Video Enhancement - Frame-Rate Up Convert
See the difference Frame-Rate Up Convert can make
More about Video Enhancement
The process of encoding video involves more than just the encoding. Analyzing and preparing your source content is equally important. Visionular has developed a suite of AI-driven image enhancement processes that can intelligently apply the correct optimization before the encoder goes to work. Perhaps most importantly, our AI then encodes each video taking into consideration the pre-encode optimizations.
Frame-Rate Up Convert
One way to improve the viewing experience of older films is to use a technique called Intelligent Frame-Rate Up Convert. This process involves using artificial intelligence to create new frames that better match the refresh rates of today’s TVs and mobile devices. This can help to eliminate jerkiness or choppiness and other visual artifacts that can impact quality. This technique can help to improve the fidelity of older films when viewed on today’s devices.
Whether you deliver high-action footage at lower frame rates or need to show a dramatic slow-motion replay, ensure you have silky smooth playback, no matter the source footage.
Technology
Leveraging the deep learning-based framework, our frame interpolation engine takes advantage of the frame feature residual information to restore image details and effectively improve the accuracy of the interpolated frames.
Our Intelligent Frame Rate Interpolation Engine can determine whether the frame is appropriately using the bidirectional optical flow results and if needed, will modify the optical flow value for a cleaner interpolated frame.
Behind The Curtain
Intelligent Frame-Rate Up Conversion
Our frame rate up conversation technology, analyzes movement in a scene to figure out an object’s path based on its location in two consecutive frames. It thent creates an entirely new frame that is inserted smooth and crisp image.
The method based on deep learning optical flow estimation is presently the most effective overall when it comes to video frame interpolation. The following are some of the most popular current video frame interpolation algorithms:
FlowNet
FlowNet 2.0
