In this paper we propose a new image randomness measure using Shannon entropy over local image blocks. The proposed local Shannon entropy measure overcomes several weaknesses of the conventional global Shannon entropy measure, including unfair randomness comparisons between images of different sizes, failure to discern image randomness before and after image shuffling, and possible inaccurate scores for synthesized images. Statistical tests pertinent to this new measure are also derived. This new measure is therefore both quantitative and qualitative. The parameters in the local Shannon entropy measure are further optimized for a better capture of local image randomness. The estimated statistics and observed distribution from 50,000 experiments match the theoretical ones. Finally, two examples are given, applying the proposed measure to image randomness among shuffled images and encrypted images. Both examples show that the proposed method is more effective and more accurate than the global Shannon entropy measure

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Statistical Methods for Machine Learning Discover How to Transform Data into Knowledge with Python by Jason Brownlee 统计学是机器学习的支柱。没有机器学习，你就无法对机器学习产生深刻的理解和应用。 切开方程、希腊字母和混淆，并发现统计中需要了解的主题。 使用清晰的解释、标准的 Python 库和分步教程课程，您将发现统计方法对机器学习、汇总统计、假设测试、非参数统计、重新采样方法的重要性等等。 by Jason Brownlee

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