In the age of exponential growth of video conferencing and video on demand services, video compression has become more and more important. In the past few years, there has been a significant amount of progress in designing video compression techniques using machine learning to augment or replace the traditional video codecs. In this talk, I will discuss some of the key ideas shaping the next generation of learned video codecs, and how they improve upon some of the shortcomings of traditional codecs. In spite of the impressive strides, significant challenges remain in making the learned video codecs a reality. I will discuss some of the key challenges, and how we at WaveOne are working towards overcoming them.
- Santa Clara Valley Chapter of the IEEE Information Theory Society
- Santa Clara Valley Chapter of the IEEE Signal Processing Society
Biography: Kedar is a research scientist at WaveOne Inc. He received his Ph.D. from Stanford University in 2020, where he specialized in the field of data compression and information theory. He holds a B.Tech in Electrical Engineering Indian Institute of Technology, Bombay, and a M.S. from Stanford University. Kedar is the recipient of the Numerical Technologies Founders Prize at Stanford, and the Qualcomm Innovation Fellowship.