Video Enhancement - Image Quality Repair and Enhancement
See Visionular’s Image Repair and Enhancement Engine
Click the orange play button to see how you restore low-res or vintage footage with Visionular’s Image Repair and Enhancement Engine.
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 optimizations before the encoder goes to work. Perhaps most importantly, our AI communicates with the encoder, so it knows how to encode each video taking into account any pre-encode optimizations that were performed.
Image Quality Repair and Enhancement
Erase the mark of time by repairing scratches, blurriness, and image distortion on classic films. Restore faded colors breathing new life into films that have been degraded by time and neglect.
Our AI and ML-assisted video encoding technology adds extra fidelity to the filmmaker’s vision, preserving the artistic integrity of the original filmmaker.
Technology
To improve texture, sharpen details, and enhance the overall clarity of the source film using convolutional neural networks (CNN), we employ our proprietary technology and tools. Denoising, de-interlacing, sharpening, and other processing operations are used to improve texture, sharpness, and clarity in the original film through CNN.
To make the CNN network model more effective in restoring videos, we optimized our training data set with a variety of blur kernels, random downsampling, and coding compression cascade degradation detunes the high-definition data set so that a more realistic simulation can be used.
Behind The Curtain
Image Quality Repair and Enhancement Engine
Our image repair and enhancement tool combines de-interlacing, denoising, and sharpening, amongst other processes resulting in enhanced texture, sharpened details, and improved clarity using convolutional neural networks (CNN).
To improve the effectiveness of the CNN network model, we’ve optimized our training data set with a variety of blur kernels, random downsampling, and coding compression cascade degradation, which detunes the high-definition data set for a more realistic simulation.