The Lottery Ticket Adaptation for Neural Video Coding

Recently, learning based video compression methods have attracted increasing attention. However, most learning based video codecs are not adaptive to different video contents. Though adaptation at inference time is a solution to tackle this issue, adapting all the codec’s parameters is computationally expensive and brings heavy bitrate overhead. The recently proposed Lottery Ticket Hypothesis (LTH) states that an over-parameterized neural network contains smaller subnetworks (winning tickets) that can match the performance of the original network. In this paper, we present a novel lottery-ticket adaptation technique on decoder-side multiplicative parameters of a neural network, transferring the concept of winning lottery tickets to video compression tasks. At inference time, the winning multiplicative parameters are overfitted, compressed, and signaled together with encoded frames for decoding. We show that our approach outperforms the Versatile Video Coding (VVC) standard in the Multiscale Structural Similarity (MS-SSIM) at a low bitrate on both the UVG and JVET sequences. To the best of our knowledge, this is the first attempt to apply LTH in the video compression domain. Also, this is the first published end-to-end learned video codec working directly on YUV format, which outperforms VVC on UVG and JVET datasets in MS-SSIM.