Fidelity or Quality? A Region-Aware Framework for Enhanced Image Decoding via Hybrid Neural Networks

The generative deep learning models such as the generative adversarial networks (GAN) have been shown to efficiently generate visually appealing images by learning the natural scene statistics. However, the signal fidelity, instead of the visual quality, has been largely ignored in the generation process, especially for the highly structural regions. In this paper, we introduce a region-aware visual signal restoration scheme to achieve a good balance between visual quality and fidelity. As a specific example of this framework, we develop an enhanced decoding scheme with hybrid neural networks, such that the base fidelity layer and texture quality enhancement layer are combined adaptively to restore the compressed images. The efficiency of the proposed framework is demonstrated with extensive experimental results, which show favorable performance against the state-of-the-art methods.