Towards A Theory of Jpeg Block Convergence
Towards A Theory of Jpeg Block Convergence
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DOI：
10.1109/icip.2018.8451234
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发表时间：
2018-10
期刊：
影响因子：
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通讯作者：
Cecilia Pasquini;Rainer Böhme
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文献类型：
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作者：
Cecilia Pasquini;Rainer Böhme
JPEG块的收敛统计已被证明是一个有用的工具，用于法医分析高质量的压缩图像。由于目前的方法是基于经验观察，我们提出了一个理论分析来解释灰度图像和最大质量JPEG压缩（即质量因子等于100）的情况。导出了稳定块比在不同压缩阶段的近似分布，表明稳定块比最终取决于DCT域中量化噪声的方差。我们通过使用JPEG错误统计数据的结果，将这些结果应用于区分从未压缩过的图像和压缩过一次且质量最高的图像。在不同大小和内容的图像块上进行的实验验证了理论结果，通过不需要校准的最大似然分类规则可以获得较高的精度。
The convergence statistics of JPEG blocks has been shown to be a useful tool to forensically analyze high quality compressed images. Since current approaches are based on empirical observations, we propose a theoretical analysis explaining the case of grayscale images and maximum quality JPEG compression (i.e., quality factor equal to 100). The approximate distribution of the stable block ratio at different compression stages is derived, showing that it ultimately depends on the variance of the quantization noise in the DCT domain. We apply such results to discriminate never compressed images and images compressed once with maximum quality, by resorting to results on JPEG error statistics. Tests on image patches with different size and content validate the theoretical results, which allow for obtaining high accuracy through a calibration-free maximum likelihood classification rule.