Paper co-authored by John Woods wins ISCAS Grand Challenge Top Creativity Award

ECSE Professor Emeritus John Woods co-authored "Wavelet-Based Learned Scalable Video Coding," with Cunhui Dong, Haichuan Ma, Dong Liu, all with the University of Science and Technology of China (USTC) that won the top creativity award of the ISCAS (IEEE International Symposium on Circuits and Systems) 2022 grand challenge.  ISCAS 2022 was held in Austin, Texas, from May 28 to June 1, 2022.

Abstract of this award-winning paper:

Scalability is an important requirement for video coding when coded videos stream over dynamic-bandwidth net- works. The state-of-the-art scalable video coding schemes adopt layer-based methods upon H.265, represented by the SHVC stan- dard. Compared to layer-based schemes, wavelet-based schemes were suspected less efficient for a long while. We try to improve the compression efficiency of wavelet-based scalable video coding by leveraging the recent progresses of deep learning. First, we propose an entropy coding method, using trained convolutional neural networks (CNNs) for probability estimation, to compress the wavelet-transformed subbands. Second, we design a CNN- based method for inverse temporal wavelet transform. We integrate the two proposed methods into a traditional wavelet-based scalable video coding scheme, named Interframe-EZBC. The two methods together achieve more than 20% bits savings. Then, our scheme outperforms the SHVC reference software by 9.09%, 6.55%, and 8.66% BD-rate reductions in YUV respectively.