面向图像集的置信区间内一致性增强质量评价准则

在对整个图像集进行增强质量评价时，现有的平均准则会随着不同图像集非一致性地变化，从而导致较大的评价质量波动。为此，该文提出一个面向图像集的置信区间内一致性增强质量评价准则，通过设置应用参数并使用置信区间筛选数据，再比较各图像增强前后的质量分数差值，由此评估图像质量增强的一致性，最终计算出一致性增强质量分数有效值。在众多图像增强算法中，所提准则能够挑选出具体应用所需要的稳定性强、可靠性高的增强算法。实验结果表明，所提准则具有良好的主客观评价一致性，性能优于当前的平均准则，为各种图像增强算法提供了一个可用于任意图像集的质量评价准则。

Consistency Enhancement Quality Assessment Criterion in Confidence Interval for Image Set

When evaluating the enhancement quality of a whole image set, the existing average score criterion varies inconsistently with different image sets and produces a large evaluation quality fluctuation. Therefore, this paper proposes a consistency enhancement quality assessment criterion in confidence interval for any image set. By setting application parameters and using confidence interval to screen data, the proposed criterion compares the quality score difference before and after enhancing each image, and evaluates the consistency of image quality enhancement, and then calculates the effective value of consistency enhancement quality scores. Among many image enhancement algorithms, the proposed criterion can select the high-reliability enhancement algorithm for a specific application. The experimental results show that the proposed criterion has good subjective and objective consistency and outperforms the existing average score criterion, which provides an evaluation criterion for those image enhancement algorithms applied to any image set.