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From Camera Shake to Cinematic: Stabilizing Veo 3 Clips Before SimaBit Encoding

Google's Veo 3 represents a significant leap in AI video generation, offering capabilities like native audio generation and 4K resolution outputs. However, AI-generated footage often exhibits unwanted camera movement that can distract viewers and complicate encoding workflows. Before you feed these clips into advanced preprocessing engines for bandwidth optimization, stabilizing shaky footage is crucial for maximizing quality metrics and compression efficiency.

Why Stabilization Matters Before You Hit "Encode"

Veo 3's advanced capabilities include realistic physics simulation and sophisticated camera movements, but these features can sometimes introduce unintended jitter. When shaky footage goes through encoding pipelines, the instability creates additional motion vectors that encoders must process, leading to inefficient bit allocation and reduced perceptual quality.

Stabilization directly impacts your video quality metrics. By reducing unwanted camera movement before encoding, you create cleaner frames that compress more efficiently. This preprocessing step becomes especially important when using AI-powered solutions like SimaBit, which achieved 22% average bitrate reduction while delivering a 4.2-point VMAF quality increase—but these gains depend on starting with stable source material.

FFmpeg provides several ways to stabilize shaky videos, from simple built-in filters to advanced multi-pass approaches. Understanding these options helps you choose the right stabilization method for your Veo 3 content before it enters your encoding pipeline.

Start in Veo 3: Writing a Static-Camera Prompt

The most effective stabilization happens at the source. A well-structured prompt typically includes subject, scene, action, style, camera movement, composition, and atmosphere. By explicitly controlling camera movement in your prompts, you can prevent shake before it occurs.

Camera: tripod lock-off (no pan/tilt/roll) with the subject centered creates the most stable output. When generating clips, keep your action conservative and reuse the same reference images and descriptors across multiple generations to maintain consistency.

Google's prompt guide encourages plain-language camera directions. Instead of complex movements, specify "static camera" or "locked tripod shot" in your prompts. This approach tells Veo 3 to minimize camera motion, resulting in footage that requires less post-processing stabilization.

When Prompts Aren't Enough: FFmpeg's Two Paths to Stability

Deshake filter is the simplest and quickest method to stabilize shaky video in FFmpeg. It analyzes frame-to-frame motion and applies compensating transforms, making it ideal for minor shake corrections without extensive processing time.

The recommended method for stabilizing videos with FFmpeg is to use the VidStab library, which requires a build of FFmpeg compiled with --enable-libvidstab. VidStab offers more sophisticated stabilization through a two-pass process that first detects motion patterns then applies smoothing transformations.

Stabilization is a lossy process that can reduce video quality due to zoom and interpolation effects. The choice between deshake and VidStab depends on your footage characteristics: deshake works well for subtle shake, while VidStab excels with more complex motion that requires aggressive correction.

The One-Line Deshake Command You Can Copy-Paste

For quick stabilization of Veo 3 clips, FFmpeg's deshake filter offers immediate results with minimal setup. Here's the command that handles most stabilization needs:

ffmpeg -i input_video.mp4 -vf deshake=rx=16:ry=16:edge=none -c:a copy output_video.mp4

This command applies deshake with 16-pixel search radius in both directions and prevents edge artifacts. The audio stream copies directly without re-encoding, preserving quality while processing only the video.

For more aggressive stabilization, VidStab provides finer control. The first pass uses vidstabdetect to analyse the video and output stabilisation data to a file called transforms.trf. This analysis captures motion patterns that the second pass uses to create smooth, stable output.

ffmpeg -y -i "input.mp4" -vf vidstabdetect=shakiness=10:accuracy=15:stepsize=2:result="transform.trf" -an -f null -

The shakiness parameter set to 10 captures aggressive movement, while accuracy at 15 ensures precise motion detection. These settings work particularly well for AI-generated content where motion patterns may differ from traditional camera shake.

Visualize Motion Vectors & Quantify VMAF Gains

FFmpeg has the ability to stabilise a video using a 2 pass approach that allows you to visualize the stabilization process. After running your stabilization filter, you can measure the improvement using industry-standard metrics.

VMAF is a video quality metric that Netflix jointly developed with university collaborators. It combines human vision modeling with machine learning to predict perceived quality. VMAF scores range from 0 to 100, with higher scores indicating better quality.

To visualize motion changes, you can process the changes in pixels between frames. This helps confirm that your stabilization reduced unwanted movement. Compare the before and after footage by overlaying motion vectors or generating difference maps that highlight areas of movement.

SimaBit achieved 22% average reduction in bitrate while delivering a 4.2-point VMAF quality increase. When you stabilize footage before SimaBit processing, these gains compound—cleaner source material enables more efficient preprocessing and better final quality.

Encode Smarter: Feeding the Stabilized Clip into SimaBit

The October 16, 2025 announcement of SimaBit's integration with Dolby Hybrik marks a pivotal moment for streaming infrastructure. SimaBit's AI engine analyzes content before it reaches the encoder, removing perceptual redundancies and optimizing bit allocation in real-time.

SimaBit processes 1080p frames in under 16 milliseconds, making it suitable for both live streaming and VOD workflows. When you feed stabilized Veo 3 content into SimaBit, the preprocessing engine can focus on optimizing visual quality rather than compensating for unwanted motion.

These sophisticated systems can reduce video bandwidth requirements by 22% or more while simultaneously boosting perceptual quality. Stabilized input footage maximizes these benefits by providing cleaner motion vectors and more predictable frame-to-frame changes that SimaBit can optimize more effectively.

Common Pitfalls: Cropping, Zoom & Quality Trade-offs

Existing video stabilization methods often generate visible distortion or require aggressive cropping of frame boundaries. When stabilizing Veo 3 content, you must balance stability against these potential quality losses.

vidstabtransform offers an option optzoom, which when set to value 2, will attempt to zoom in and out of the content to keep visible black borders outside the visible area. However, excessive zoom can reduce resolution and introduce interpolation artifacts.

Parameters such as shakiness, accuracy, and stepsize can be set to describe how shaky the video is. Setting these too aggressively can overcorrect, creating unnatural motion or losing important frame content. Start with moderate values and increase only if needed.

Key Takeaways

Stabilizing Veo 3 clips before encoding represents a critical optimization step in modern video workflows. By addressing camera shake at the prompt level and applying appropriate FFmpeg filters when needed, you create cleaner source material that compresses more efficiently.

The combination of proper prompt engineering, targeted stabilization, and AI-powered preprocessing like SimaBit's 22% average reduction in bitrate creates a powerful pipeline for AI-generated video content. Start with static camera prompts in Veo 3, apply FFmpeg's deshake or VidStab as needed, verify improvements with VMAF scoring, and then leverage SimaBit's preprocessing to maximize both quality and bandwidth efficiency.

Whether you're working with subtle handheld effects or correcting unexpected AI-generated motion, these stabilization techniques ensure your content is optimized before it enters the encoding pipeline. The result is higher quality video that streams more efficiently—exactly what modern audiences and platforms demand.

Frequently Asked Questions

What’s the fastest way to stabilize Veo 3 clips before encoding?

Use FFmpeg’s deshake filter for quick, minor corrections. A one-liner like ffmpeg -i input.mp4 -vf "deshake=rx=16:ry=16:edge=none" -c:a copy output.mp4 removes small jitters with minimal setup and preserves audio.

When should I choose VidStab instead of deshake?

Pick VidStab for heavier or complex motion. Its two-pass workflow (vidstabdetect then vidstabtransform) analyzes patterns and applies smoother transforms, offering finer control than deshake for aggressive stabilization needs.

How do I prevent camera shake at the prompt stage in Veo 3?

Specify camera control directly in your prompt, e.g., "static camera" or "locked tripod shot," and keep action conservative. Reuse reference images and descriptors across generations to maintain stability and composition consistency.

Does stabilization reduce quality, and how can I manage crop/zoom trade-offs?

Stabilization can introduce cropping, zoom, and interpolation artifacts. Start with moderate parameters (e.g., deshake with edge handling or VidStab with conservative shakiness/accuracy) and only increase aggressiveness if residual jitter remains.

How can I quantify improvements after stabilization?

Measure with VMAF and inspect motion vectors or difference maps. Compare before/after scores and visual overlays to confirm reduced unwanted motion and improved perceptual quality.

How does stabilization affect SimaBit results?

Cleaner, stabilized input lets SimaBit focus on perceptual redundancies rather than compensating for jitter. According to Sima Labs’ resources on the Dolby Hybrik integration, SimaBit delivers ~22% average bitrate reduction with a 4.2-point VMAF lift; stabilizing first helps maximize these gains.

Sources

From Camera Shake to Cinematic: Stabilizing Veo 3 Clips Before SimaBit Encoding

Google's Veo 3 represents a significant leap in AI video generation, offering capabilities like native audio generation and 4K resolution outputs. However, AI-generated footage often exhibits unwanted camera movement that can distract viewers and complicate encoding workflows. Before you feed these clips into advanced preprocessing engines for bandwidth optimization, stabilizing shaky footage is crucial for maximizing quality metrics and compression efficiency.

Why Stabilization Matters Before You Hit "Encode"

Veo 3's advanced capabilities include realistic physics simulation and sophisticated camera movements, but these features can sometimes introduce unintended jitter. When shaky footage goes through encoding pipelines, the instability creates additional motion vectors that encoders must process, leading to inefficient bit allocation and reduced perceptual quality.

Stabilization directly impacts your video quality metrics. By reducing unwanted camera movement before encoding, you create cleaner frames that compress more efficiently. This preprocessing step becomes especially important when using AI-powered solutions like SimaBit, which achieved 22% average bitrate reduction while delivering a 4.2-point VMAF quality increase—but these gains depend on starting with stable source material.

FFmpeg provides several ways to stabilize shaky videos, from simple built-in filters to advanced multi-pass approaches. Understanding these options helps you choose the right stabilization method for your Veo 3 content before it enters your encoding pipeline.

Start in Veo 3: Writing a Static-Camera Prompt

The most effective stabilization happens at the source. A well-structured prompt typically includes subject, scene, action, style, camera movement, composition, and atmosphere. By explicitly controlling camera movement in your prompts, you can prevent shake before it occurs.

Camera: tripod lock-off (no pan/tilt/roll) with the subject centered creates the most stable output. When generating clips, keep your action conservative and reuse the same reference images and descriptors across multiple generations to maintain consistency.

Google's prompt guide encourages plain-language camera directions. Instead of complex movements, specify "static camera" or "locked tripod shot" in your prompts. This approach tells Veo 3 to minimize camera motion, resulting in footage that requires less post-processing stabilization.

When Prompts Aren't Enough: FFmpeg's Two Paths to Stability

Deshake filter is the simplest and quickest method to stabilize shaky video in FFmpeg. It analyzes frame-to-frame motion and applies compensating transforms, making it ideal for minor shake corrections without extensive processing time.

The recommended method for stabilizing videos with FFmpeg is to use the VidStab library, which requires a build of FFmpeg compiled with --enable-libvidstab. VidStab offers more sophisticated stabilization through a two-pass process that first detects motion patterns then applies smoothing transformations.

Stabilization is a lossy process that can reduce video quality due to zoom and interpolation effects. The choice between deshake and VidStab depends on your footage characteristics: deshake works well for subtle shake, while VidStab excels with more complex motion that requires aggressive correction.

The One-Line Deshake Command You Can Copy-Paste

For quick stabilization of Veo 3 clips, FFmpeg's deshake filter offers immediate results with minimal setup. Here's the command that handles most stabilization needs:

ffmpeg -i input_video.mp4 -vf deshake=rx=16:ry=16:edge=none -c:a copy output_video.mp4

This command applies deshake with 16-pixel search radius in both directions and prevents edge artifacts. The audio stream copies directly without re-encoding, preserving quality while processing only the video.

For more aggressive stabilization, VidStab provides finer control. The first pass uses vidstabdetect to analyse the video and output stabilisation data to a file called transforms.trf. This analysis captures motion patterns that the second pass uses to create smooth, stable output.

ffmpeg -y -i "input.mp4" -vf vidstabdetect=shakiness=10:accuracy=15:stepsize=2:result="transform.trf" -an -f null -

The shakiness parameter set to 10 captures aggressive movement, while accuracy at 15 ensures precise motion detection. These settings work particularly well for AI-generated content where motion patterns may differ from traditional camera shake.

Visualize Motion Vectors & Quantify VMAF Gains

FFmpeg has the ability to stabilise a video using a 2 pass approach that allows you to visualize the stabilization process. After running your stabilization filter, you can measure the improvement using industry-standard metrics.

VMAF is a video quality metric that Netflix jointly developed with university collaborators. It combines human vision modeling with machine learning to predict perceived quality. VMAF scores range from 0 to 100, with higher scores indicating better quality.

To visualize motion changes, you can process the changes in pixels between frames. This helps confirm that your stabilization reduced unwanted movement. Compare the before and after footage by overlaying motion vectors or generating difference maps that highlight areas of movement.

SimaBit achieved 22% average reduction in bitrate while delivering a 4.2-point VMAF quality increase. When you stabilize footage before SimaBit processing, these gains compound—cleaner source material enables more efficient preprocessing and better final quality.

Encode Smarter: Feeding the Stabilized Clip into SimaBit

The October 16, 2025 announcement of SimaBit's integration with Dolby Hybrik marks a pivotal moment for streaming infrastructure. SimaBit's AI engine analyzes content before it reaches the encoder, removing perceptual redundancies and optimizing bit allocation in real-time.

SimaBit processes 1080p frames in under 16 milliseconds, making it suitable for both live streaming and VOD workflows. When you feed stabilized Veo 3 content into SimaBit, the preprocessing engine can focus on optimizing visual quality rather than compensating for unwanted motion.

These sophisticated systems can reduce video bandwidth requirements by 22% or more while simultaneously boosting perceptual quality. Stabilized input footage maximizes these benefits by providing cleaner motion vectors and more predictable frame-to-frame changes that SimaBit can optimize more effectively.

Common Pitfalls: Cropping, Zoom & Quality Trade-offs

Existing video stabilization methods often generate visible distortion or require aggressive cropping of frame boundaries. When stabilizing Veo 3 content, you must balance stability against these potential quality losses.

vidstabtransform offers an option optzoom, which when set to value 2, will attempt to zoom in and out of the content to keep visible black borders outside the visible area. However, excessive zoom can reduce resolution and introduce interpolation artifacts.

Parameters such as shakiness, accuracy, and stepsize can be set to describe how shaky the video is. Setting these too aggressively can overcorrect, creating unnatural motion or losing important frame content. Start with moderate values and increase only if needed.

Key Takeaways

Stabilizing Veo 3 clips before encoding represents a critical optimization step in modern video workflows. By addressing camera shake at the prompt level and applying appropriate FFmpeg filters when needed, you create cleaner source material that compresses more efficiently.

The combination of proper prompt engineering, targeted stabilization, and AI-powered preprocessing like SimaBit's 22% average reduction in bitrate creates a powerful pipeline for AI-generated video content. Start with static camera prompts in Veo 3, apply FFmpeg's deshake or VidStab as needed, verify improvements with VMAF scoring, and then leverage SimaBit's preprocessing to maximize both quality and bandwidth efficiency.

Whether you're working with subtle handheld effects or correcting unexpected AI-generated motion, these stabilization techniques ensure your content is optimized before it enters the encoding pipeline. The result is higher quality video that streams more efficiently—exactly what modern audiences and platforms demand.

Frequently Asked Questions

What’s the fastest way to stabilize Veo 3 clips before encoding?

Use FFmpeg’s deshake filter for quick, minor corrections. A one-liner like ffmpeg -i input.mp4 -vf "deshake=rx=16:ry=16:edge=none" -c:a copy output.mp4 removes small jitters with minimal setup and preserves audio.

When should I choose VidStab instead of deshake?

Pick VidStab for heavier or complex motion. Its two-pass workflow (vidstabdetect then vidstabtransform) analyzes patterns and applies smoother transforms, offering finer control than deshake for aggressive stabilization needs.

How do I prevent camera shake at the prompt stage in Veo 3?

Specify camera control directly in your prompt, e.g., "static camera" or "locked tripod shot," and keep action conservative. Reuse reference images and descriptors across generations to maintain stability and composition consistency.

Does stabilization reduce quality, and how can I manage crop/zoom trade-offs?

Stabilization can introduce cropping, zoom, and interpolation artifacts. Start with moderate parameters (e.g., deshake with edge handling or VidStab with conservative shakiness/accuracy) and only increase aggressiveness if residual jitter remains.

How can I quantify improvements after stabilization?

Measure with VMAF and inspect motion vectors or difference maps. Compare before/after scores and visual overlays to confirm reduced unwanted motion and improved perceptual quality.

How does stabilization affect SimaBit results?

Cleaner, stabilized input lets SimaBit focus on perceptual redundancies rather than compensating for jitter. According to Sima Labs’ resources on the Dolby Hybrik integration, SimaBit delivers ~22% average bitrate reduction with a 4.2-point VMAF lift; stabilizing first helps maximize these gains.

Sources

From Camera Shake to Cinematic: Stabilizing Veo 3 Clips Before SimaBit Encoding

Google's Veo 3 represents a significant leap in AI video generation, offering capabilities like native audio generation and 4K resolution outputs. However, AI-generated footage often exhibits unwanted camera movement that can distract viewers and complicate encoding workflows. Before you feed these clips into advanced preprocessing engines for bandwidth optimization, stabilizing shaky footage is crucial for maximizing quality metrics and compression efficiency.

Why Stabilization Matters Before You Hit "Encode"

Veo 3's advanced capabilities include realistic physics simulation and sophisticated camera movements, but these features can sometimes introduce unintended jitter. When shaky footage goes through encoding pipelines, the instability creates additional motion vectors that encoders must process, leading to inefficient bit allocation and reduced perceptual quality.

Stabilization directly impacts your video quality metrics. By reducing unwanted camera movement before encoding, you create cleaner frames that compress more efficiently. This preprocessing step becomes especially important when using AI-powered solutions like SimaBit, which achieved 22% average bitrate reduction while delivering a 4.2-point VMAF quality increase—but these gains depend on starting with stable source material.

FFmpeg provides several ways to stabilize shaky videos, from simple built-in filters to advanced multi-pass approaches. Understanding these options helps you choose the right stabilization method for your Veo 3 content before it enters your encoding pipeline.

Start in Veo 3: Writing a Static-Camera Prompt

The most effective stabilization happens at the source. A well-structured prompt typically includes subject, scene, action, style, camera movement, composition, and atmosphere. By explicitly controlling camera movement in your prompts, you can prevent shake before it occurs.

Camera: tripod lock-off (no pan/tilt/roll) with the subject centered creates the most stable output. When generating clips, keep your action conservative and reuse the same reference images and descriptors across multiple generations to maintain consistency.

Google's prompt guide encourages plain-language camera directions. Instead of complex movements, specify "static camera" or "locked tripod shot" in your prompts. This approach tells Veo 3 to minimize camera motion, resulting in footage that requires less post-processing stabilization.

When Prompts Aren't Enough: FFmpeg's Two Paths to Stability

Deshake filter is the simplest and quickest method to stabilize shaky video in FFmpeg. It analyzes frame-to-frame motion and applies compensating transforms, making it ideal for minor shake corrections without extensive processing time.

The recommended method for stabilizing videos with FFmpeg is to use the VidStab library, which requires a build of FFmpeg compiled with --enable-libvidstab. VidStab offers more sophisticated stabilization through a two-pass process that first detects motion patterns then applies smoothing transformations.

Stabilization is a lossy process that can reduce video quality due to zoom and interpolation effects. The choice between deshake and VidStab depends on your footage characteristics: deshake works well for subtle shake, while VidStab excels with more complex motion that requires aggressive correction.

The One-Line Deshake Command You Can Copy-Paste

For quick stabilization of Veo 3 clips, FFmpeg's deshake filter offers immediate results with minimal setup. Here's the command that handles most stabilization needs:

ffmpeg -i input_video.mp4 -vf deshake=rx=16:ry=16:edge=none -c:a copy output_video.mp4

This command applies deshake with 16-pixel search radius in both directions and prevents edge artifacts. The audio stream copies directly without re-encoding, preserving quality while processing only the video.

For more aggressive stabilization, VidStab provides finer control. The first pass uses vidstabdetect to analyse the video and output stabilisation data to a file called transforms.trf. This analysis captures motion patterns that the second pass uses to create smooth, stable output.

ffmpeg -y -i "input.mp4" -vf vidstabdetect=shakiness=10:accuracy=15:stepsize=2:result="transform.trf" -an -f null -

The shakiness parameter set to 10 captures aggressive movement, while accuracy at 15 ensures precise motion detection. These settings work particularly well for AI-generated content where motion patterns may differ from traditional camera shake.

Visualize Motion Vectors & Quantify VMAF Gains

FFmpeg has the ability to stabilise a video using a 2 pass approach that allows you to visualize the stabilization process. After running your stabilization filter, you can measure the improvement using industry-standard metrics.

VMAF is a video quality metric that Netflix jointly developed with university collaborators. It combines human vision modeling with machine learning to predict perceived quality. VMAF scores range from 0 to 100, with higher scores indicating better quality.

To visualize motion changes, you can process the changes in pixels between frames. This helps confirm that your stabilization reduced unwanted movement. Compare the before and after footage by overlaying motion vectors or generating difference maps that highlight areas of movement.

SimaBit achieved 22% average reduction in bitrate while delivering a 4.2-point VMAF quality increase. When you stabilize footage before SimaBit processing, these gains compound—cleaner source material enables more efficient preprocessing and better final quality.

Encode Smarter: Feeding the Stabilized Clip into SimaBit

The October 16, 2025 announcement of SimaBit's integration with Dolby Hybrik marks a pivotal moment for streaming infrastructure. SimaBit's AI engine analyzes content before it reaches the encoder, removing perceptual redundancies and optimizing bit allocation in real-time.

SimaBit processes 1080p frames in under 16 milliseconds, making it suitable for both live streaming and VOD workflows. When you feed stabilized Veo 3 content into SimaBit, the preprocessing engine can focus on optimizing visual quality rather than compensating for unwanted motion.

These sophisticated systems can reduce video bandwidth requirements by 22% or more while simultaneously boosting perceptual quality. Stabilized input footage maximizes these benefits by providing cleaner motion vectors and more predictable frame-to-frame changes that SimaBit can optimize more effectively.

Common Pitfalls: Cropping, Zoom & Quality Trade-offs

Existing video stabilization methods often generate visible distortion or require aggressive cropping of frame boundaries. When stabilizing Veo 3 content, you must balance stability against these potential quality losses.

vidstabtransform offers an option optzoom, which when set to value 2, will attempt to zoom in and out of the content to keep visible black borders outside the visible area. However, excessive zoom can reduce resolution and introduce interpolation artifacts.

Parameters such as shakiness, accuracy, and stepsize can be set to describe how shaky the video is. Setting these too aggressively can overcorrect, creating unnatural motion or losing important frame content. Start with moderate values and increase only if needed.

Key Takeaways

Stabilizing Veo 3 clips before encoding represents a critical optimization step in modern video workflows. By addressing camera shake at the prompt level and applying appropriate FFmpeg filters when needed, you create cleaner source material that compresses more efficiently.

The combination of proper prompt engineering, targeted stabilization, and AI-powered preprocessing like SimaBit's 22% average reduction in bitrate creates a powerful pipeline for AI-generated video content. Start with static camera prompts in Veo 3, apply FFmpeg's deshake or VidStab as needed, verify improvements with VMAF scoring, and then leverage SimaBit's preprocessing to maximize both quality and bandwidth efficiency.

Whether you're working with subtle handheld effects or correcting unexpected AI-generated motion, these stabilization techniques ensure your content is optimized before it enters the encoding pipeline. The result is higher quality video that streams more efficiently—exactly what modern audiences and platforms demand.

Frequently Asked Questions

What’s the fastest way to stabilize Veo 3 clips before encoding?

Use FFmpeg’s deshake filter for quick, minor corrections. A one-liner like ffmpeg -i input.mp4 -vf "deshake=rx=16:ry=16:edge=none" -c:a copy output.mp4 removes small jitters with minimal setup and preserves audio.

When should I choose VidStab instead of deshake?

Pick VidStab for heavier or complex motion. Its two-pass workflow (vidstabdetect then vidstabtransform) analyzes patterns and applies smoother transforms, offering finer control than deshake for aggressive stabilization needs.

How do I prevent camera shake at the prompt stage in Veo 3?

Specify camera control directly in your prompt, e.g., "static camera" or "locked tripod shot," and keep action conservative. Reuse reference images and descriptors across generations to maintain stability and composition consistency.

Does stabilization reduce quality, and how can I manage crop/zoom trade-offs?

Stabilization can introduce cropping, zoom, and interpolation artifacts. Start with moderate parameters (e.g., deshake with edge handling or VidStab with conservative shakiness/accuracy) and only increase aggressiveness if residual jitter remains.

How can I quantify improvements after stabilization?

Measure with VMAF and inspect motion vectors or difference maps. Compare before/after scores and visual overlays to confirm reduced unwanted motion and improved perceptual quality.

How does stabilization affect SimaBit results?

Cleaner, stabilized input lets SimaBit focus on perceptual redundancies rather than compensating for jitter. According to Sima Labs’ resources on the Dolby Hybrik integration, SimaBit delivers ~22% average bitrate reduction with a 4.2-point VMAF lift; stabilizing first helps maximize these gains.