AI-based Image and Video Compression on Mobile Neural Accelerators

移动神经加速器上基于人工智能的图像和视频压缩

• 批准号: 78200
• 金额: $ 34.5万
• 依托单位: DEEP RENDER LTD
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=78200
• 关键词: AI; based; Image; Video; Compression

Deep Render is a research-led startup founded by Imperial College graduates from the Department of Computing. We are a London based AI start-up that is developing the next generation of media compression algorithms. Our proprietary and patented technology is at the forefront of machine learning research, and our Biological Compression has already leapfrogged the best previous compression standards by up to 80% better efficiency. Additionally, while the traditional compression methods have hit peak-innovation with significantly declining performance gains over the past decades, Biological Compression is only starting to realise its full potential.As global data consumption is growing exponentially with more than four out of five bits of global internet traffic being image and video related, Deep Render's AI-based compression technology is vital to counteract upcoming broadband constraints. The outbreak of COVID-19 has accelerated this problematic trend even further, as a result of the crisis, internet usage has increased significantly. In particular, the demand for streaming, video-on-demand and remote-working tools has skyrocketed. The exponential increasing bandwidth demand creates a massive challenge, as seen by the EU's request asking big-tech companies to lower streaming resolutions to safeguard our communications infrastructure. Better compression technology is urgently needed to stop this problem from snowballing. Jeff Hecht states in Nature.com "Researchers are scrambling to repair and expand data pipes worldwide to keep the information revolution from grinding to a halt".This project is about implementing Deep Render's prior compression research on consumer products so that the average person can benefit from smaller file sizes, and consequently, higher bandwidth supply. Precisely, we aim to execute our software on neural accelerator chipsets of an iPhone 12 and a Galaxy S20\. The challenge is the performance-optimisation of our computational-hungry algorithm (neural networks) and to make it runnable in real-time on mobile platforms without loss of our superior compression efficiency. This task requires in-depth knowledge of mobile chipsets, as well as low-level software optimisation and engineering capability.To accomplish our goal, we propose a collaboration with Professor Paul Kelly from Imperial College London. Paul Kelly is a Professor of Software Technology, he is the lead for the Software Performance Optimisation Group, and he has a close affiliation to Imperial's Artificial Intelligence Network. We aim at leveraging his extraordinary expertise in high-performance software implementation and his prior research in low-level neural network optimisation to reach sub-16.6ms decoding times for 4K video on and iPhone 12 and Galaxy S20 device. Professor Kelly sits on the Advisory Board of Deep Render since June 2018 and has an excellent understanding of Deep Render's technology, and a great working relationship with its founders.Our value proposition is easy to understand. By making file sizes 80% smaller, we increase the bandwidth supply of the internet by a factor of up to 5\. Deep Render is going to help create a new age in which bandwidth constraints are a problem of the past. As a result of COVID-19, solving this problem has gained more importance, and Deep Render is determined to create a fast solution.

Deep Render是一家由帝国理工学院计算学系毕业生创立的研究型初创公司。我们是一家总部位于伦敦的人工智能初创公司，正在开发下一代媒体压缩算法。我们的专利和专利技术处于机器学习研究的前沿，我们的生物压缩已经超越了以前最好的压缩标准，效率提高了80%。此外，虽然传统的压缩方法在过去几十年中已经达到了创新的顶峰，性能收益大幅下降，但生物压缩才刚刚开始实现其全部潜力。随着全球数据消费呈指数级增长，全球互联网流量中超过五分之四与图像和视频相关，Deep Render的基于人工智能的压缩技术对于抵消即将到来的宽带限制至关重要。新冠肺炎的爆发进一步加速了这一有问题的趋势，由于这场危机，互联网使用量大幅增加。特别是，对流媒体、视频点播和远程工作工具的需求飙升。指数级增长的带宽需求带来了巨大的挑战，欧盟要求大型科技公司降低流媒体分辨率，以保护我们的通信基础设施。迫切需要更好的压缩技术来阻止这个问题的滚雪球。Jeff Hecht在Nature.com上写道：“研究人员正争先恐后地在全球范围内修复和扩展数据管道，以防止信息革命陷入停滞”。这个项目是关于实施Deep Render对消费产品的先前压缩研究，以便普通人可以从更小的文件大小中受益，从而获得更高的带宽供应。准确地说，我们的目标是在iPhone 12和Galaxy S20的神经加速器芯片组上执行我们的软件。我们面临的挑战是优化我们的计算密集型算法(神经网络)的性能，并使其可在移动平台上实时运行，而不会损失我们优越的压缩效率。这项任务需要深入了解移动芯片组，以及低水平的软件优化和工程能力。为了实现我们的目标，我们建议与伦敦帝国理工学院的保罗·凯利教授合作。保罗·凯利是一名软件技术教授，他是软件性能优化小组的负责人，他与帝国理工学院的人工智能网络有着密切的联系。我们的目标是利用他在高性能软件实施方面的非凡专业知识和他之前在低级别神经网络优化方面的研究，使iPhone 12和Galaxy S20设备上的4K视频解码时间达到16.6毫秒以下。Kelly教授自2018年6月以来一直担任Deep Render的顾问委员会成员，他对Deep Render的技术非常了解，并与其创始人建立了良好的工作关系。我们的价值主张很容易理解。通过将文件大小缩小80%，我们将互联网的带宽供应增加了高达5倍。深度渲染将帮助创造一个新时代，在这个时代，带宽限制已经成为过去的问题。由于新冠肺炎，解决这个问题变得更加重要，深度渲染决心创造一个快速的解决方案。