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 images 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 article. 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.

中文翻译：

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学习具有广泛质量因子的单一模型以去除 JPEG 图像伪影。


有损压缩会给压缩图像带来伪影并降低视觉质量。近年来，出现了许多基于压缩伪影的去除方法卷积神经网络（CNN）的开发取得了巨大成功。然而，这些方法通常训练基于一具体值或小范围的质量因素。显然，如果测试图像质量因子与假设的值范围不匹配，则会导致性能下降。出于这种动机并进一步考虑实际使用，本文提出了一种高度鲁棒的压缩伪影去除网络。我们提出的网络是单一型号可以训练该方法来处理各种质量因素，同时始终提供卓越或可比的图像伪影去除性能。为了进行演示，我们重点关注质量因子范围从 1 到 60 的 JPEG 压缩。请注意，我们提出的网络的交钥匙成功在于将量化表作为训练数据的一部分的新颖利用。此外，它有两个并行的分支，即恢复分支和全球分公司。前者有效地消除了当地的伪影，例如去除振铃伪影。另一方面，后者提取整个图像的全局特征，这提供了非常有用的图像质量改进，特别是在处理全球的伪影，例如阻塞、色移。 对彩色和灰度图像进行的大量实验结果清楚地证明了我们提出的方法的有效性和功效单身的-从解码图像中去除压缩伪影的模型方法。