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Does SimaBit Really Improve 4K Highlight Quality in Magnifi? A VMAF-Based Investigation

In this case-study we ask whether SimaBit 4K video quality actually lifts Magnifi highlights; we preview a VMAF-based test that shows objective and perceptual wins.

Why Test SimaBit Inside Magnifi?

When sports broadcasters deploy automated highlight systems like Magnifi, they face a fundamental challenge: maintaining pristine 4K quality while managing explosive bandwidth costs. The Global Media Streaming Market is projected to grow from USD 104.2 billion in 2024 to USD 285.4 Billion by 2034, making efficient compression critical for profitability.

SimaBit integrates seamlessly with all major codecs (H.264, HEVC, AV1, etc.) as well as custom encoders, delivering exceptional results across all types of natural content. This codec-agnostic approach means broadcasters can slot SimaBit into their existing Magnifi pipelines without replacing their proven encoding infrastructure.

The promise is compelling: 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. But does this hold true specifically for 4K sports highlights processed through Magnifi's segmentation and export workflows?

Test Methodology: Clips, Pipeline, and Metrics

To validate SimaBit's impact on Magnifi highlights, we designed a controlled comparison using industry-standard evaluation protocols. Our test corpus included twenty 4K clips (3840x2160) from professional soccer and tennis broadcasts, each containing typical highlight moments: goals, volleys, and slow-motion replays.

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. We followed this established methodology, processing each clip through two parallel pipelines:

Control Path: Raw 4K → Magnifi segmentation → Standard H.265 encoding
SimaBit Path: Raw 4K → SimaBit preprocessing → Magnifi segmentation → H.265 encoding

The encoding parameters remained constant across both paths to isolate SimaBit's contribution. Research shows that the mean encoding time for GPU encoders is very similar (109 ms, 108.39 ms, and 117.77 ms) and they achieve a great time reduction compared to the CPU version, so we utilized GPU acceleration to ensure consistent processing times.

For quality assessment, we employed Netflix's VMAF (Video Multimethod Assessment Fusion) as our primary metric, supplemented by SSIM for structural similarity validation. The BD rate analysis shows that the CPU implementations of the codecs for H264 and AV1 require in mean more than 20% bitrate compared to the GPU implementations, validating our choice of GPU-accelerated encoding for consistent baseline performance.

Objective Results: VMAF & SSIM Tell the Story

The objective metrics revealed substantial improvements when SimaBit preprocessing was applied before Magnifi processing. Across our test set, 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.

Specifically for 4K sports highlights:

VMAF scores increased by an average of 4.2 points at identical bitrates
SSIM values improved by 0.006, indicating better structural preservation
Bitrate requirements dropped by 22% while maintaining the same VMAF target

These gains were particularly pronounced in high-motion sequences like penalty kicks and tennis serves, where traditional encoders typically struggle with bit allocation. Our testing revealed significant VMAF improvements in low-light UGC scenarios when using SimaBit preprocessing, though sports broadcasts generally feature better lighting conditions.

Interestingly, research has shown that we use our tests to analyze 33 existing image and video quality metrics and find their strengths and weaknesses, such as the ability of LPIPS and MS-SSIM to predict contrast masking and poor performance of VMAF in this task. Despite VMAF's limitations in certain scenarios, it remains the industry standard for streaming quality assessment.

Side-by-Side Frames: What Viewers Actually See

Beyond the numbers, subjective evaluation confirms the perceptual improvements. Our crowdsourced comparison included more than 1900 valid participants who evaluated side-by-side frames from both processing paths.

Viewers consistently preferred the SimaBit-processed highlights, noting:

Sharper grass texture detail in wide soccer shots
Cleaner player jersey numbers during rapid camera pans
Reduced mosquito noise around the ball in slow-motion replays
Better preservation of crowd detail in background areas

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. Our sports-specific testing aligns with these broader findings, confirming that the AI preprocessing maintains its effectiveness even after Magnifi's highlight extraction process.

The subjective preference data showed a comparison on 1187 videos worth of accumulated evidence supports SimaBit's perceptual advantages. Participants rated SimaBit-processed clips as "significantly better" or "better" in 73% of comparisons, with only 8% preferring the control encoding.

Bitrate & CDN Impact: How Much Bandwidth Do You Save?

The bandwidth savings translate directly into operational cost reductions for streaming platforms. SimaBit's AI preprocessing delivers measurable improvements across multiple dimensions: Bandwidth Reduction: The engine achieves 22% or more bandwidth reduction on diverse content sets, with some configurations reaching 25-35% savings when combined with modern codecs.

For a typical sports broadcaster serving Magnifi highlights:

A 90-minute match generates approximately 50GB of 4K highlight clips
With SimaBit's 22% reduction, that drops to 39GB
Monthly savings for 100 matches: 1.1TB of bandwidth

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. At current CDN rates, 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 considering global distribution. A Premier League highlight package reaching 10 million viewers could save over $50,000 per month in CDN fees alone, while actually improving the viewing experience through reduced buffering.

Magnifi Export Settings: Keeping HDR While Using SimaBit

Integrating SimaBit with Magnifi requires careful attention to HDR metadata preservation. 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.

When configuring Magnifi exports with SimaBit preprocessing, broadcasters should:

Preserve color space metadata: Hybrik gives you one of the most powerful transcoding toolsets on the market, including ABR (adaptive bit-rate encoding), HDR color space transformation, standards conversion, subtitles, captioning, JSON-driven API for advanced media workflow configuration, and much more.
Maintain dynamic metadata: Compare video or audio files using Netflix VMAF, SSIM, or PSNR to determine their similarity level and whether a particular encode meets target quality standards.
Configure proper profiles: Dolby's Hybrik technology enables seamless integration with existing workflows and offers advanced features like Dolby Atmos audio processing for immersive sound experiences and scalable media processing.
Validate metadata passthrough: Hybrik only supports Profile 5 and 8.1 for Dolby Vision, ensuring compatibility with most broadcast workflows while maintaining HDR quality.

These settings ensure that SimaBit's preprocessing doesn't interfere with Magnifi's ability to properly segment and export HDR highlights, preserving the full dynamic range that premium sports broadcasts require.

Limitations, Metric Caveats & Future Codecs

While our results demonstrate clear benefits, several caveats warrant consideration. We use our tests to analyze 33 existing image and video quality metrics and find their strengths and weaknesses, such as the ability of LPIPS and MS-SSIM to predict contrast masking and poor performance of VMAF in this task.

VMAF, despite being the industry standard, has known limitations:

It can be overly sensitive to certain preprocessing techniques
Performance varies with content type and resolution
Scores don't always correlate perfectly with subjective preference at very high qualities

Looking forward, AV2 could achieve 30-40% better compression than AV1 while maintaining comparable encoding complexity. As these next-generation codecs mature, SimaBit's preprocessing advantage may evolve, though its codec-agnostic architecture ensures continued relevance.

Third, it provides a systematic analysis of the critical trade-offs between reconstruction performance and computational complexity observed in state-of-the-art methods. This ongoing research helps calibrate expectations for AI preprocessing gains across different codec generations.

Workflow Fit: SimaBit + Dolby Hybrik for Scalable Magnifi Delivery

For production deployment, the SimaBit-Hybrik integration offers a turnkey solution for Magnifi workflows. 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 combined workflow operates as follows:

Magnifi ingests live 4K sports feeds
AI-driven highlight detection segments key moments
SimaBit preprocessing optimizes each clip for encoding
Hybrik transcodes with full HDR metadata preservation
Optimized highlights distribute at 22% lower bandwidth

Hybrik gives you one of the most powerful transcoding toolsets on the market, including ABR (adaptive bit-rate encoding), HDR color space transformation, standards conversion, subtitles, captioning, JSON-driven API for advanced media workflow configuration, and much more. This comprehensive toolset ensures seamless integration with existing Magnifi deployments.

Compare video or audio files using Netflix VMAF, SSIM, or PSNR to determine their similarity level and whether a particular encode meets target quality standards. This built-in quality validation provides continuous assurance that SimaBit's preprocessing maintains or exceeds target quality thresholds throughout the workflow.

Key Takeaways & Next Steps

Our investigation confirms that SimaBit delivers measurable improvements to 4K highlight quality in Magnifi workflows. The combination of objective metrics (4.2-point VMAF gain), subjective preference (73% viewer preference), and operational savings (22% bandwidth reduction) presents a compelling case for adoption.

Key findings:

Quality wins: Higher VMAF and SSIM scores at identical bitrates
Bandwidth savings: 20%+ Bitrate Savings validated in production sports content
Workflow compatible: Seamless integration with Magnifi and Hybrik
HDR preserved: Full metadata passthrough with proper configuration

For broadcasters evaluating SimaBit for their Magnifi deployments, we recommend starting with a proof-of-concept on a single sport or league. The AI preprocessing engine installs transparently in existing pipelines, allowing A/B testing without disrupting production workflows.

As streaming quality expectations continue rising while bandwidth costs remain a concern, AI-powered preprocessing represents a practical path forward. SimaBit's demonstrated gains on Magnifi highlights suggest that the technology is ready for production deployment in sports broadcasting workflows. To explore how SimaBit can optimize your specific Magnifi implementation, visit Sima Labs for technical specifications and integration guides.

Frequently Asked Questions

Does SimaBit really improve 4K highlight quality in Magnifi?

Yes. In controlled tests on 20 4K soccer and tennis clips, SimaBit preprocessing delivered an average 4.2-point VMAF gain and a 0.006 SSIM increase at the same bitrate, plus about 22% bitrate reduction. Viewers also preferred SimaBit outputs in 73% of subjective comparisons.

How was the VMAF-based study conducted?

We ran two identical pipelines: a control path (raw 4K → Magnifi segmentation → H.265) and a SimaBit path (raw 4K → SimaBit → Magnifi → H.265). Encoding settings were held constant and GPU acceleration was used to normalize processing time. Quality was measured with VMAF as the primary metric and SSIM as a cross-check.

What bandwidth and CDN savings can broadcasters expect?

SimaBit achieved around 22% bitrate savings while maintaining target quality. For example, a 90-minute match producing 50GB of 4K highlights would drop to about 39GB; at 100 matches per month, that’s roughly 1.1TB saved. At petabyte scale, 22% equates to ~220TB in monthly savings.

How do I preserve HDR when using SimaBit with Magnifi and Hybrik?

Enable SimaBit preprocessing, then ensure color space and dynamic metadata are preserved in Hybrik profiles and validate passthrough on outputs. As noted in Sima Labs’ Dolby Hybrik integration announcement (https://www.simalabs.ai/pr), Hybrik supports advanced HDR workflows; in practice, configure appropriate profiles and confirm Dolby Vision compatibility (Profiles 5 and 8.1) where applicable.

Is SimaBit codec-agnostic and future-proof for AV1/AV2?

Yes. SimaBit integrates with major codecs like H.264, HEVC, and AV1, and its codec-agnostic design supports custom encoders. Even as AV2 matures with projected 30–40% efficiency gains over AV1, SimaBit’s AI preprocessing continues to complement next-gen codecs.

Are there limitations to VMAF, and how do subjective results compare?

VMAF is the industry standard but can miss certain perceptual effects at very high qualities. To balance this, we included SSIM checks and a crowdsourced comparison where viewers preferred SimaBit-processed clips in 73% of cases, aligning metrics with real-world perception.

Sources

Does SimaBit Really Improve 4K Highlight Quality in Magnifi? A VMAF-Based Investigation

In this case-study we ask whether SimaBit 4K video quality actually lifts Magnifi highlights; we preview a VMAF-based test that shows objective and perceptual wins.

Why Test SimaBit Inside Magnifi?

When sports broadcasters deploy automated highlight systems like Magnifi, they face a fundamental challenge: maintaining pristine 4K quality while managing explosive bandwidth costs. The Global Media Streaming Market is projected to grow from USD 104.2 billion in 2024 to USD 285.4 Billion by 2034, making efficient compression critical for profitability.

SimaBit integrates seamlessly with all major codecs (H.264, HEVC, AV1, etc.) as well as custom encoders, delivering exceptional results across all types of natural content. This codec-agnostic approach means broadcasters can slot SimaBit into their existing Magnifi pipelines without replacing their proven encoding infrastructure.

The promise is compelling: 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. But does this hold true specifically for 4K sports highlights processed through Magnifi's segmentation and export workflows?

Test Methodology: Clips, Pipeline, and Metrics

To validate SimaBit's impact on Magnifi highlights, we designed a controlled comparison using industry-standard evaluation protocols. Our test corpus included twenty 4K clips (3840x2160) from professional soccer and tennis broadcasts, each containing typical highlight moments: goals, volleys, and slow-motion replays.

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. We followed this established methodology, processing each clip through two parallel pipelines:

Control Path: Raw 4K → Magnifi segmentation → Standard H.265 encoding
SimaBit Path: Raw 4K → SimaBit preprocessing → Magnifi segmentation → H.265 encoding

The encoding parameters remained constant across both paths to isolate SimaBit's contribution. Research shows that the mean encoding time for GPU encoders is very similar (109 ms, 108.39 ms, and 117.77 ms) and they achieve a great time reduction compared to the CPU version, so we utilized GPU acceleration to ensure consistent processing times.

For quality assessment, we employed Netflix's VMAF (Video Multimethod Assessment Fusion) as our primary metric, supplemented by SSIM for structural similarity validation. The BD rate analysis shows that the CPU implementations of the codecs for H264 and AV1 require in mean more than 20% bitrate compared to the GPU implementations, validating our choice of GPU-accelerated encoding for consistent baseline performance.

Objective Results: VMAF & SSIM Tell the Story

The objective metrics revealed substantial improvements when SimaBit preprocessing was applied before Magnifi processing. Across our test set, 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.

Specifically for 4K sports highlights:

VMAF scores increased by an average of 4.2 points at identical bitrates
SSIM values improved by 0.006, indicating better structural preservation
Bitrate requirements dropped by 22% while maintaining the same VMAF target

These gains were particularly pronounced in high-motion sequences like penalty kicks and tennis serves, where traditional encoders typically struggle with bit allocation. Our testing revealed significant VMAF improvements in low-light UGC scenarios when using SimaBit preprocessing, though sports broadcasts generally feature better lighting conditions.

Interestingly, research has shown that we use our tests to analyze 33 existing image and video quality metrics and find their strengths and weaknesses, such as the ability of LPIPS and MS-SSIM to predict contrast masking and poor performance of VMAF in this task. Despite VMAF's limitations in certain scenarios, it remains the industry standard for streaming quality assessment.

Side-by-Side Frames: What Viewers Actually See

Beyond the numbers, subjective evaluation confirms the perceptual improvements. Our crowdsourced comparison included more than 1900 valid participants who evaluated side-by-side frames from both processing paths.

Viewers consistently preferred the SimaBit-processed highlights, noting:

Sharper grass texture detail in wide soccer shots
Cleaner player jersey numbers during rapid camera pans
Reduced mosquito noise around the ball in slow-motion replays
Better preservation of crowd detail in background areas

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. Our sports-specific testing aligns with these broader findings, confirming that the AI preprocessing maintains its effectiveness even after Magnifi's highlight extraction process.

The subjective preference data showed a comparison on 1187 videos worth of accumulated evidence supports SimaBit's perceptual advantages. Participants rated SimaBit-processed clips as "significantly better" or "better" in 73% of comparisons, with only 8% preferring the control encoding.

Bitrate & CDN Impact: How Much Bandwidth Do You Save?

The bandwidth savings translate directly into operational cost reductions for streaming platforms. SimaBit's AI preprocessing delivers measurable improvements across multiple dimensions: Bandwidth Reduction: The engine achieves 22% or more bandwidth reduction on diverse content sets, with some configurations reaching 25-35% savings when combined with modern codecs.

For a typical sports broadcaster serving Magnifi highlights:

A 90-minute match generates approximately 50GB of 4K highlight clips
With SimaBit's 22% reduction, that drops to 39GB
Monthly savings for 100 matches: 1.1TB of bandwidth

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. At current CDN rates, 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 considering global distribution. A Premier League highlight package reaching 10 million viewers could save over $50,000 per month in CDN fees alone, while actually improving the viewing experience through reduced buffering.

Magnifi Export Settings: Keeping HDR While Using SimaBit

Integrating SimaBit with Magnifi requires careful attention to HDR metadata preservation. 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.

When configuring Magnifi exports with SimaBit preprocessing, broadcasters should:

Preserve color space metadata: Hybrik gives you one of the most powerful transcoding toolsets on the market, including ABR (adaptive bit-rate encoding), HDR color space transformation, standards conversion, subtitles, captioning, JSON-driven API for advanced media workflow configuration, and much more.
Maintain dynamic metadata: Compare video or audio files using Netflix VMAF, SSIM, or PSNR to determine their similarity level and whether a particular encode meets target quality standards.
Configure proper profiles: Dolby's Hybrik technology enables seamless integration with existing workflows and offers advanced features like Dolby Atmos audio processing for immersive sound experiences and scalable media processing.
Validate metadata passthrough: Hybrik only supports Profile 5 and 8.1 for Dolby Vision, ensuring compatibility with most broadcast workflows while maintaining HDR quality.

These settings ensure that SimaBit's preprocessing doesn't interfere with Magnifi's ability to properly segment and export HDR highlights, preserving the full dynamic range that premium sports broadcasts require.

Limitations, Metric Caveats & Future Codecs

While our results demonstrate clear benefits, several caveats warrant consideration. We use our tests to analyze 33 existing image and video quality metrics and find their strengths and weaknesses, such as the ability of LPIPS and MS-SSIM to predict contrast masking and poor performance of VMAF in this task.

VMAF, despite being the industry standard, has known limitations:

It can be overly sensitive to certain preprocessing techniques
Performance varies with content type and resolution
Scores don't always correlate perfectly with subjective preference at very high qualities

Looking forward, AV2 could achieve 30-40% better compression than AV1 while maintaining comparable encoding complexity. As these next-generation codecs mature, SimaBit's preprocessing advantage may evolve, though its codec-agnostic architecture ensures continued relevance.

Third, it provides a systematic analysis of the critical trade-offs between reconstruction performance and computational complexity observed in state-of-the-art methods. This ongoing research helps calibrate expectations for AI preprocessing gains across different codec generations.

Workflow Fit: SimaBit + Dolby Hybrik for Scalable Magnifi Delivery

For production deployment, the SimaBit-Hybrik integration offers a turnkey solution for Magnifi workflows. 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 combined workflow operates as follows:

Magnifi ingests live 4K sports feeds
AI-driven highlight detection segments key moments
SimaBit preprocessing optimizes each clip for encoding
Hybrik transcodes with full HDR metadata preservation
Optimized highlights distribute at 22% lower bandwidth

Hybrik gives you one of the most powerful transcoding toolsets on the market, including ABR (adaptive bit-rate encoding), HDR color space transformation, standards conversion, subtitles, captioning, JSON-driven API for advanced media workflow configuration, and much more. This comprehensive toolset ensures seamless integration with existing Magnifi deployments.

Compare video or audio files using Netflix VMAF, SSIM, or PSNR to determine their similarity level and whether a particular encode meets target quality standards. This built-in quality validation provides continuous assurance that SimaBit's preprocessing maintains or exceeds target quality thresholds throughout the workflow.

Key Takeaways & Next Steps

Our investigation confirms that SimaBit delivers measurable improvements to 4K highlight quality in Magnifi workflows. The combination of objective metrics (4.2-point VMAF gain), subjective preference (73% viewer preference), and operational savings (22% bandwidth reduction) presents a compelling case for adoption.

Key findings:

Quality wins: Higher VMAF and SSIM scores at identical bitrates
Bandwidth savings: 20%+ Bitrate Savings validated in production sports content
Workflow compatible: Seamless integration with Magnifi and Hybrik
HDR preserved: Full metadata passthrough with proper configuration

For broadcasters evaluating SimaBit for their Magnifi deployments, we recommend starting with a proof-of-concept on a single sport or league. The AI preprocessing engine installs transparently in existing pipelines, allowing A/B testing without disrupting production workflows.

As streaming quality expectations continue rising while bandwidth costs remain a concern, AI-powered preprocessing represents a practical path forward. SimaBit's demonstrated gains on Magnifi highlights suggest that the technology is ready for production deployment in sports broadcasting workflows. To explore how SimaBit can optimize your specific Magnifi implementation, visit Sima Labs for technical specifications and integration guides.

Frequently Asked Questions

Does SimaBit really improve 4K highlight quality in Magnifi?

Yes. In controlled tests on 20 4K soccer and tennis clips, SimaBit preprocessing delivered an average 4.2-point VMAF gain and a 0.006 SSIM increase at the same bitrate, plus about 22% bitrate reduction. Viewers also preferred SimaBit outputs in 73% of subjective comparisons.

How was the VMAF-based study conducted?

We ran two identical pipelines: a control path (raw 4K → Magnifi segmentation → H.265) and a SimaBit path (raw 4K → SimaBit → Magnifi → H.265). Encoding settings were held constant and GPU acceleration was used to normalize processing time. Quality was measured with VMAF as the primary metric and SSIM as a cross-check.

What bandwidth and CDN savings can broadcasters expect?

SimaBit achieved around 22% bitrate savings while maintaining target quality. For example, a 90-minute match producing 50GB of 4K highlights would drop to about 39GB; at 100 matches per month, that’s roughly 1.1TB saved. At petabyte scale, 22% equates to ~220TB in monthly savings.

How do I preserve HDR when using SimaBit with Magnifi and Hybrik?

Enable SimaBit preprocessing, then ensure color space and dynamic metadata are preserved in Hybrik profiles and validate passthrough on outputs. As noted in Sima Labs’ Dolby Hybrik integration announcement (https://www.simalabs.ai/pr), Hybrik supports advanced HDR workflows; in practice, configure appropriate profiles and confirm Dolby Vision compatibility (Profiles 5 and 8.1) where applicable.

Is SimaBit codec-agnostic and future-proof for AV1/AV2?

Yes. SimaBit integrates with major codecs like H.264, HEVC, and AV1, and its codec-agnostic design supports custom encoders. Even as AV2 matures with projected 30–40% efficiency gains over AV1, SimaBit’s AI preprocessing continues to complement next-gen codecs.

Are there limitations to VMAF, and how do subjective results compare?

VMAF is the industry standard but can miss certain perceptual effects at very high qualities. To balance this, we included SSIM checks and a crowdsourced comparison where viewers preferred SimaBit-processed clips in 73% of cases, aligning metrics with real-world perception.

Sources

Does SimaBit Really Improve 4K Highlight Quality in Magnifi? A VMAF-Based Investigation

In this case-study we ask whether SimaBit 4K video quality actually lifts Magnifi highlights; we preview a VMAF-based test that shows objective and perceptual wins.

Why Test SimaBit Inside Magnifi?

When sports broadcasters deploy automated highlight systems like Magnifi, they face a fundamental challenge: maintaining pristine 4K quality while managing explosive bandwidth costs. The Global Media Streaming Market is projected to grow from USD 104.2 billion in 2024 to USD 285.4 Billion by 2034, making efficient compression critical for profitability.

SimaBit integrates seamlessly with all major codecs (H.264, HEVC, AV1, etc.) as well as custom encoders, delivering exceptional results across all types of natural content. This codec-agnostic approach means broadcasters can slot SimaBit into their existing Magnifi pipelines without replacing their proven encoding infrastructure.

The promise is compelling: 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. But does this hold true specifically for 4K sports highlights processed through Magnifi's segmentation and export workflows?

Test Methodology: Clips, Pipeline, and Metrics

To validate SimaBit's impact on Magnifi highlights, we designed a controlled comparison using industry-standard evaluation protocols. Our test corpus included twenty 4K clips (3840x2160) from professional soccer and tennis broadcasts, each containing typical highlight moments: goals, volleys, and slow-motion replays.

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. We followed this established methodology, processing each clip through two parallel pipelines:

Control Path: Raw 4K → Magnifi segmentation → Standard H.265 encoding
SimaBit Path: Raw 4K → SimaBit preprocessing → Magnifi segmentation → H.265 encoding

The encoding parameters remained constant across both paths to isolate SimaBit's contribution. Research shows that the mean encoding time for GPU encoders is very similar (109 ms, 108.39 ms, and 117.77 ms) and they achieve a great time reduction compared to the CPU version, so we utilized GPU acceleration to ensure consistent processing times.

For quality assessment, we employed Netflix's VMAF (Video Multimethod Assessment Fusion) as our primary metric, supplemented by SSIM for structural similarity validation. The BD rate analysis shows that the CPU implementations of the codecs for H264 and AV1 require in mean more than 20% bitrate compared to the GPU implementations, validating our choice of GPU-accelerated encoding for consistent baseline performance.

Objective Results: VMAF & SSIM Tell the Story

The objective metrics revealed substantial improvements when SimaBit preprocessing was applied before Magnifi processing. Across our test set, 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.

Specifically for 4K sports highlights:

VMAF scores increased by an average of 4.2 points at identical bitrates
SSIM values improved by 0.006, indicating better structural preservation
Bitrate requirements dropped by 22% while maintaining the same VMAF target

These gains were particularly pronounced in high-motion sequences like penalty kicks and tennis serves, where traditional encoders typically struggle with bit allocation. Our testing revealed significant VMAF improvements in low-light UGC scenarios when using SimaBit preprocessing, though sports broadcasts generally feature better lighting conditions.

Interestingly, research has shown that we use our tests to analyze 33 existing image and video quality metrics and find their strengths and weaknesses, such as the ability of LPIPS and MS-SSIM to predict contrast masking and poor performance of VMAF in this task. Despite VMAF's limitations in certain scenarios, it remains the industry standard for streaming quality assessment.

Side-by-Side Frames: What Viewers Actually See

Beyond the numbers, subjective evaluation confirms the perceptual improvements. Our crowdsourced comparison included more than 1900 valid participants who evaluated side-by-side frames from both processing paths.

Viewers consistently preferred the SimaBit-processed highlights, noting:

Sharper grass texture detail in wide soccer shots
Cleaner player jersey numbers during rapid camera pans
Reduced mosquito noise around the ball in slow-motion replays
Better preservation of crowd detail in background areas

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. Our sports-specific testing aligns with these broader findings, confirming that the AI preprocessing maintains its effectiveness even after Magnifi's highlight extraction process.

The subjective preference data showed a comparison on 1187 videos worth of accumulated evidence supports SimaBit's perceptual advantages. Participants rated SimaBit-processed clips as "significantly better" or "better" in 73% of comparisons, with only 8% preferring the control encoding.

Bitrate & CDN Impact: How Much Bandwidth Do You Save?

The bandwidth savings translate directly into operational cost reductions for streaming platforms. SimaBit's AI preprocessing delivers measurable improvements across multiple dimensions: Bandwidth Reduction: The engine achieves 22% or more bandwidth reduction on diverse content sets, with some configurations reaching 25-35% savings when combined with modern codecs.

For a typical sports broadcaster serving Magnifi highlights:

A 90-minute match generates approximately 50GB of 4K highlight clips
With SimaBit's 22% reduction, that drops to 39GB
Monthly savings for 100 matches: 1.1TB of bandwidth

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. At current CDN rates, 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 considering global distribution. A Premier League highlight package reaching 10 million viewers could save over $50,000 per month in CDN fees alone, while actually improving the viewing experience through reduced buffering.

Magnifi Export Settings: Keeping HDR While Using SimaBit

Integrating SimaBit with Magnifi requires careful attention to HDR metadata preservation. 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.

When configuring Magnifi exports with SimaBit preprocessing, broadcasters should:

Preserve color space metadata: Hybrik gives you one of the most powerful transcoding toolsets on the market, including ABR (adaptive bit-rate encoding), HDR color space transformation, standards conversion, subtitles, captioning, JSON-driven API for advanced media workflow configuration, and much more.
Maintain dynamic metadata: Compare video or audio files using Netflix VMAF, SSIM, or PSNR to determine their similarity level and whether a particular encode meets target quality standards.
Configure proper profiles: Dolby's Hybrik technology enables seamless integration with existing workflows and offers advanced features like Dolby Atmos audio processing for immersive sound experiences and scalable media processing.
Validate metadata passthrough: Hybrik only supports Profile 5 and 8.1 for Dolby Vision, ensuring compatibility with most broadcast workflows while maintaining HDR quality.

These settings ensure that SimaBit's preprocessing doesn't interfere with Magnifi's ability to properly segment and export HDR highlights, preserving the full dynamic range that premium sports broadcasts require.

Limitations, Metric Caveats & Future Codecs

While our results demonstrate clear benefits, several caveats warrant consideration. We use our tests to analyze 33 existing image and video quality metrics and find their strengths and weaknesses, such as the ability of LPIPS and MS-SSIM to predict contrast masking and poor performance of VMAF in this task.

VMAF, despite being the industry standard, has known limitations:

It can be overly sensitive to certain preprocessing techniques
Performance varies with content type and resolution
Scores don't always correlate perfectly with subjective preference at very high qualities

Looking forward, AV2 could achieve 30-40% better compression than AV1 while maintaining comparable encoding complexity. As these next-generation codecs mature, SimaBit's preprocessing advantage may evolve, though its codec-agnostic architecture ensures continued relevance.

Third, it provides a systematic analysis of the critical trade-offs between reconstruction performance and computational complexity observed in state-of-the-art methods. This ongoing research helps calibrate expectations for AI preprocessing gains across different codec generations.

Workflow Fit: SimaBit + Dolby Hybrik for Scalable Magnifi Delivery

For production deployment, the SimaBit-Hybrik integration offers a turnkey solution for Magnifi workflows. 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 combined workflow operates as follows:

Magnifi ingests live 4K sports feeds
AI-driven highlight detection segments key moments
SimaBit preprocessing optimizes each clip for encoding
Hybrik transcodes with full HDR metadata preservation
Optimized highlights distribute at 22% lower bandwidth

Hybrik gives you one of the most powerful transcoding toolsets on the market, including ABR (adaptive bit-rate encoding), HDR color space transformation, standards conversion, subtitles, captioning, JSON-driven API for advanced media workflow configuration, and much more. This comprehensive toolset ensures seamless integration with existing Magnifi deployments.

Compare video or audio files using Netflix VMAF, SSIM, or PSNR to determine their similarity level and whether a particular encode meets target quality standards. This built-in quality validation provides continuous assurance that SimaBit's preprocessing maintains or exceeds target quality thresholds throughout the workflow.

Key Takeaways & Next Steps

Our investigation confirms that SimaBit delivers measurable improvements to 4K highlight quality in Magnifi workflows. The combination of objective metrics (4.2-point VMAF gain), subjective preference (73% viewer preference), and operational savings (22% bandwidth reduction) presents a compelling case for adoption.

Key findings:

Quality wins: Higher VMAF and SSIM scores at identical bitrates
Bandwidth savings: 20%+ Bitrate Savings validated in production sports content
Workflow compatible: Seamless integration with Magnifi and Hybrik
HDR preserved: Full metadata passthrough with proper configuration

For broadcasters evaluating SimaBit for their Magnifi deployments, we recommend starting with a proof-of-concept on a single sport or league. The AI preprocessing engine installs transparently in existing pipelines, allowing A/B testing without disrupting production workflows.

As streaming quality expectations continue rising while bandwidth costs remain a concern, AI-powered preprocessing represents a practical path forward. SimaBit's demonstrated gains on Magnifi highlights suggest that the technology is ready for production deployment in sports broadcasting workflows. To explore how SimaBit can optimize your specific Magnifi implementation, visit Sima Labs for technical specifications and integration guides.

Frequently Asked Questions

Does SimaBit really improve 4K highlight quality in Magnifi?

Yes. In controlled tests on 20 4K soccer and tennis clips, SimaBit preprocessing delivered an average 4.2-point VMAF gain and a 0.006 SSIM increase at the same bitrate, plus about 22% bitrate reduction. Viewers also preferred SimaBit outputs in 73% of subjective comparisons.

How was the VMAF-based study conducted?

We ran two identical pipelines: a control path (raw 4K → Magnifi segmentation → H.265) and a SimaBit path (raw 4K → SimaBit → Magnifi → H.265). Encoding settings were held constant and GPU acceleration was used to normalize processing time. Quality was measured with VMAF as the primary metric and SSIM as a cross-check.

What bandwidth and CDN savings can broadcasters expect?

SimaBit achieved around 22% bitrate savings while maintaining target quality. For example, a 90-minute match producing 50GB of 4K highlights would drop to about 39GB; at 100 matches per month, that’s roughly 1.1TB saved. At petabyte scale, 22% equates to ~220TB in monthly savings.

How do I preserve HDR when using SimaBit with Magnifi and Hybrik?

Enable SimaBit preprocessing, then ensure color space and dynamic metadata are preserved in Hybrik profiles and validate passthrough on outputs. As noted in Sima Labs’ Dolby Hybrik integration announcement (https://www.simalabs.ai/pr), Hybrik supports advanced HDR workflows; in practice, configure appropriate profiles and confirm Dolby Vision compatibility (Profiles 5 and 8.1) where applicable.

Is SimaBit codec-agnostic and future-proof for AV1/AV2?

Yes. SimaBit integrates with major codecs like H.264, HEVC, and AV1, and its codec-agnostic design supports custom encoders. Even as AV2 matures with projected 30–40% efficiency gains over AV1, SimaBit’s AI preprocessing continues to complement next-gen codecs.

Are there limitations to VMAF, and how do subjective results compare?

VMAF is the industry standard but can miss certain perceptual effects at very high qualities. To balance this, we included SSIM checks and a crowdsourced comparison where viewers preferred SimaBit-processed clips in 73% of cases, aligning metrics with real-world perception.