Kavli Affiliate: Feng Wang
| First 5 Authors: Feng Wang, Haihang Ruan, Zhihuang Xie, Ronggang Wang, Xiangyu Yue
| Summary:
Recently, Neural Video Compression (NVC) techniques have achieved remarkable
performance, even surpassing the best traditional lossy video codec. However,
most existing NVC methods heavily rely on transmitting Motion Vector (MV) to
generate accurate contextual features, which has the following drawbacks. (1)
Compressing and transmitting MV requires specialized MV encoder and decoder,
which makes modules redundant. (2) Due to the existence of MV Encoder-Decoder,
the training strategy is complex. In this paper, we present a noval Single
Stream NVC framework (SSNVC), which removes complex MV Encoder-Decoder
structure and uses a one-stage training strategy. SSNVC implicitly use temporal
information by adding previous entropy model feature to current entropy model
and using previous two frame to generate predicted motion information at the
decoder side. Besides, we enhance the frame generator to generate higher
quality reconstructed frame. Experiments demonstrate that SSNVC can achieve
state-of-the-art performance on multiple benchmarks, and can greatly simplify
compression process as well as training process.
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