Abstract:

In the Image Quality Assessment (IQA), no-reference quality assessment methods have demonstrated significant application value and development potential for managing distorted images in real-world scenarios. However, real-world distorted images exhibit high diversity and complexity, which make designing relevant evaluation algorithms more difficult. In recent years, deep learning technology has achieved remarkable success in various subfields of image processing, such as image classification, object detection, and image segmentation. These advancements have motivated researchers to introduce Deep Neural Network (DNN) technology into IQA. Owing to their outstanding feature extraction and learning capabilities, DNNs have provided innovative solutions and made significant progress in the quality assessment of distorted images in real-world environments. Despite these advancements, existing methods still have certain limitations in describing the image quality in real-world scenes, particularly when handling diverse image content. Additionally, many DNN-based IQA methods require the input images to be scaled or cropped to a fixed resolution, which often compromises the original structure and content of the images, thereby affecting the accuracy and generalizability of the quality assessment. To address these issues, this paper proposes an adaptive No-Reference IQA (NR-IQA) method based on Multi-Scale Pyramid Pooling (MSPP-IQA). This method does not require preprocessing and can assess the quality of an image in its original size. Furthermore, by introducing content understanding and attention modules, MSPP-IQA can mimic the working principles of the Human Visual System (HVS), simultaneously perceiving global high-level and local low-level features. Experimental results demonstrate that, compared to current mainstream methods, MSPP-IQA performs well on both real-world and synthetic distortion datasets. These results validate the effectiveness and superiority of MSPP-IQA in addressing the challenges in assessing the quality of real-world distorted images.

Key words: No-Reference Image Quality Assessment (NR-IQA), real distortion, multi-scale feature fusion, Spatial Pyramid Pooling (SPP), attention mechanism

摘要：

在图像质量评价(IQA)领域, 无参考质量评价方法在处理真实场景下的失真图像时展现了巨大的应用价值和未来发展潜力, 然而真实环境中的失真图像具有高度的多样性和复杂性, 增加了相关评价算法设计的难度。近年来, 深度学习技术在图像分类、目标检测以及图像分割等细分领域均取得了令人瞩目的成果。这些进展推动科研人员将深度神经网络(DNN)技术引入IQA中。DNN凭借其出色的特征提取和学习能力, 为真实环境中的失真IQA带来了创新性的解决方案和显著的进步。但是, 现有方法在处理真实场景图像质量描述时仍存在一定的局限性, 特别是在应对图像内容多样性方面。此外, 许多基于DNN的IQA方法需要对输入图像进行缩放或裁剪以固定分辨率, 这往往会破坏图像的原始结构和内容, 从而影响质量评估的准确性和泛化能力。为了解决这些问题, 提出一种基于多尺度金字塔池化的自适应无参考图像质量评价方法(MSPP-IQA)。MSPP-IQA允许直接使用原始尺寸的图像进行质量评估, 无需任何图像预处理, 通过引入图像内容理解模块和注意力模块, 模仿人类视觉系统(HVS)的工作原理, 同时感知全局高级特征和局部低级特征。实验结果表明, 相较于当前主流方法, MSPP-IQA在真实失真和合成失真数据集上均表现出良好的性能。这一实验结果充分证明了MSPP-IQA在应对真实失真IQA挑战方面的有效性和优越性。

关键词: 无参考图像质量评价, 真实失真, 多尺度特征融合, 空间金字塔池化, 注意力机制