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Biological Image and Video Compression

Biological Image and Video Compression
生物图像和视频压缩
批准号:
105768
负责人:
金额:
$63.5万
依托单位:
依托单位国家:
英国
项目类别:
Study
财政年份:
2019
资助国家:
英国
项目状态:
已结题
起止时间:
2019 至 --

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英文摘要
Deep Render Ltd is an Imperial College spin-out of 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. Furthermore, our Biological Image Compression results are state-of-the-art, already providing a 50% efficiency gain over the best previous compression standards.Our vision is to combine the fields of artificial intelligence, statistics and information theory to unlock the fundamental limits of image and video compression. The human eye is the best data compressor known to humanity -- with compression ratios at least 2,000 times better than everything developed to date. Biological Compression approximates the neurological processes of the human eye through a non-linear, learning-based approach, thereby creating a novel class of highly efficient compression algorithms. Moreover, while the traditional compression methods have hit peak-innovation with significantly declining performance gains, Biological Compression technology is only starting.Once Deep Render has completed the project, we will have an image compression codec at least 75% more efficient than the current state-of-the-art. Such an efficiency jump is the most significant inter-generation improvement the compression industry has ever seen. Further, we will have started to roll over these advances to video compression as well.Our target customers are the streaming industry (Netflix, YouTube, BBC, Sky), as well as the cloud-storage industry for images (Facebook, Instagram, Snapchat). Niche markets include VR/AR-streaming (Oculus), ultra-low latency live streaming (video calls, Skype), and all industries with large image/video storage needs such as the medical image processing market (NHS).Our value proposition is easy to understand. By making file sizes 75% smaller, we directly increase the bandwidth supply of the internet by a factor of 4, thus reducing data transport costs and operational overhead. Increasing the bandwidth supply by making file sizes smaller, is magnitudes more cost- and time-efficient than increasing the bandwidth supply through rewiring the globe with progressively more fibre-cables.Deep Render is going to help create a new age in which bandwidth constraints are a problem of the past. We are excitingly looking forward to seeing what amazing products people will build in such a world.
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国内基金
海外基金
基于CE-3及IMAGE卫星地球等离子体层EUV探测数据的反演研究
Raw-Image微小物体高精度位姿测量法
  • 批准号:
    61105029
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    22.0万元
  • 批准年份:
    2011
  • 负责人:
    宋薇
  • 依托单位: