  Video streams are delivered continuously to save the cost of storage and
device memory. Real-time denoising algorithms are typically adopted on the user
device to remove the noise involved during the shooting and transmission of
video streams. However, sliding-window-based methods feed multiple input frames
for a single output and lack computation efficiency. Recent multi-output
inference works propagate the bidirectional temporal feature with a parallel or
recurrent framework, which either suffers from performance drops on the
temporal edges of clips or can not achieve online inference. In this paper, we
propose a Bidirectional Streaming Video Denoising (BSVD) framework, to achieve
high-fidelity real-time denoising for streaming videos with both past and
future temporal receptive fields. The bidirectional temporal fusion for online
inference is considered not applicable in the MoViNet. However, we introduce a
novel Bidirectional Buffer Block as the core module of our BSVD, which makes it
possible during our pipeline-style inference. In addition, our method is
concise and flexible to be utilized in both non-blind and blind video
denoising. We compare our model with various state-of-the-art video denoising
models qualitatively and quantitatively on synthetic and real noise. Our method
outperforms previous methods in terms of restoration fidelity and runtime. Our
source code is publicly available at https://github.com/ChenyangQiQi/BSVD
