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.

翻译：JPEG是一种广泛使用的压缩方案，可有效减少传输图像的数据量。由于信息损失，块间会出现伪影，这不仅影响图像质量，还会导致特征漂移，进而损害后续高级视觉任务。基于高质量图像训练的高级视觉模型在处理压缩图像时会出现性能下降，尤其在移动设备上。近年来，大量基于学习的JPEG伪影去除方法被提出以处理视觉伪影。然而，将这些JPEG伪影去除方法作为压缩图像分类的预处理并非理想选择，原因如下：1. 这些方法专为人类视觉设计，而非面向高级视觉模型；2. 这些方法在资源受限设备上作为预处理时效率不足。针对这些问题，本文提出一种新型轻量级自适应特征漂移（AFD）模块，可在处理压缩图像时提升预训练图像分类模型的性能。首先，设计FDE-Net在DCT域生成空间特征漂移图（FDM）；接着，将估计的FDM传递至FE-Net，以建立退化特征与对应高质量特征之间的映射关系。FE-Net采用配备结构重参数化的简单而有效的RepConv模块，在训练阶段丰富特征表示，同时保持部署阶段的高效性。在有限压缩图像上训练后，AFD模块可作为“即插即用”模型嵌入预训练分类模型，显著提升其对压缩图像的分类性能。实验表明，本文提出的AFD模块能全面改善预训练分类模型的分类准确率，性能显著优于现有方法。