Image and Video Compression Meets Computer Vision

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Recently tremendous progresses have been made in the theories of artificial intelligence, thanks to the advent of deep learning, a branch of machine learning theory that has been successfully applied in many fields and created numerous new business opportunities. In this program, we focus on the applications of deep learning in image/video compression and computer vision. Traditionally, image/video compression was mainly studied using signal processing, with little understanding of the contents, whereas computer vision aimed at understanding the contents using machine learning techniques. Since deep learning has become a powerful tool for both image/video compression and computer vision, it is time to unify the two fields more closely. Our long-term goal is to bridge the gap between image/video compression and computer vision. In the short term, we aim to develop new algorithms in the following topics: 1) deep learning-based image/video compression. 2) Compression/privacy-friendly designs for various computer vision tasks. The former will allow image/video compression schemes to achieve better compression of the images/videos by analyzing the contents of the images/videos. The latter will jointly design the architectures of the compression and computer vision to achieve better performance, and will ensure users' privacy in computer vision applications. The techniques we will employ in this program include advanced signal processing and machine learning tools such as trellis-coded quantization, range batch normalization, octave convolution, and federated learning. Video traffic currently accounts for about 80% of all internet traffic. As 5G mobile networks start to be deployed, more video traffics will be enabled. 5G will also significantly accelerate the adoption of various intelligent internet-of-things applications. By applying the theories and insights in image/video compression and computer vision to each other, we can obtain novel solutions with better performance, and create important paradigm shifts in both fields. It can also cultivate some innovative and disruptive applications that involve compression, computer vision, and deep learning. The privacy-preserving intelligent solutions developed in this program and their practical applications will make our life safer and more convenient without worrying about sacrificing our privacy. We also aim to develop fast and high-performance solutions that can be used in mobile and internet-of-things scenarios. This program will provide solid theoretical foundations and practical programming skills to the students involved, making them well prepared for their future careers in both image/video processing and computer vision. They will make valuable contributions to Canada's economy and help Canada to maintain its leadership position in the world in artificial intelligence and its applications.