  With the benefit of deep learning techniques, recent researches have made
significant progress in image compression artifacts reduction. Despite their
improved performances, prevailing methods only focus on learning a mapping from
the compressed image to the original one but ignore the intrinsic attributes of
the given compressed images, which greatly harms the performance of downstream
parsing tasks. Different from these methods, we propose to decouple the
intrinsic attributes into two complementary features for artifacts
reduction,ie, the compression-insensitive features to regularize the high-level
semantic representations during training and the compression-sensitive features
to be aware of the compression degree. To achieve this, we first employ
adversarial training to regularize the compressed and original encoded features
for retaining high-level semantics, and we then develop the compression
quality-aware feature encoder for compression-sensitive features. Based on
these dual complementary features, we propose a Dual Awareness Guidance Network
(DAGN) to utilize these awareness features as transformation guidance during
the decoding phase. In our proposed DAGN, we develop a cross-feature fusion
module to maintain the consistency of compression-insensitive features by
fusing compression-insensitive features into the artifacts reduction baseline.
Our method achieves an average 2.06 dB PSNR gains on BSD500, outperforming
state-of-the-art methods, and only requires 29.7 ms to process one image on
BSD500. Besides, the experimental results on LIVE1 and LIU4K also demonstrate
the efficiency, effectiveness, and superiority of the proposed method in terms
of quantitative metrics, visual quality, and downstream machine vision tasks.
