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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av1 libaom video codec's Coding Tools Analysis by visionular

libaom Coding Tools Analysis

This article comprises two parts. In part one I focused on providing analysis of the AV1 coding tools that in this part 2 will become the basis for examining libaom to determine which can be most useful in the pursuit of better coding performance (speed) and efficiency (bitrate savings). Some video engineers are claiming that there are too many parameters provided by libaom and though there certainly are many choices and this can lead to confusion about which to use, I’ve performed much of the analysis and hope that this post will be useful as you consider your own unique application and use...

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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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AV1 Encoder Optimization from the Decoder’s Point of View

Demuxed 2020 was a “don’t miss” event. More than 1,000 video engineers participated, and 28 video engineers, codec developers, and image scientists from Netflix, Disney, Facebook, Apple, LinkedIn, Akamai, Fastly, Cloudflare, VideoLan, and Visionular shared best practices, open-source projects, and the latest industry development trends in video. Our very own Zoe Liu presented a talk about optimizing an encoder from the perspective of the decoder. Video engineers understand the importance of ensuring playback stability and the role that the encoder plays. AV1 contains more than 100 coding tools as compared with its predecessor VP9. This is the key to AV1’s significantly improved coding...

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state of the AV1 video coding standard in Q4 of 2020

AV1 Had a Huge Q4 – Ten Updates

Since the founding of Visionular, we have been passionately focused on building the AV1 standard by developing the best AV1 codec implementation possible. Aside from assembling the largest dedicated group of codec engineers in the industry, we have taken every opportunity to promote the AV1 standard as active members of the AOM. In the fourth quarter of this year, the team was especially busy. Here are a few of the more notable AV1 centric activities that we led or took part in. In October, Mark Donnigan moderated a panel at the RTE2020 virtual conference titled “Selecting The Best Video Codec To Scale Your Apps for RTE” where he was...

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Dan Rayburn: Continuous Innovation is a Competitive Strategy

Dan Rayburn recently quoted me in an article he wrote titled, “Continuous Innovation Is a Competitive Strategy: Why 870 Million Monthly Users in Asia Depend on HEVC and AV1 Codec Standards.” When writing this blog post, I feel proud that in December 2020, Visionular is now supplying essential video encoding solutions to more than thirty leading video streaming app companies, services, and hyperscale platforms. The use cases span RTC and other low latency real-time applications, as well as VOD. Companies like Google,, Mobiuspace, ShareChat, Firework, H3C, Juphoon, Lomotif, Jumu, NetEase, SHAREit, TAL, Baidu, and others utilize our codec solutions to maintain...

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TL;DR: Watch this video to get the essential data in 34 seconds. Here’s what you are going to see: Aurora1 beats the venerable NVIDIA NVENC HEVC encoder that is built-in to the popular Tesla 4 by delivering the same quality or higher at exactly half the bitrate (5Mbps for 1080p60). In order to compare appropriately, Aurora1 was operated in its real-time mode as required for cloud gaming and live streaming services.Video engineers seeking to optimize the quality of video that they stream face many challenges. Everything from the codec standard selected to the choice of resolution and bitrate to the...

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Zoe Liu Startup Interview Oct 2021

LiveVideoStack, one of the largest video technical communities globally, talked recently with Zoe Liu, our Co-Founder, and CTO about Entrepreneurship and startup lessons that she has learned. Following are highlights from the conversation.LVS: What is the most significant insight that entrepreneurship has brought you? Zoe: One word – “Growth.” Before starting my business, I was a pure engineer. It wasn’t until I came out of Google after 18 years in R&D and deep tech development with Apple, Nokia, and a few other companies that I gained a fundamental understanding of the market and what it takes to launch and sell the...

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fast AV1 encoding using visionular's aurora1 av1 encoder

Fast AV1 Encoding Is Finally Here

For years “codec complexity” ergo “high computational cost” has hung over the AV1 codec, causing some to hold off adopting the standard. Since video codec developers are continuously improving the performance of their codec implementations, this assumption doesn’t have to be true. The secret to breaking the operational cost barrier for AV1 is to use an encoder that was built from the ground up, to take full advantage of all of the incredible encoding tools AV1 has to offer. Through joint core RDO optimization and adaptive AI-based video processing, we pull all those levers and squeeze every ounce of quality out of...

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