Per Title, per Shot and Adaptive Encoding with IRIS.ANALYST

Different types of video content have different bitrate requirements to reach a high-quality experience for the viewer. While we don’t like to spend too much bitrate, we also like to reach the best possible picture quality. Spatial and temporal complexity are parameters, which will help to define the individual bitrates per title or per shot. Video noise and grain are part of the spatial and temporal signal components of video and as we learned from NETFLIX, can make a 30% difference when it comes to bitrate requirements. We can confirm this 30% bit-rate reduction at Wavelet Beam

IRIS.ANALYST Feature Set:

• Auto-Selecting Video Noise Profiles

• Measuring video resolution without a test target in the video

• Measuring Video Noise Levels on the fly

• Spatial and temporal complexity measurement

• Contrast and Brightness

Revolutionize Your Video Quality with IRIS: The Ultimate Video Noise Management Solution by Wavelet Beam

Wavelet Beam's IRIS Video Noise Management is here to transform your experience! Our innovative technology helps encoders distinguish between noise and actual image details, resulting in a higher perceived resolution in the encoded video and ultra-low video bitrates.

IRIS's fully automatic process ensures seamless integration into your workflow, whether you're using cluster, cloud, or on-premises systems. Our solutions are highly scalable and energy-efficient, making them ideal for processing huge video archives on a single system.

With IRIS, Wavelet Beam customers can enjoy higher video resolution, optimized video bitrate, and enhanced image quality. Our software also reduces video bitrate and faulty motion vectors, ensuring every detail is captured perfectly.

Experience hassle-free installation and integration with our SaaS solution, offering an attractive per-minute price. Say goodbye to low-quality videos and hello to high-resolution, optimized, and enhanced video with Wavelet Beam's IRIS Video Noise Management.

Wavelet Beam proudly presents IRIS.AI, the new solution for video upscaling

It is important to apply video denoising before AI-based upscaling because noise in a video can make it more difficult for upscaling algorithms to produce high-quality results. Upscaling algorithms use AI techniques such as deep learning to add additional pixels to the video, and the quality of the results will depend on the specific algorithm used and the quality of the original video. When a video contains noise, the upscaling algorithm can misinterpret the noise as actual image details, leading to unwanted artifacts and decreased visual quality in the upscaled video. By removing the noise from the video before upscaling, it is possible to provide the upscaling algorithm with a cleaner, higher-quality input, which can result in better upscaling results and improved visual quality in the upscaled video.

Wavelet Beam proudly presents IRIS.RAW, the new solution for video RAW data denoising:

Wavelet Beam proudly presents IRIS.RAW, the new solution for video RAW data denoising. Modern digital cameras are mostly single chip cameras, which are using a bayern pattern for capturing light. This means that for each pixel only one color channel is available for digitalization. Latest in postproduction the two missing colors have to be interpolated for each pixel (de-bayering). Every camera signal also has noise levels, which are producing inaccuracies in the de-bayering process. Removing and decreasing the noise levels before de-bayering will provide a much higher video resolution and HDR performance. Our solutions IRIS and IRIS.RAW are high-fidelity video denoising applications, which are GPU accelerated and one of the fastest on the market.