4:2:2 cameras – capable of capturing twice the color information compared to most standard cameras – is now widely available to consumers. At the same time, the capabilities and quality of the generated AI video models are rapidly increasing, enabling new tools and workflows.
Based on the Nvidia Blackwell architecture, the Nvidia RTX GPU includes dedicated hardware for encoding and decoding 4:2:2 video, and comes with a fifth-generation tensor core designed to accelerate AI and deep learning workloads.
The GeForce RTX 50 series and NVIDIA RTX Pro Blackwell series are prepared to meet this demand, quickly reducing generation AI, new AI capabilities, cutting edge video editing workflows and faster exports.
4:2:2 will become mainstream
4:2:2 There are an increasing number of 10-bit compatible video cameras.
These cameras have traditionally been reserved for professional use due to their high cost, but become more cost-friendly and the major manufacturers offer them at prices under $600.
A 4:2:2 camera can capture twice as much color information as a standard 4:2:0 camera, only increasing the raw file size by 30%.
Standard cameras typically use 4:2:0 8-bit color compression and can capture only a small portion of the color information. While 4:2:0 is acceptable for video playback in browsers, professional video editors require a camera that captures 4:2:2 color accuracy and fidelity while keeping file size reasonable.
https://www.youtube.com/watch?v=r8r0md1dtza
The drawback of 4:2:2 is that additional color information requires more computing power to play, and often leads to stud sounds. As a result, many editors had to create a proxy before editing. This is a time-consuming process that requires additional storage and reduces fidelity during editing.
The GeForce RTX 50 series adds 4:2:2 hardware acceleration to decode and helps you solve this computational challenge. The RTX 50 Series GPU boasts 10x acceleration with 4:2:2 encoding and can decode up to 8k 75 frames per second, equivalent to a 10x 4K 30fps stream per decoder.
The most popular video editing apps, such as Davinci Resolve, Capcut and Wondershare Filmora from Blackmagic Design, support Nvidia hardware acceleration for 4:2:2 encoding and decoding. Adobe Premiere Pro offers decoding support.
Combining 4:2:2 support with Nvidia hardware increases your creative potential. 10-bit 4:2:2 holds more color information than 8-bit 4:2:0, resulting in more accurate color representation and better color grading results for video editors.
Additional color data from the 4:2:2 support provides greater flexibility during color correction and grading, allowing for more detailed adjustments. By improving the kayne, it allows for cleaner and more accurate extraction of subjects from the background, as well as sharp edges of smaller keyed objects.
Generated AI-equipped video editing
Generated AI models are to allow the video editor to generate filler videos, expand clips, change video styles, apply advanced visual effects with fast and ease of use, and significantly reduce production time.
Popular models such as WAN and LTX Video can generate high quality videos with faster accuracy and reduced load times.
Based on Nvidia Blackwell, GeForce RTX and NVIDIA RTX Pro GPUs enable these large and complex models to run quickly and on-device thanks to Pytorch's Nvidia Cuda optimization. Additionally, the fifth generation tensor cores of these GPUs provide support for FP4 quantization, allowing developers and enthusiasts to improve performance by more than twice as much and halve the VRAM they need.
https://www.youtube.com/watch?v=fuaebojcjw8
The cutting edge video editing AI functions
The latest video editing app offers an impressive array of advanced AI features accelerated by GeForce RTX and NVIDIA RTX Pro GPUs.
Davinci Resolve Studio 20 is now commonly released, adding new AI effects and integrates Nvidia Tensorrt to optimize AI performance. One of the new features, Ultranr Noise Reduction is an AI-driven noise reduction mode that intelligently targets and reduces digital noise in video footage, minimizing softening and maintains image transparency. Ultranr noise reduction is up to 75% faster than the previous generation on the GeForce RTX 5090 GPU.
https://www.youtube.com/watch?v=kxbxwa3rypu
Magic Mask is another AI-powered feature in Davinci Resolve that allows users to quickly and accurately select and track objects, people, or features within a scene, simplifying the process of creating masks and effects. Magic Mask v2 adds paint brushes to further adjust your masking choices for a more accurate and faster workflow.
https://www.youtube.com/watch?v=tj4te3S2U04
Topaz Video AI Pro Video Enhancement Software intelligently increases video resolution to 4K, 8K and even 16K using AI models such as Gaia and Artemis. This software also benefits from Tensort's acceleration.
https://www.youtube.com/watch?v=bio6g_sv8cc
The first local desktop spreading model for video enrichment, the Topaz Starlight Mini can enhance footage from tricky 8/16mm film to interlaced mini dv videos. This model offers exceptional quality at the expense of intensive computational requirements. only Run locally on the RTX GPU.
Adobe Premiere Pro recently released several new AI features, including Adobe Media Intelligence, which uses AI to analyze footage and apply semantic tags to clips. This allows users to find specific footage more easily and quickly by describing content that includes objects, locations, camera angles, and even transcribed audio. Media Intelligence runs 30% faster on a GeForce RTX 5090 laptop GPU compared to a GeForce RTX 4090 laptop GPU.
Adobe's Enhanced Audio Features improves the quality of recorded audio by filtering out unnecessary noise and making audio clearer. Extend your speech at 7x faster speeds on the GeForce RTX 5090 laptop GPU compared to the MacBook Pro M4 Max.
Cut like a professional
GeForce RTX and NVIDIA RTX Pro GPUs are built to provide the computational power needed for advanced video editing workflows.
These GPUs include a powerful NVIDIA Hardware Decoder (NVDEC) for smooth playback and unlocking scrubs of high-resolution video footage and multi-stream video without the need for a proxy. NVDEC is supported by Adobe Premiere Pro, Capcut, Davinci Resolve, Vegas Pro, and Wondershare Filmora.
The creative app uses these additional encoders on the GEFORCE RTX 5080 and 5090 GPUs, uses RTX Pro 6000, 5000, 4500, and 4000 Blackwell GPUs, and has 4:2:2 support.
Authors use, for example, RTX 5080 and 5090 to import 5x 8k30 or 20x 4k30 streams at once, or import 10x 4k60 for multi-camera editing, and check multiple camera angles without slowing down. With the RTX Pro 6000, this can be boosted to streams up to 10x 8k30 or 40x 4k30.
The NVIDIA CUDA core accelerates video and image processing effects such as motion tracking, sharpening, upsampling, transition effects, and other computationally intensive tasks. It also accelerates rendering time, allowing real-time previews while manipulating high-resolution video footage, speeding up AI features such as automatic color correction, object removal, and noise reduction.
When it's time to export, a video editor using GeForce RTX 50 Series 9th Generation NVIDIA Video Encoder can improve video quality by 5% with HEVC and AV1 Encoding (BD-BR), bringing high quality exports at the same bitrate.
Additionally, the new Ultra High Quality (UHQ) mode available with the latest Blackwell encoders offers an additional 5% higher quality with HEVC and AV1, and is backward compatible with the GeForce RTX 40 series.
Davinci Resolve, Capcut, and Filmora support multi-encoder encoding via split encoding where input frames are split into three parts, each split into three parts that are processed by a different NVENC encoder.
Join the keynote speech by Nvidia Founder and CEO Jensen Huang, held at Nvidia GTC Paris on June 11th at Vivatech. Check out our 2-day technical sessions, training and certifications on June 10th and at 2 days.
Stay tuned for more RTX and AI-powered advances in content creation.
Every week, RTX AI Garage The blog series features community-driven AI innovation and content for those looking to learn more about NIM microservices and AI blueprints, as well as buildings AI Agentcreative workflows, digital humans, productivity apps, and more.
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