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Fujitsu Developed AI Video Recognition with High-Quality Compression Technology
March 6, 2020 News

 

Fujitsu Laboratories Ltd. has developed a technology for compressing ultra-high-definition, high-volume video data to the minimum size needed for AI video recognition applications. This technology can compress video data to just one tenth the size of data prepared using conventional compression technology intended for visual confirmation by humans.

In recent years, there has been a sharp increase in demand for AI analysis of video data in various business areas. The spread of 5th-generation mobile communications system, is expected to contribute to an explosive increase in the number of ultra-high-definition video images captured by cameras, as well as many images captured on the street and on production lines.

In developing this new compression technology, Fujitsu focused on an important divergence in the way in which AI and humans recognise images. Namely, AI and humans tend to differ in the areas of the image that are emphasised as important for judgment when recognising people, animals, or objects in video data. Fujitsu has developed a technology to automatically analyse the areas that AI values and to compress data to the minimum size that AI can recognise. Without compromising recognition accuracy, this makes it possible to analyse a large amount of video data, and at the same time significantly reduce operating and data transmission costs. It is also anticipated that the technology will allow users to analyse more advanced video data by combining multiple video data stored in the cloud, sensor data, and performance data such as sales data.

 

Background and Challenges

Technology for analysing images using AI has been developing rapidly in recent years, and is expected to be one of the driving forces for digital transformation in many companies in a variety of industries. The demand for AI analysis is expected to increase even further with the sophisticated 5G mobile services arriving in 2020, accompanied by the increasing use of ultra-high-definition 4K and 8K cameras and large amounts of video data for applications including behavioural analysis in the manufacturing and retail industries.

Despite this, the processing demands for deep learning techniques used for image analysis present considerable challenges. One effective technique for securing computing power to deal with these tasks is to process in conjunction with the cloud, but since video data is often very resource-intensive, there is a need for high-compression technology that can transmit all video data to the cloud without compromising quality so that network bandwidth does not become overburdened.

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