  Video Snapshot Compressive Imaging (SCI) aims to use a low-speed 2D camera to
capture high-speed scene as snapshot compressed measurements, followed by a
reconstruction algorithm to reconstruct the high-speed video frames.
State-of-the-art (SOTA) deep learning-based algorithms have achieved impressive
performance, yet with heavy computational workload. Network quantization is a
promising way to reduce computational cost. However, a direct low-bit
quantization will bring large performance drop. To address this challenge, in
this paper, we propose a simple low-bit quantization framework (dubbed Q-SCI)
for the end-to-end deep learning-based video SCI reconstruction methods which
usually consist of a feature extraction, feature enhancement, and video
reconstruction module. Specifically, we first design a high-quality feature
extraction module and a precise video reconstruction module to extract and
propagate high-quality features in the low-bit quantized model. In addition, to
alleviate the information distortion of the Transformer branch in the quantized
feature enhancement module, we introduce a shift operation on the query and key
distributions to further bridge the performance gap. Comprehensive experimental
results manifest that our Q-SCI framework can achieve superior performance,
e.g., 4-bit quantized EfficientSCI-S derived by our Q-SCI framework can
theoretically accelerate the real-valued EfficientSCI-S by 7.8X with only 2.3%
performance gap on the simulation testing datasets. Code is available at
https://github.com/mcao92/QuantizedSCI.
