As a widely adopted technique in data transmission, video compression
effectively reduces the size of files, making it possible for real-time cloud
computing. Tuttavia, it comes at the cost of visual quality, posing challenges
to the robustness of downstream vision models. In this work, we present a
versatile codec-aware enhancement framework that reuses codec information to
adaptively enhance videos under different compression settings, assisting
various downstream vision tasks without introducing computation bottleneck.
Specifically, the proposed codec-aware framework consists of a
compression-aware adaptation (CAA) network that employs a hierarchical
adaptation mechanism to estimate parameters of the frame-wise enhancement
network, namely the bitstream-aware enhancement (BAE) network. The BAE network
further leverages temporal and spatial priors embedded in the bitstream to
effectively improve the quality of compressed input frames. Extensive
experimental results demonstrate the superior quality enhancement performance
of our framework over existing enhancement methods, as well as its versatility
in assisting multiple downstream tasks on compressed videos as a plug-and-play
module. Code and models are available at
https://huimin-zeng.github.io/PnP-VCVE/.

Questo articolo esplora i giri e le loro implicazioni.

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