  Low latency rates are crucial for online video-based applications, such as
video conferencing and cloud gaming, which make improving video quality in
online scenarios increasingly important. However, existing quality enhancement
methods are limited by slow inference speed and the requirement for temporal
information contained in future frames, making it challenging to deploy them
directly in online tasks. In this paper, we propose a novel method, STLVQE,
specifically designed to address the rarely studied online video quality
enhancement (Online-VQE) problem. Our STLVQE designs a new VQE framework which
contains a Module-Agnostic Feature Extractor that greatly reduces the redundant
computations and redesign the propagation, alignment, and enhancement module of
the network. A Spatial-Temporal Look-up Tables (STL) is proposed, which
extracts spatial-temporal information in videos while saving substantial
inference time. To the best of our knowledge, we are the first to exploit the
LUT structure to extract temporal information in video tasks. Extensive
experiments on the MFQE 2.0 dataset demonstrate that our STLVQE achieves a
satisfactory performance-speed trade-off.
