Visionular ML Models & Image Processing Technology

Through the use of machine learning models, noise modeling and the noise strength that is inherent in videos can be analyzed and estimated and then used to guide the processing of denoising, quality enhancement, and encoding optimization. Video Noise Estimation can effectively estimate the strength of compression noise and Gaussian noise. We pair these two types of noises as an indicator for Image Quality Assessment benchmarking, namely IQA=(compression noise strength, Gaussian Noise strength). As denoted in the following figure, the left image has an IQA score of (2.34, 0.0), indicating that the image has a strong compression noise but quite weak Gaussian...

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Visionular Intelligent Optimization Technology

This paper provides an introduction with technical details about Visionular’s Content-Adaptive Encoding (CAE) Intelligent Optimization technology that combines content-adaptive encoding algorithms which operate deep inside the codec, and are powered by advanced machine learning processes, image processing, and image enhancement, and controlled by a subjectively aligned quality assessment mechanism that provides the most effective video encoding solutions on the market. Built for maximum flexibility, and modern workflows, our Intelligent Optimization technology works across all use-cases from premium VOD, live broadcast streaming, to ultra-low latency RTC video conferencing and communications applications. Improved visual quality. Regardless of bandwidth limitations, our encoders are able to...

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Learning-Based Multi-Frame Video Quality Enhancement

IEEE 2019 ICIP presentation learning-based multi-frame video quality enhancement visionular This paper was presented by Junchao Tong, Xilin Wu, Dandan Ding, Zheng Zhu, and Zoe Liu, “Learning-Based Multi-Frame Video Quality Enhancement,” in the Proceedings of the IEEE International Conference on Image Processing (ICIP), September 22-25, 2019 in Taipei, Taiwan. The convolution neural network (CNN) has shown great success in video quality enhancement. Existing methods mainly conduct enhancement tasks in the spatial domain, exploring the pixel correlations within one frame. Taking advantage of the similarity across successive frames, this paper demonstrates a learning-based multi-frame approach, with an aim to explore the greatest potential for video quality...

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Bi-Prediction Based Video Quality Enhancement via Learning

This paper was presented by Dandan Ding, Wenyu Wang, Junchao Tong, Xinbo Gao, Zoe Liu, and Yong Fang, “Bi-Prediction Based Video Quality Enhancement via Learning”, IEEE Transactions on Cybernetics, June 17, 2020. Convolutional neural networks (CNNs)-based video quality enhancement generally employs optical flow for pixel-wise motion estimation and compensation, followed by utilizing motion-compensated frames and jointly exploring the spatiotemporal correlation across frames to facilitate the enhancement. This method, called the optical-flow-based method (OPT), usually achieves high accuracy at the expense of high computational complexity. In this article, we develop a new framework, referred to as bi-prediction-based multi-frame video enhancement (PMVE), to achieve a...

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How To Deliver HDR-Like Video In An SDR Package

What is Pseudo HDR?  HDR, or High Dynamic Range video retains more detail in an image’s brightest and darkest portions than standard dynamic range (SDR). It also displays a broader gamut of colors and texture.   Some of the limitations of HDR are less about the footage itself and more about the infrastructure required to view it. For example, many devices still do not support HDR footage.  Add to that the higher bandwidth needed to deliver it and you have significantly limited who can watch HDR originated or produced content.  Visionular provides HDR-like quality video while keeping it in an SDR format. We refer...

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Unlocking Superior Visual Quality Using AI

There is a lot of hype around the role of AI and ML in video. But with so much marketing buzz, many video engineers struggle to make sense of the real from the fiction. In this presentation that was given during the August 2021 Streaming Connect virtual conference, we presented the latest cutting-edge research about how AI+Codec can unlock superior visual experiences for OTT streaming. CLICK TO LEARN MORE ABOUT AV1

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film restoration using Visionular's AuroraEnhancer technology

Using AI & ML to Enhance the Visual Appeal of Video

Stories are what bind us, and it has been this way throughout human history. Through stories, people share their society’s values, the knowledge of past generations and their dreams for the future. And there has never been a more powerful, immersive and beautiful way to tell those stories than through film. Enabling that film to look as good as it can is what drives our team of more than 50 video codec, algorithm, and signal processing engineers. The devices we stream video on today are unforgiving. Every flaw in the original source, bit of noise, and artifact, is under the microscope...

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