VideoLAN has launched a new version of the VLC media player that includes support for RTX Video Super Resolution (VSR), NVIDIA’s new technology for upscaling lower-quality video with the help of AI and a deep learning network. VLC 3.0.19 RTX Vetinari is a special version of the “Vetinari” branch of the popular media player with RTX upscaling, VideoLAN has confirmed, and according to the change log, this version of VLC activates VSR upscaling by default on NVIDIA GeForce RTX GPUs that support the feature (i.e., GeForce RTX 30 and 40 Series). Some users claim that NVIDIA VSR is capable of upscaling video just as well as madVR, the hugely popular video renderer from madshi that allows users to choose from various high-quality upscalers, including the GPU-intensive NGU models, but based on some of the comparisons that have been shared online (1, 2), reception of the results are sure to vary from user to user.
From an NVIDIA post:
RTX VSR is a breakthrough in AI pixel processing that dramatically improves the quality of streamed video content beyond edge detection and feature sharpening.
Blocky compression artifacts are a persistent issue in streamed video. Whether the fault of the server, the client or the content itself, issues often become amplified with traditional upscaling, leaving a less pleasant visual experience for those watching streamed content.
RTX VSR reduces or eliminates artifacts caused by compressing video — such as blockiness, ringing artifacts around edges, washout of high-frequency details and banding on flat areas — while reducing lost textures. It also sharpens edges and details.
The technology uses a deep learning network that performs upscaling and compression artifact reduction in a single pass. The network analyzes the lower-resolution video frame and predicts the residual image at the target resolution. This residual image is then superimposed on top of a traditional upscaled image, correcting artifact errors and sharpening edges to match the output resolution.
The deep learning network is trained on a wide range of content with various compression levels. It learns about types of compression artifacts present in low-resolution or low-quality videos that are otherwise absent in uncompressed images as a reference for network training. Extensive visual evaluation is employed to ensure that the generated model is effective on nearly all real-world and gaming content.