Low-Latency Neural Stereo Streaming
Qiqi Hou Farzad Farhadzadeh Amir Said Guillaume Sautiere Hoang Le
The rise of new video modalities like virtual reality or autonomous driving
has increased the demand for efficient multi-view video compression methods,
both in terms of rate-distortion (R-D) performance and in terms of delay and
runtime. While most recent stereo video compression approaches have shown
promising performance, they compress left and right views sequentially, leading
to poor parallelization and runtime performance. This work presents Low-Latency
neural codec for Stereo video Streaming (LLSS), a novel parallel stereo video
coding method designed for fast and efficient low-latency stereo video
streaming. Instead of using a sequential cross-view motion compensation like
existing methods, LLSS introduces a bidirectional feature shifting module to
directly exploit mutual information among views and encode them effectively
with a joint cross-view prior model for entropy coding. Thanks to this design,
LLSS processes left and right views in parallel, minimizing latency; all while
substantially improving R-D performance compared to both existing neural and
conventional codecs.