The multimedia learning group at the NELVT lab is dedicated to new theories, cutting-edge algorithms and core technologies for multimedia content analysis, coding and protection in a wide spectrum of next-generation multimedia applications. These technologies are expected to play a crucial role in the further development of digital media industry.
Featured Direction: Multimedia Big Data
Multimedia is increasingly becoming the “biggest big data” as the most important and valuable source for insights and information. It covers from everyone’s experiences to everything happening in the world. There will be lots of multimedia big data — surveillance video, entertainment and social media, medical images, consumer images, voice and video, to name a few, only if their volumes grow to the extent that the traditional multimedia processing and analysis systems cannot handle effectively. As such, multimedia big data will emerge as the next “must have” competency in our society, and is spurring on tremendous amounts of research and development of related technologies and applications.
Multimedia big data introduces many technological challenges, including compression, storage, transmission, analysis, recognition, and security. Among them, two major grand challenges are how to extra-efficiently compress the huge amount of data so as to facilitate transmission and storage, and how to intelligently analyze, mine and understand the multimedia information inside from such a huge amount of big data. Take surveillance video as an example. According to a recent report by IDC, by 2020, as much as 5,800 Exabytes of surveillance videos will be saved, transmitted and analyzed, averagely doubling the data volume every two years. However, traditionally, the average compression rate in the field of video coding increases ~2x every decade. This will lead to a huge gap between the two rates in future several years, consequently presenting an unprecedented challenge for ultra-high efficiency and low-complexity video coding technology. More importantly, only a small percentage of the data would be useful and valuable if they were tagged and analyzed. Yet, technology is far from where it needs to be, and in practice, only 3 percent of potentially useful data is tagged — and even less is currently being analyzed. In this sense, the huge amount of surveillance videos generated by thousands of cameras may become the data tsunami.
As an active and inter-disciplinary research field, multimedia big data also presents a great opportunity for multimedia computing in the big data era. The challenges and opportunities highlighted in this field will foster some interesting future developments in the multimedia research and applications.
2. Video Big Data: Challenges and Opportunities [in Chinese]