Efficient and Effective Blind JPEG Image Improvement With Sequential Feature Processing

Image compression technologies such as JPEG enable efficient use of digital images. However, these technologies also degrade the quality of the image, resulting in a poor visual appearance and reduced usability. One way to solve the quality problem and achieve high efficiency in using high-quality JPEG images is to use quality improvement technologies based on artifact removal. In recent years, various methods using machine-learning-optimized neural networks have been devised to achieve high performance whereas maintaining versatility for JPEG images with various qualities. On the other hand, methods that achieve high-quality images need enormous computing costs, which is a barrier to easy use, especially when using images with large size. Given this situation, this paper proposes a method that achieves higher performance with smaller computational resources while maintaining the image quality in broader situations. The proposed method consists of a Quality Estimation Part (QE Part) that estimates the quality of the input image and Image Processing Parts (IP Parts) that process the image based on the estimated quality representation. Sequential processing is carried out by connecting multiple IP Parts in a cascade, which enables efficient and effective processing of different features. Each IP Part consists of multiple Processing Blocks, which enable effective quality improvement while maintaining efficiency. These measures enable the proposed method to achieve state-of-the-art quantitative results and better qualitative results that outperform conventional methods for images with various qualities. The code and the pre-trained models are released at https://github.com/ezumi-keio/Sequential_Processing-main.