Jump to Content
Cheng Chen

Cheng Chen

Cheng Chen received the B.S. degree in Automation from Tsinghua University at 2007. He received the M.S. and Ph.D. degrees in Electrical and Computer Engineering from the University to Iowa in 2014 and 2016. Dr. Chen's research interests at Google involve digital video coding and processing, digital image processing, and machine learning.
Authored Publications
Google Publications
Other Publications
Sort By
  • Title
  • Title, descending
  • Year
  • Year, descending
    AN OVERVIEW OF CORE CODING TOOLS IN THE AV1 VIDEO CODEC
    Adrian Grange
    Andrey Norkin
    Ching-Han Chiang
    Hui Su
    Jean-Marc Valin
    Luc Trudeau
    Nathan Egge
    Paul Wilkins
    Peter de Rivaz
    Sarah Parker
    Steinar Midtskogen
    Thomas Davies
    Zoe Liu
    The Picture Coding Symposium (PCS) (2018)
    Preview abstract AV1 is an emerging open-source and royalty-free video compression format, which is jointly developed and finalized in early 2018 by the Alliance for Open Media (AOMedia) industry consortium. The main goal of AV1 development is to achieve substantial compression gain over state-of-the-art codecs while maintaining practical decoding complexity and hardware feasibility. This paper provides a brief technical overview of key coding techniques in AV1 along with preliminary compression performance comparison against VP9 and HEVC. View details
    Preview abstract Screen content videos that typically contain computer generated texts and graphics are getting more demanding in nowadays online video service. They involve a great amount of circumstances that are not commonly seen in natural videos, including sharp edge transition and repetitive pattern, which make their statistical characteristics distinct from those of natural videos. This makes it questionable about the efficacy of the conventional discrete cosine transform (DCT), which builds on the Gauss-Markov model assumption that leads to a base-band signal, on coding the computer-generated graphics. This work exploits a class of staircase transforms. Unlike the DCT whose bases are samplings of sinusoidal functions, the staircase transforms have their bases sampled from staircase functions, which naturally better approximate the sharp transitions often encountered in the context of screen content. As an alternative transform kernel, the staircase transform is integrated into a hybrid transform coding scheme, in conjunction with DCT. It is experimentally shown that the proposed approach provides an average of 2.9% compression performance gains in terms of BD-rate reduction. A perceptual comparison further demonstrates that the use of staircase transform achieves substantial reduction in ringing artifact due to the Gibbs phenomenon. View details
    No Results Found