  Lossy image compression is pervasively conducted to save communication
bandwidth, resulting in undesirable compression artifacts. Recently, extensive
approaches have been proposed to reduce image compression artifacts at the
decoder side; however, they require a series of architecture-identical models
to process images with different quality, which are inefficient and
resource-consuming. Besides, it is common in practice that compressed images
are with unknown quality and it is intractable for existing approaches to
select a suitable model for blind quality enhancement. In this paper, we
propose a resource-efficient blind quality enhancement (RBQE) approach for
compressed images. Specifically, our approach blindly and progressively
enhances the quality of compressed images through a dynamic deep neural network
(DNN), in which an early-exit strategy is embedded. Then, our approach can
automatically decide to terminate or continue enhancement according to the
assessed quality of enhanced images. Consequently, slight artifacts can be
removed in a simpler and faster process, while the severe artifacts can be
further removed in a more elaborate process. Extensive experiments demonstrate
that our RBQE approach achieves state-of-the-art performance in terms of both
blind quality enhancement and resource efficiency. The code is available at
https://github.com/RyanXingQL/RBQE.
