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Bandwidth vs Quality: Why 22 % Less Data Doesn’t Mean Fuzzy Video on Nugs
Bandwidth vs Quality: Why 22% Less Data Doesn't Mean Fuzzy Video on Nugs
Streaming engineers still frame the debate as bandwidth vs quality, yet 2025 data shows you can drop bitrate without degrading the picture.
Why "bandwidth vs quality" is a false trade-off in 2025
The traditional assumption that reducing bandwidth automatically means sacrificing video quality no longer holds true. AI preprocessing represents a fundamentally different approach to video optimization that enhances encoder performance by intelligently preparing video content before encoding.
Modern AI-powered preprocessing engines are delivering measurable bandwidth reductions of 22% or more on existing H.264, HEVC, and AV1 stacks without requiring hardware upgrades or workflow changes. This breakthrough challenges the conventional wisdom that viewers must accept fuzzy video to save on data costs.
The impact on viewer engagement is significant. Research shows that a 1% increase in buffering ratio can reduce average play time by more than 3 minutes for a 90-minute stream. By reducing bandwidth requirements without compromising quality, preprocessing technology addresses both technical and business challenges simultaneously.
Perceptual preprocessing 101: cleaning frames before they hit the encoder
Perceptual preprocessing works by analyzing video content before it reaches the encoder, identifying visual patterns that the human eye won't notice and optimizing bit allocation accordingly. SimaBit from Sima Labs delivers 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.
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. By removing these imperceptible elements before encoding begins, the system ensures that every bit counts toward actual visual quality.
Crucially, SimaBit processes 1080p frames in under 16 milliseconds, making it suitable for both live streaming applications and video-on-demand workflows. This speed ensures that preprocessing doesn't become a bottleneck in production pipelines.
Live & VOD ready (< 16 ms per 1080p frame)
The sub-16 millisecond processing time for 1080p frames ensures that SimaBit can handle the most demanding streaming scenarios. Whether you're broadcasting live sports, gaming tournaments, or processing vast VOD libraries, this latency fits comfortably within existing encoding budgets without introducing delays that would impact viewer experience.
Proving it: VMAF and friends replace guesswork with science
VMAF represents the cutting edge of perceptual video quality assessment, combining machine learning with human visual perception modeling. Unlike traditional metrics that measure mathematical differences, VMAF predicts how actual viewers will perceive quality changes.
The percentage of time spent in buffering has the largest impact on user engagement across all content types. By reducing bandwidth requirements while maintaining high VMAF scores, preprocessing technology directly addresses this critical metric.
VQ-TIF estimates VMAF with a Pearson Correlation Coefficient of 0.96 and a Mean Absolute Error of 2.71, demonstrating that modern quality assessment can accurately predict viewer perception at scale. This scientific validation replaces subjective guesswork with data-driven optimization.
Why VMAF beats PSNR & SSIM for perceptual truth
VMAF stands out because it bridges the gap between technical measurements and real-world viewing. While PSNR measures pixel-level accuracy and SSIM evaluates structural similarity, neither fully captures how humans actually perceive video quality.
Analysis of 33 existing image and video quality metrics found that LPIPS and MS-SSIM excel at predicting contrast masking, while VMAF's machine learning approach better models the complete viewing experience. This perceptual focus ensures that bandwidth savings translate to genuine efficiency gains rather than degraded viewer satisfaction.
Business upside: less egress, lower churn, greener delivery
With SimaBit's demonstrated 22% bandwidth reduction, a platform serving 1 petabyte monthly would save approximately 220 terabytes in CDN costs. These savings compound when combined with reduced infrastructure requirements and lower energy consumption.
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. These improvements directly impact viewer retention and platform economics.
Re-buffering is one of the most common factors that reduce user Quality of Experience in online streaming video. By addressing bandwidth efficiency at the preprocessing stage, platforms can maintain competitive streaming quality while significantly reducing operational costs.
A 1 PB/month example
A platform serving 1 petabyte monthly would save approximately 220 terabytes in CDN costs with SimaBit's 22% bandwidth reduction. At typical CDN rates of $0.02-0.05 per GB, this translates to monthly savings of $4,400-11,000, or $52,800-132,000 annually. For platforms operating at larger scales, these savings can fund additional content acquisition or infrastructure improvements.
Dataset results: Netflix Open, YouTube UGC & AV2 synergy
SimaBit has been benchmarked on Netflix Open Content, YouTube UGC, and the OpenVid-1M GenAI video set, with verification via VMAF/SSIM metrics and golden-eye subjective studies. This comprehensive testing across diverse content types demonstrates consistent performance improvements.
AV2 shows around 30% lower bitrate than AV1 at the same quality. Combined with SimaBit preprocessing, teams report 28.63% bitrate reduction compared to AV1 in PSNR-YUV results and 32.59% in VMAF measurements. This synergy between preprocessing and next-generation codecs multiplies efficiency gains.
SimaBit's saliency masking removes up to 60% of visible noise while optimizing bit allocation for important visual elements. This targeted approach ensures that bandwidth savings come from removing imperceptible elements rather than compromising critical visual information.
Plug-in path: deploying SimaBit without rocking the boat
The preprocessing engine slips in front of any encoder without requiring changes to downstream systems, player compatibility, or content delivery networks. This seamless integration preserves existing workflows while adding AI-powered optimization.
Significantly improved time-to-market for post-live video content becomes possible when preprocessing handles optimization automatically. Teams can focus on content creation rather than manual encoding parameter tuning.
Globo's round trip time is just 10 milliseconds, demonstrating that cloud-based preprocessing can meet the stringent latency requirements of live production environments. This real-world validation shows that advanced preprocessing doesn't require on-premises infrastructure.
The bandwidth vs quality debate is over—here's what's next
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, ensuring that today's infrastructure investments remain valuable tomorrow.
The evidence is clear: modern perceptual preprocessing delivers measurable bandwidth savings without sacrificing visual quality. With proven reductions of 22% or more across diverse content types, validated by industry-standard VMAF metrics, and seamless integration with existing workflows, the traditional trade-off between bandwidth and quality no longer applies.
For streaming platforms looking to reduce CDN costs while maintaining competitive quality, SimaBit from Sima Labs offers a proven path forward. The technology's ability to enhance existing codec performance while preparing for future standards makes it an essential component of modern video infrastructure.
Frequently Asked Questions
How can 22% less bandwidth not reduce video quality?
Perceptual preprocessing removes noise and artifacts the human eye won’t notice, letting encoders spend bits where it matters. In SimaBit tests, platforms maintained or improved VMAF while cutting bitrate ~22%, so viewers see crisp video with fewer stalls.
What is perceptual preprocessing and how does SimaBit use it?
SimaBit analyzes each frame for saliency, denoises low-light content, and reallocates bits toward important details before encoding. This AI-first pass improves encoder efficiency across H.264, HEVC, and AV1 without changing downstream players or CDNs.
Is SimaBit ready for live as well as VOD workflows?
Yes. SimaBit processes 1080p frames in under 16 ms, fitting comfortably within typical live encoding budgets, and scales for large VOD libraries without becoming a bottleneck.
How do you validate that quality is preserved?
We use perceptual metrics such as VMAF, supported by models like VQ-TIF that correlate strongly with human opinion scores. The result is data-driven proof that bitrate reductions preserve perceived quality, not just pixel math.
Does SimaBit require new hardware or encoder changes?
No. SimaBit sits ahead of your existing encoder as a plug-in step and works with current H.264, HEVC, and AV1 stacks. It’s also available via Dolby Hybrik for immediate deployment (see https://www.simalabs.ai/pr).
What business impact can I expect from a 22% reduction?
A platform serving 1 PB per month would save about 220 TB of egress, translating to roughly $4,400–$11,000 in monthly CDN savings at common rates. Reduced rebuffering and improved VMAF also help lower churn and improve watch time.
Sources
https://www.simalabs.ai/resources/openvid-1m-genai-evaluation-ai-preprocessing-vmaf-ugc
https://probe.dev/resources/vmaf-perceptual-quality-analysis
https://www.fastpix.io/blog/understanding-vmaf-psnr-and-ssim-full-reference-video-quality-metrics
https://www.simalabs.ai/resources/how-generative-ai-video-models-enhance-streaming-q-c9ec72f0
Bandwidth vs Quality: Why 22% Less Data Doesn't Mean Fuzzy Video on Nugs
Streaming engineers still frame the debate as bandwidth vs quality, yet 2025 data shows you can drop bitrate without degrading the picture.
Why "bandwidth vs quality" is a false trade-off in 2025
The traditional assumption that reducing bandwidth automatically means sacrificing video quality no longer holds true. AI preprocessing represents a fundamentally different approach to video optimization that enhances encoder performance by intelligently preparing video content before encoding.
Modern AI-powered preprocessing engines are delivering measurable bandwidth reductions of 22% or more on existing H.264, HEVC, and AV1 stacks without requiring hardware upgrades or workflow changes. This breakthrough challenges the conventional wisdom that viewers must accept fuzzy video to save on data costs.
The impact on viewer engagement is significant. Research shows that a 1% increase in buffering ratio can reduce average play time by more than 3 minutes for a 90-minute stream. By reducing bandwidth requirements without compromising quality, preprocessing technology addresses both technical and business challenges simultaneously.
Perceptual preprocessing 101: cleaning frames before they hit the encoder
Perceptual preprocessing works by analyzing video content before it reaches the encoder, identifying visual patterns that the human eye won't notice and optimizing bit allocation accordingly. SimaBit from Sima Labs delivers 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.
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. By removing these imperceptible elements before encoding begins, the system ensures that every bit counts toward actual visual quality.
Crucially, SimaBit processes 1080p frames in under 16 milliseconds, making it suitable for both live streaming applications and video-on-demand workflows. This speed ensures that preprocessing doesn't become a bottleneck in production pipelines.
Live & VOD ready (< 16 ms per 1080p frame)
The sub-16 millisecond processing time for 1080p frames ensures that SimaBit can handle the most demanding streaming scenarios. Whether you're broadcasting live sports, gaming tournaments, or processing vast VOD libraries, this latency fits comfortably within existing encoding budgets without introducing delays that would impact viewer experience.
Proving it: VMAF and friends replace guesswork with science
VMAF represents the cutting edge of perceptual video quality assessment, combining machine learning with human visual perception modeling. Unlike traditional metrics that measure mathematical differences, VMAF predicts how actual viewers will perceive quality changes.
The percentage of time spent in buffering has the largest impact on user engagement across all content types. By reducing bandwidth requirements while maintaining high VMAF scores, preprocessing technology directly addresses this critical metric.
VQ-TIF estimates VMAF with a Pearson Correlation Coefficient of 0.96 and a Mean Absolute Error of 2.71, demonstrating that modern quality assessment can accurately predict viewer perception at scale. This scientific validation replaces subjective guesswork with data-driven optimization.
Why VMAF beats PSNR & SSIM for perceptual truth
VMAF stands out because it bridges the gap between technical measurements and real-world viewing. While PSNR measures pixel-level accuracy and SSIM evaluates structural similarity, neither fully captures how humans actually perceive video quality.
Analysis of 33 existing image and video quality metrics found that LPIPS and MS-SSIM excel at predicting contrast masking, while VMAF's machine learning approach better models the complete viewing experience. This perceptual focus ensures that bandwidth savings translate to genuine efficiency gains rather than degraded viewer satisfaction.
Business upside: less egress, lower churn, greener delivery
With SimaBit's demonstrated 22% bandwidth reduction, a platform serving 1 petabyte monthly would save approximately 220 terabytes in CDN costs. These savings compound when combined with reduced infrastructure requirements and lower energy consumption.
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. These improvements directly impact viewer retention and platform economics.
Re-buffering is one of the most common factors that reduce user Quality of Experience in online streaming video. By addressing bandwidth efficiency at the preprocessing stage, platforms can maintain competitive streaming quality while significantly reducing operational costs.
A 1 PB/month example
A platform serving 1 petabyte monthly would save approximately 220 terabytes in CDN costs with SimaBit's 22% bandwidth reduction. At typical CDN rates of $0.02-0.05 per GB, this translates to monthly savings of $4,400-11,000, or $52,800-132,000 annually. For platforms operating at larger scales, these savings can fund additional content acquisition or infrastructure improvements.
Dataset results: Netflix Open, YouTube UGC & AV2 synergy
SimaBit has been benchmarked on Netflix Open Content, YouTube UGC, and the OpenVid-1M GenAI video set, with verification via VMAF/SSIM metrics and golden-eye subjective studies. This comprehensive testing across diverse content types demonstrates consistent performance improvements.
AV2 shows around 30% lower bitrate than AV1 at the same quality. Combined with SimaBit preprocessing, teams report 28.63% bitrate reduction compared to AV1 in PSNR-YUV results and 32.59% in VMAF measurements. This synergy between preprocessing and next-generation codecs multiplies efficiency gains.
SimaBit's saliency masking removes up to 60% of visible noise while optimizing bit allocation for important visual elements. This targeted approach ensures that bandwidth savings come from removing imperceptible elements rather than compromising critical visual information.
Plug-in path: deploying SimaBit without rocking the boat
The preprocessing engine slips in front of any encoder without requiring changes to downstream systems, player compatibility, or content delivery networks. This seamless integration preserves existing workflows while adding AI-powered optimization.
Significantly improved time-to-market for post-live video content becomes possible when preprocessing handles optimization automatically. Teams can focus on content creation rather than manual encoding parameter tuning.
Globo's round trip time is just 10 milliseconds, demonstrating that cloud-based preprocessing can meet the stringent latency requirements of live production environments. This real-world validation shows that advanced preprocessing doesn't require on-premises infrastructure.
The bandwidth vs quality debate is over—here's what's next
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, ensuring that today's infrastructure investments remain valuable tomorrow.
The evidence is clear: modern perceptual preprocessing delivers measurable bandwidth savings without sacrificing visual quality. With proven reductions of 22% or more across diverse content types, validated by industry-standard VMAF metrics, and seamless integration with existing workflows, the traditional trade-off between bandwidth and quality no longer applies.
For streaming platforms looking to reduce CDN costs while maintaining competitive quality, SimaBit from Sima Labs offers a proven path forward. The technology's ability to enhance existing codec performance while preparing for future standards makes it an essential component of modern video infrastructure.
Frequently Asked Questions
How can 22% less bandwidth not reduce video quality?
Perceptual preprocessing removes noise and artifacts the human eye won’t notice, letting encoders spend bits where it matters. In SimaBit tests, platforms maintained or improved VMAF while cutting bitrate ~22%, so viewers see crisp video with fewer stalls.
What is perceptual preprocessing and how does SimaBit use it?
SimaBit analyzes each frame for saliency, denoises low-light content, and reallocates bits toward important details before encoding. This AI-first pass improves encoder efficiency across H.264, HEVC, and AV1 without changing downstream players or CDNs.
Is SimaBit ready for live as well as VOD workflows?
Yes. SimaBit processes 1080p frames in under 16 ms, fitting comfortably within typical live encoding budgets, and scales for large VOD libraries without becoming a bottleneck.
How do you validate that quality is preserved?
We use perceptual metrics such as VMAF, supported by models like VQ-TIF that correlate strongly with human opinion scores. The result is data-driven proof that bitrate reductions preserve perceived quality, not just pixel math.
Does SimaBit require new hardware or encoder changes?
No. SimaBit sits ahead of your existing encoder as a plug-in step and works with current H.264, HEVC, and AV1 stacks. It’s also available via Dolby Hybrik for immediate deployment (see https://www.simalabs.ai/pr).
What business impact can I expect from a 22% reduction?
A platform serving 1 PB per month would save about 220 TB of egress, translating to roughly $4,400–$11,000 in monthly CDN savings at common rates. Reduced rebuffering and improved VMAF also help lower churn and improve watch time.
Sources
https://www.simalabs.ai/resources/openvid-1m-genai-evaluation-ai-preprocessing-vmaf-ugc
https://probe.dev/resources/vmaf-perceptual-quality-analysis
https://www.fastpix.io/blog/understanding-vmaf-psnr-and-ssim-full-reference-video-quality-metrics
https://www.simalabs.ai/resources/how-generative-ai-video-models-enhance-streaming-q-c9ec72f0
Bandwidth vs Quality: Why 22% Less Data Doesn't Mean Fuzzy Video on Nugs
Streaming engineers still frame the debate as bandwidth vs quality, yet 2025 data shows you can drop bitrate without degrading the picture.
Why "bandwidth vs quality" is a false trade-off in 2025
The traditional assumption that reducing bandwidth automatically means sacrificing video quality no longer holds true. AI preprocessing represents a fundamentally different approach to video optimization that enhances encoder performance by intelligently preparing video content before encoding.
Modern AI-powered preprocessing engines are delivering measurable bandwidth reductions of 22% or more on existing H.264, HEVC, and AV1 stacks without requiring hardware upgrades or workflow changes. This breakthrough challenges the conventional wisdom that viewers must accept fuzzy video to save on data costs.
The impact on viewer engagement is significant. Research shows that a 1% increase in buffering ratio can reduce average play time by more than 3 minutes for a 90-minute stream. By reducing bandwidth requirements without compromising quality, preprocessing technology addresses both technical and business challenges simultaneously.
Perceptual preprocessing 101: cleaning frames before they hit the encoder
Perceptual preprocessing works by analyzing video content before it reaches the encoder, identifying visual patterns that the human eye won't notice and optimizing bit allocation accordingly. SimaBit from Sima Labs delivers 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.
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. By removing these imperceptible elements before encoding begins, the system ensures that every bit counts toward actual visual quality.
Crucially, SimaBit processes 1080p frames in under 16 milliseconds, making it suitable for both live streaming applications and video-on-demand workflows. This speed ensures that preprocessing doesn't become a bottleneck in production pipelines.
Live & VOD ready (< 16 ms per 1080p frame)
The sub-16 millisecond processing time for 1080p frames ensures that SimaBit can handle the most demanding streaming scenarios. Whether you're broadcasting live sports, gaming tournaments, or processing vast VOD libraries, this latency fits comfortably within existing encoding budgets without introducing delays that would impact viewer experience.
Proving it: VMAF and friends replace guesswork with science
VMAF represents the cutting edge of perceptual video quality assessment, combining machine learning with human visual perception modeling. Unlike traditional metrics that measure mathematical differences, VMAF predicts how actual viewers will perceive quality changes.
The percentage of time spent in buffering has the largest impact on user engagement across all content types. By reducing bandwidth requirements while maintaining high VMAF scores, preprocessing technology directly addresses this critical metric.
VQ-TIF estimates VMAF with a Pearson Correlation Coefficient of 0.96 and a Mean Absolute Error of 2.71, demonstrating that modern quality assessment can accurately predict viewer perception at scale. This scientific validation replaces subjective guesswork with data-driven optimization.
Why VMAF beats PSNR & SSIM for perceptual truth
VMAF stands out because it bridges the gap between technical measurements and real-world viewing. While PSNR measures pixel-level accuracy and SSIM evaluates structural similarity, neither fully captures how humans actually perceive video quality.
Analysis of 33 existing image and video quality metrics found that LPIPS and MS-SSIM excel at predicting contrast masking, while VMAF's machine learning approach better models the complete viewing experience. This perceptual focus ensures that bandwidth savings translate to genuine efficiency gains rather than degraded viewer satisfaction.
Business upside: less egress, lower churn, greener delivery
With SimaBit's demonstrated 22% bandwidth reduction, a platform serving 1 petabyte monthly would save approximately 220 terabytes in CDN costs. These savings compound when combined with reduced infrastructure requirements and lower energy consumption.
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. These improvements directly impact viewer retention and platform economics.
Re-buffering is one of the most common factors that reduce user Quality of Experience in online streaming video. By addressing bandwidth efficiency at the preprocessing stage, platforms can maintain competitive streaming quality while significantly reducing operational costs.
A 1 PB/month example
A platform serving 1 petabyte monthly would save approximately 220 terabytes in CDN costs with SimaBit's 22% bandwidth reduction. At typical CDN rates of $0.02-0.05 per GB, this translates to monthly savings of $4,400-11,000, or $52,800-132,000 annually. For platforms operating at larger scales, these savings can fund additional content acquisition or infrastructure improvements.
Dataset results: Netflix Open, YouTube UGC & AV2 synergy
SimaBit has been benchmarked on Netflix Open Content, YouTube UGC, and the OpenVid-1M GenAI video set, with verification via VMAF/SSIM metrics and golden-eye subjective studies. This comprehensive testing across diverse content types demonstrates consistent performance improvements.
AV2 shows around 30% lower bitrate than AV1 at the same quality. Combined with SimaBit preprocessing, teams report 28.63% bitrate reduction compared to AV1 in PSNR-YUV results and 32.59% in VMAF measurements. This synergy between preprocessing and next-generation codecs multiplies efficiency gains.
SimaBit's saliency masking removes up to 60% of visible noise while optimizing bit allocation for important visual elements. This targeted approach ensures that bandwidth savings come from removing imperceptible elements rather than compromising critical visual information.
Plug-in path: deploying SimaBit without rocking the boat
The preprocessing engine slips in front of any encoder without requiring changes to downstream systems, player compatibility, or content delivery networks. This seamless integration preserves existing workflows while adding AI-powered optimization.
Significantly improved time-to-market for post-live video content becomes possible when preprocessing handles optimization automatically. Teams can focus on content creation rather than manual encoding parameter tuning.
Globo's round trip time is just 10 milliseconds, demonstrating that cloud-based preprocessing can meet the stringent latency requirements of live production environments. This real-world validation shows that advanced preprocessing doesn't require on-premises infrastructure.
The bandwidth vs quality debate is over—here's what's next
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, ensuring that today's infrastructure investments remain valuable tomorrow.
The evidence is clear: modern perceptual preprocessing delivers measurable bandwidth savings without sacrificing visual quality. With proven reductions of 22% or more across diverse content types, validated by industry-standard VMAF metrics, and seamless integration with existing workflows, the traditional trade-off between bandwidth and quality no longer applies.
For streaming platforms looking to reduce CDN costs while maintaining competitive quality, SimaBit from Sima Labs offers a proven path forward. The technology's ability to enhance existing codec performance while preparing for future standards makes it an essential component of modern video infrastructure.
Frequently Asked Questions
How can 22% less bandwidth not reduce video quality?
Perceptual preprocessing removes noise and artifacts the human eye won’t notice, letting encoders spend bits where it matters. In SimaBit tests, platforms maintained or improved VMAF while cutting bitrate ~22%, so viewers see crisp video with fewer stalls.
What is perceptual preprocessing and how does SimaBit use it?
SimaBit analyzes each frame for saliency, denoises low-light content, and reallocates bits toward important details before encoding. This AI-first pass improves encoder efficiency across H.264, HEVC, and AV1 without changing downstream players or CDNs.
Is SimaBit ready for live as well as VOD workflows?
Yes. SimaBit processes 1080p frames in under 16 ms, fitting comfortably within typical live encoding budgets, and scales for large VOD libraries without becoming a bottleneck.
How do you validate that quality is preserved?
We use perceptual metrics such as VMAF, supported by models like VQ-TIF that correlate strongly with human opinion scores. The result is data-driven proof that bitrate reductions preserve perceived quality, not just pixel math.
Does SimaBit require new hardware or encoder changes?
No. SimaBit sits ahead of your existing encoder as a plug-in step and works with current H.264, HEVC, and AV1 stacks. It’s also available via Dolby Hybrik for immediate deployment (see https://www.simalabs.ai/pr).
What business impact can I expect from a 22% reduction?
A platform serving 1 PB per month would save about 220 TB of egress, translating to roughly $4,400–$11,000 in monthly CDN savings at common rates. Reduced rebuffering and improved VMAF also help lower churn and improve watch time.
Sources
https://www.simalabs.ai/resources/openvid-1m-genai-evaluation-ai-preprocessing-vmaf-ugc
https://probe.dev/resources/vmaf-perceptual-quality-analysis
https://www.fastpix.io/blog/understanding-vmaf-psnr-and-ssim-full-reference-video-quality-metrics
https://www.simalabs.ai/resources/how-generative-ai-video-models-enhance-streaming-q-c9ec72f0
SimaLabs
©2025 Sima Labs. All rights reserved
SimaLabs
©2025 Sima Labs. All rights reserved
SimaLabs
©2025 Sima Labs. All rights reserved