  JPEG is a widely used compression scheme to efficiently reduce the volume of
transmitted images. The artifacts appear among blocks due to the information
loss, which not only affects the quality of images but also harms the
subsequent high-level tasks in terms of feature drifting. High-level vision
models trained on high-quality images will suffer performance degradation when
dealing with compressed images, especially on mobile devices. Numerous
learning-based JPEG artifact removal methods have been proposed to handle
visual artifacts. However, it is not an ideal choice to use these JPEG artifact
removal methods as a pre-processing for compressed image classification for the
following reasons: 1. These methods are designed for human vision rather than
high-level vision models; 2. These methods are not efficient enough to serve as
pre-processing on resource-constrained devices. To address these issues, this
paper proposes a novel lightweight AFD module to boost the performance of
pre-trained image classification models when facing compressed images. First, a
FDE-Net is devised to generate the spatial-wise FDM in the DCT domain. Next,
the estimated FDM is transmitted to the FE-Net to generate the mapping
relationship between degraded features and corresponding high-quality features.
A simple but effective RepConv block equipped with structural
re-parameterization is utilized in FE-Net, which enriches feature
representation in the training phase while maintaining efficiency in the
deployment phase. After training on limited compressed images, the AFD-Module
can serve as a "plug-and-play" model for pre-trained classification models to
improve their performance on compressed images. Experiments demonstrate that
our proposed AFD module can comprehensively improve the accuracy of the
pre-trained classification models and significantly outperform the existing
methods.
