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

• URL: http://arxiv.org/abs/2006.16581v5
• Date: Mon, 12 Oct 2020 08:23:58 GMT
• Title: Early Exit or Not: Resource-Efficient Blind Quality Enhancement for Compressed Images
• Authors: Qunliang Xing, Mai Xu, Tianyi Li, Zhenyu Guan
• Abstract summary: Lossy image compression is pervasively conducted to save communication bandwidth, resulting in undesirable compression artifacts. We propose a resource-efficient blind quality enhancement (RBQE) approach for compressed images. Our approach can automatically decide to terminate or continue enhancement according to the assessed quality of enhanced images.
• Score: 54.40852143927333
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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