Available model-based techniques for the estimation of the primary quantization matrix in double-compressed JPEG images work only under specific conditions regarding the relationship between the first and second compression quality factors, and the alignment of the first and second JPEG compression grids. In this paper, we propose a single CNN-based estimation technique that can work under a wide range of settings. We do so, by adapting a dense CNN network to the problem at hand. Particular attention is paid to the choice of the loss function. Experimental results highlight several advantages of the new method, including: i) capability of working under very general conditions, ii) improved performance in terms of MSE and Accuracy, especially in the non-aligned case, iii) better spatial resolution due to the ability of providing good results also on small image patches.

中文翻译：

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通过 CNN 对双重压缩的 JPEG 图像进行初级量化矩阵估计

用于估计双重压缩 JPEG 图像中的主要量化矩阵的可用基于模型的技术仅在关于第一和第二压缩质量因子之间的关系以及第一和第二 JPEG 压缩网格的对齐的特定条件下工作。在本文中，我们提出了一种基于 CNN 的估计技术，可以在各种设置下工作。我们通过使密集的 CNN 网络适应手头的问题来做到这一点。特别注意损失函数的选择。实验结果突出了新方法的几个优点，包括：i) 在非常一般的条件下工作的能力，ii) 在 MSE 和精度方面的性能提高，特别是在非对齐的情况下，