New Dynamic compression option for H264/H265
Why change the export options? Users frequently ask about the ‘best’ bitrate setting for their Video AI projects, and the answer depends on many different factors including the content of the video, its resolution, and the display size that it will be shown at.
A new solution: To simplify this step of AI processing, we’re introducing a more efficient way to export files in Video AI starting with NVIDIA and AMD GPU users on Windows.
- The new Dynamic mode in the Export panel offers three adaptive compression options for H264 and H265 videos. These presets are designed to help users create files that are only as large as they need to be and don’t waste disk space with high constant bitrates.
Quick Start: For NVIDIA and AMD users on Windows, just select Dynamic from the export options panel and try the medium preset as a starting point. It offers a good balance between file size and visual quality.
How it Works: Using Netflix’s VMAF video analysis tool, we’ve set three compression levels that balance file size and visual quality. For most normal viewing situations a score of 95 or higher is considered ‘visually lossless’, so we’ve set up the High preset to score over 95 while the other two settings are focused on file size optimization:
- High - 98.4 VMAF @ 391MB file size
- Medium - 88.3 VMAF @ 39.9MB file size
- Low - 79.3 VMAF @ 23.4MB file size
Increasing VMAF scores into the high 90s takes a significant amount of disk space, which is why the Medium and Low settings are so close in file size compared to the High setting.
- These settings use CQP rate control, which is a more efficient way of adapting the size of a video to the content that is being encoded. This means that simple scenes should use less bandwidth, while scenes with complex motion and detailed textures will avoid loss of detail.
- We’ll continue to adjust these settings based on user feedback and we plan to bring Dynamic Compression Levels to Apple Silicon and Intel ARC users very soon.
- For users looking for the closest setting to the previous “Auto” bitrate selection we recommend using the “Dynamic - High” mode to ensure file quality lines up with previous exports.
Examples of the three bitrate modes (zoomed to show fine detail):
In these images, you can see the low and medium settings show some blurring and smoothing on the skin texture, while the high setting is able to retain the finer detail of the original shot.
Video credits: “In the Hand” by Guido Pezz
Other improvements
Since last month’s roadmap update, we’ve also released a major internal rework of how filters work and fixed various model quality/performance issues:
- Improved visual quality on first few frames for almost all Enhancement models
- First few frames no longer repeat with Frame Interpolation
- Faster Iris 1x and 2x processing on macOS Ventura
- Apollo can now run on M1/M2 when used with HD+ inputs
- Improved preset management
- Create presets from anywhere
- Reworked custom output resolution flow
- Tesla GPUs now show NVENC H264 and H265 encoders
- Processing on Intel CPU machines (12th, 13th gen) no longer slows down when app is minimized or out of focus
- Fixes Mac issue where all memory would sometimes be used
- Exports / Previews now show filename when hovering over thumbnail
- Login now uses proxy settings
- Fixes image sequence issues when non-ASCII characters are in the path
- Fixed integer overflow issue for start frame number
- Added Iris to benchmark (Ctrl/⌘-B)
- Log file can now be opened when app is open (Ctrl/⌘-G)
- Added and improved tooltips around the app
We’ve also attempted to fix processing errors with Intel Arc / iGPU and when exporting a large number of frames. Please let us know if you experience related issues.
Please read the release threads for 3.4, 3.3.10, 3.3.9, and 3.3.6 for the full list of changes in each release.
Next
We have several foundational improvements in the pipeline that should significantly improve how useful Topaz Video AI is to you.
New Enhancement models
- Iris v2 - will offer better detail and improved slider responsiveness. Iris is targeted toward restoring low quality input videos with faces
- Improved results on medium-quality input videos, either through improving an existing model or training a new one
- New 1x denoising model to improve low light performance for high resolution input videos
Workflow improvements
- Direct integration with popular NLEs
- Improved preview experience that allows easier comparison between settings
- Allow offloading processing to a cloud backend
- Allow pausing and resuming processing
- Allow applying a second Enhancement pass without exporting and re-importing
We plan on shipping most of these improvements before the end of 2023. Please apply for the video beta program if you’d like to shape the development of these features. Thanks, and we look forward to hearing what you think!