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使用HVS双通道和对比灵敏度来盲目评估图像质量

Fan Chen¹, Hong Fu², Hengyong Yu³

¹Department of Artificial Intelligence, Shenzhen University, Shenzhen 518060, China.

Sensors (Basel, Switzerland)

|July 11, 2023

概括

这项研究引入了一种新的双通道卷积神经网络,用于盲人图像质量评估 (BIQA). 该方法受到人类视觉系统 (HVS) 的启发,提高了图像质量预测的准确性.

科学领域:

计算机视觉计算机视觉
人工智能的人工智能
图像处理图像处理

背景情况:

盲人图像质量评估 (BIQA) 试图通过模仿人类感知来预测图像质量.
深度学习和人类视觉系统 (HVS) 特性为BIQA提供了协同作用的潜力.
现有的BIQA方法可以通过结合HVS路径功能来改进.

研究的目的:

为BIQA任务提出一种新的双通道卷积神经网络.
为了提高图像质量评估,利用HVS (腹部和背部通道) 的独特处理流.
通过整合内容和全球形状特征提取,提高BIQA模型的准确性和稳定性.

主要方法:

由HVS腹部 ("什么") 和背部 ("哪里") 路径启发的双路径卷积神经网络被开发出来.
"什么"路径提取内容特征,而"何处"路径使用对比度敏感度加权梯度图像提取全球形状特征.
一个双通道的多尺度特征融合模块集成了来自两个通道的特征,以捕获全球和本地图像细节.

主要成果:

拟议的双通道网络在六个基准图像质量评估数据库上实现了最先进的性能.
集成HVS启发的路径和多尺度特征融合显著改善了BIQA的准确性.
在"何处"路径中对比度敏感度加权增强了对感知相关的整体形状特征的提取.

结论:

拟议的双通道CNN有效地模仿人类视觉处理,用于优越的盲人图像质量评估.
这种方法为开发更准确和更强大的BIQA系统提供了有希望的方向.
该方法展示了在图像质量评估中将深度学习与HVS原则相结合的好处.

关键词:

对比度的敏感度对比度的敏感度背部通道背部通道双流网络是双流网络.没有参考的图像质量评估.腹腔通道的通道.

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