Wavelet-Based Learned Scalable Video Coding

Scalability is an important requirement for video coding when coded videos stream over dynamic-bandwidth networks. The state-of-the-art scalable video coding schemes adopt layer-based methods upon H.265, represented by the SHVC standard. 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.

I. Introduction

Video streaming is a fundamental technology for many video-based applications, such as live streaming, video on demand, real-time communications, and so on. In order to enable users to obtain the best video quality according to their current network bandwidth when using video streaming-based applications, scalable video coding technologies are believed promising. Scalable video coding compresses videos into coarse-to-fine hierarchical bitstreams. Users can determine the amount of bits used to decode videos according to their current bandwidth, which enables that the user can get the best possible video quality.