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多层特征融合与语义增强的盲图像质量评价

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针对现有盲图像质量评价算法在面对真实失真图像时性能较差的问题,本文提出多层特征融合和语义信息增强相结合的无参考图像质量评价算法.提取图像的局部和全局失真特征,利用特征融合模块对特征进行多层融合;利用多层扩张卷积增强语义信息,进而指导失真图像到质量分数的映射过程;考虑预测分数和主观分数之间的相对排名关系,对L1 损失函数和三元组排名损失函数进行融合,构建新的损失函数Lmix.为了验证本文方法的有效性,在野生图像质量挑战数据集上进行了验证和对比实验,该算法的斯皮尔曼等级相关系数与皮尔逊线性相关系数指标相比原算法分别提升 2.3%和 2.3%;在康斯坦茨真实图像质量数据数据集和野生图像质量挑战数据集上进行了跨数据集实验,该算法在面对真实失真图像时表现出了良好的泛化性能.
Blind image quality assessment based on multi-level feature fusion and semantic enhancement
Aiming at the low performance of the existing blind image quality assessment algorithm when facing the real distorted images,the paper proposes a new no-reference image quality assessment algorithm,namely multi-level feature fusion and semantic enhancement for NR(MFFSE-NR),which combines multi-level feature fusion and semantic in-formation enhancement.The local and global distortion features of an image are extracted,then a feature fusion module is used to fuse the features in layers.The multi-layer dilated convolution is employed to enhance semantic information and further direct the mapping process from distorted image to quality fraction.Finally,a novel loss function called Lmix is created by combining the triplet ranking loss function and the L1 loss function,taking account of the relative ranking relationship between the predicted score and the subjective score.Validation and comparison experiments carried out on LIVEC dataset show that both the SROCC and PLCC index are improved respectively by 2.3%than the original al-gorithm;cross-dataset validation on the KonIQ-10k dataset and LIVEC dataset confirm that the proposed algorithm has good generalization ability when dealing with the real distorted images.

deep learningimage qualityconvolution neural networkfeature extractionchannel attention structuremulti-level feature fusiondilated convolutiontriplet loss function

赵文清、许丽娇、陈昊阳、李梦伟

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华北电力大学 控制与计算机工程学院, 河北 保定 071003

复杂能源系统智能计算教育部工程研究中心,河北 保定 071003

深度学习 图像质量 卷积神经网络 特征提取 通道注意力结构 多层次特征融合 扩张卷积 三元组损失函数

国家自然科学基金国家自然科学基金河北省自然科学基金中央高校基本科研业务费专项中央高校基本科研业务费专项

6177316061871182F20215020132020MS1532021PT018

2024

10.11992/tis.202301007

智能系统学报
中国人工智能学会　哈尔滨工程大学

智能系统学报

CSTPCD北大核心
影响因子：0.672
ISSN：1673-4785
年,卷(期)：2024.19(1)
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