Lossy compression brings artifacts into the compressed image and degrades the
visual quality. In recent years, many compression artifacts removal methods
based on convolutional neural network (CNN) have been developed with great
success. However, these methods usually train a model based on one specific
value or a small range of quality factors. Obviously, if the test image's
quality factor does not match to the assumed value range, then degraded
performance will be resulted. With this motivation and further consideration of
practical usage, a highly robust compression artifacts removal network is
proposed in this paper. Our proposed network is a single model approach that
can be trained for handling a wide range of quality factors while consistently
delivering superior or comparable image artifacts removal performance. To
demonstrate, we focus on the JPEG compression with quality factors, ranging
from 1 to 60. Note that a turnkey success of our proposed network lies in the
novel utilization of the quantization tables as part of the training data.
Furthermore, it has two branches in parallel---i.e., the restoration branch and
the global branch. The former effectively removes the local artifacts, such as
ringing artifacts removal. On the other hand, the latter extracts the global
features of the entire image that provides highly instrumental image quality
improvement, especially effective on dealing with the global artifacts, such as
blocking, color shifting. Extensive experimental results performed on color and
grayscale images have clearly demonstrated the effectiveness and efficacy of
our proposed single-model approach on the removal of compression artifacts from
the decoded image.

本研究提出了一种高度稳健的压缩伪像去除网络，它是单模型方法，能够训练处理广泛范围的质量因素，并始终提供优秀或相当的图像伪像去除性能。该网络非常有效地利用了量化表作为训练数据的一部分，并具有两个并行分支，一个用于本地伪像去除，另一个用于提取整个图像的全局特征，特别适用于全局伪像去除。大量实验证据表明，本提案的单模型方法非常有效地用于从解码图像中去除压缩伪像。

学习单个模型，处理多种 JPEG 图像失真质量问题

Learning a Single Model with a Wide Range of Quality Factors for JPEG  Image Artifacts Removal

Handling digital images is almost always accompanied by a lossy compression
in order to facilitate efficient transmission and storage. This introduces an
unavoidable tension between the allocated bit-budget (rate) and the
faithfulness of the resulting image to the original one (distortion). An
additional complicating consideration is the effect of the compression on
recognition performance by given classifiers (accuracy). This work aims to
explore this rate-distortion-accuracy tradeoff. As a case study, we focus on
the design of the quantization tables in the JPEG compression standard. We
offer a novel optimal tuning of these tables via continuous optimization,
leveraging a differential implementation of both the JPEG encoder-decoder and
an entropy estimator. This enables us to offer a unified framework that
considers the interplay between rate, distortion and classification accuracy.
In all these fronts, we report a substantial boost in performance by a simple
and easily implemented modification of these tables.

本研究探讨了数字图像处理中的压缩方法对图像失真度和分类准确率的影响，特别是在 JPEG 压缩标准的量化表设计方面进行新的优化，取得了显著的性能提升。

速率失真准确性权衡：JPEG 案例研究

The Rate-Distortion-Accuracy Tradeoff: JPEG Case Study

Several dual-domain convolutional neural network-based methods show
outstanding performance in reducing image compression artifacts. However, they
suffer from handling color images because the compression processes for
gray-scale and color images are completely different. Moreover, these methods
train a specific model for each compression quality and require multiple models
to achieve different compression qualities. To address these problems, we
proposed an implicit dual-domain convolutional network (IDCN) with the pixel
position labeling map and the quantization tables as inputs. Specifically, we
proposed an extractor-corrector framework-based dual-domain correction unit
(DCU) as the basic component to formulate the IDCN. A dense block was
introduced to improve the performance of extractor in DRU. The implicit
dual-domain translation allows the IDCN to handle color images with the
discrete cosine transform (DCT)-domain priors. A flexible version of IDCN
(IDCN-f) was developed to handle a wide range of compression qualities.
Experiments for both objective and subjective evaluations on benchmark datasets
show that IDCN is superior to the state-of-the-art methods and IDCN-f exhibits
excellent abilities to handle a wide range of compression qualities with little
performance sacrifice and demonstrates great potential for practical
applications.

本文提出了隐式双域卷积网络 (IDCN)，该网络利用了像素位置标签映射和量化表作为输入，采用双域校正单位 (DCU) 作为基本组件，在 DRU 中引入了密集块以提高性能，并通过隐式双域转换能够处理彩色图像。DCN 相对于现有方法表现更为优越，IDCN-f 能够在性能损失较小的情况下处理更广泛的压缩质量范围，并表现出很大的实用潜力。