Wang Zhiyu, Zhuang Jiayan, Ye Sichao, Xu Ningyuan, Xiao Jiangjian, Peng Chengbin
Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China.
Ningbo Institute of Industrial Technology, Chinese Academy of Sciences, Ningbo 315201, China.
Entropy (Basel). 2023 Jan 10;25(1):144. doi: 10.3390/e25010144.
With the development of image recovery models, especially those based on adversarial and perceptual losses, the detailed texture portions of images are being recovered more naturally. However, these restored images are similar but not identical in detail texture to their reference images. With traditional image quality assessment methods, results with better subjective perceived quality often score lower in objective scoring. Assessment methods suffer from subjective and objective inconsistencies. This paper proposes a regional differential information entropy (RDIE) method for image quality assessment to address this problem. This approach allows better assessment of similar but not identical textural details and achieves good agreement with perceived quality. Neural networks are used to reshape the process of calculating information entropy, improving the speed and efficiency of the operation. Experiments conducted with this study's image quality assessment dataset and the PIPAL dataset show that the proposed RDIE method yields a high degree of agreement with people's average opinion scores compared with other image quality assessment metrics, proving that RDIE can better quantify the perceived quality of images.
随着图像恢复模型的发展，特别是基于对抗性损失和感知损失的模型，图像的细节纹理部分得到了更自然的恢复。然而，这些恢复后的图像在细节纹理上与其参考图像相似但并不完全相同。使用传统的图像质量评估方法时，主观感知质量较好的结果在客观评分中往往得分较低。评估方法存在主观和客观不一致的问题。本文提出了一种用于图像质量评估的区域差分信息熵（RDIE）方法来解决这一问题。这种方法能够更好地评估相似但不完全相同的纹理细节，并与感知质量达成良好的一致性。利用神经网络对计算信息熵的过程进行重塑，提高了运算速度和效率。使用本研究的图像质量评估数据集和PIPAL数据集进行的实验表明，与其他图像质量评估指标相比，所提出的RDIE方法与人们的平均意见得分具有高度一致性，证明RDIE能够更好地量化图像的感知质量。
