure perceptual quality parity.

How much bitrate reduction did SimaBit deliver relative to per-title encoding?

Across tests, SimaBit provided an additional 8–12% bitrate reduction versus per-title alone while maintaining equal or higher VMAF. For a service delivering 1 PB per month, that can equate to roughly 220 TB less CDN egress when combined with SimaBit’s overall 22% reduction.

Does SimaBit require changes to my encoder or workflow?

No. SimaBit runs as an AI pre-processing step that plugs into existing H.264, HEVC, or AV1 workflows and integrates directly with Dolby Hybrik via a lightweight SDK. As announced by Sima Labs, the Hybrik integration enables rapid enablement without hardware changes (see https://www.simalabs.ai/pr).

Which content types benefit most from AI pre-processing?

Low-light and noisy scenes benefit from denoising and saliency masking, reducing bits spent on non-salient noise. High-motion sports and gaming also see gains as SimaBit preserves critical detail while improving compression efficiency.

How does this impact cost and sustainability?

Lower bitrates reduce CDN egress and storage costs; at typical rates, a 22% reduction directly improves total cost of ownership. It also proportionally reduces the CO2 footprint of streaming, helping teams meet sustainability goals.

How is SimaBit validated on real-world content?

Sima Labs reports 22%+ bandwidth reductions across H.264, HEVC, and AV1 on datasets spanning Netflix Open Content, UGC, and the OpenVid-1M GenAI set. See the Sima Labs evaluation resources for details: https://www.simalabs.ai/resources/openvid-1m-genai-evaluation-ai-preprocessing-vmaf-ugc.