Abstract
Recent advancements in image quality assessment (IQA), driven by sophisticated deep neural network designs, have significantly improved the ability to approach human perceptions. However, most existing methods are obsessed with fitting the overall score, neglecting the fact that humans typically evaluate image quality from different dimensions before arriving at an overall quality assessment. To overcome this problem, we propose a multi-dimensional image quality assessment (MDIQA) framework. Specifically, we model image quality across various perceptual dimensions, including five technical and four aesthetic dimensions, to capture the multifaceted nature of human visual perception within distinct branches. Each branch of our MDIQA is initially trained under the guidance of a separate dimension, and the respective features are then amalgamated to generate the final IQA score. Additionally, when the MDIQA model is ready, we can deploy it for a flexible training of image restoration (IR) models, enabling the restoration results to better align with varying user preferences through the adjustment of perceptual dimension weights. Extensive experiments demonstrate that our MDIQA achieves superior performance and can be effectively and flexibly applied to image restoration tasks. The code is available: this https URL.
Abstract (translated)
最近，基于复杂深度神经网络设计的图像质量评估（IQA）领域的进展显著提高了接近人类感知的能力。然而，大多数现有的方法过于专注于拟合整体评分，忽视了这样一个事实：人类通常会从不同的维度来评价图片的质量，并在此基础上得出最终的整体质量评估结果。为了解决这个问题，我们提出了一种多维度图像质量评估（MDIQA）框架。 具体而言，我们在多个感知维度上建模图像质量，包括五个技术维度和四个美学维度，以捕捉人类视觉感知的多方面特性。我们的MDIQA框架中的每个分支最初在不同的维度指导下进行训练，并且随后将各自的特征整合起来生成最终的IQA分数。此外，在MDIQA模型准备就绪后，我们可以部署它来进行图像修复（IR）模型的灵活训练，通过调整感知维度权重使修复结果更好地符合各种用户偏好。 广泛的实验表明，我们的MDIQA在性能上表现出色，并且可以有效地和灵活地应用于图像修复任务中。相关代码可在此处获取：[链接] (请将 [链接] 替换为实际的URL)。
URL
https://arxiv.org/abs/2508.16887
