Early Exit or Not: Resource-Efficient Blind Quality Enhancement for Compressed Images

Abstract

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.

arXiv commentAccepted by ECCV 2020. v5 updates: enlarge character size; correct titlerunning; add publishment reference; add open-sourced url

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Cited by in corpus (4)

• Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
• Adaptive Inference through Early-Exit Networks: Design, Challenges and Directions
• NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results
• Spatial Attention-based Non-reference Perceptual Quality Prediction Network for Omnidirectional Images