Abstract
Over the past few years, deep neural models have made considerable advances in image quality assessment (IQA). However, the underlying reasons for their success remain unclear, owing to the complex nature of deep neural networks. IQA aims to describe how the human visual system (HVS) works and to create its efficient approximations. On the other hand, Saliency Prediction task aims to emulate HVS via determining areas of visual interest. Thus, we believe that saliency plays a crucial role in human perception. In this work, we conduct an empirical study that reveals the relation between IQA and Saliency Prediction tasks, demonstrating that the former incorporates knowledge of the latter. Moreover, we introduce a novel SACID dataset of saliency-aware compressed images and conduct a large-scale comparison of classic and neural-based IQA methods. All supplementary code and data will be available at the time of publication.
Abstract (translated)
在过去的几年里，深度神经网络在图像质量评估（IQA）方面取得了显著的进步。然而，由于深度神经网络的复杂性，其成功背后的原因仍然不明确。IQA 的目标描述了人视觉系统（HVS）的工作，并旨在创建其有效的近似。另一方面，Saliency 预测任务旨在通过确定视觉兴趣区域来模仿 HVS。因此，我们认为 高亮在人类感知中扮演着关键角色。在这项工作中，我们进行了一项实证研究，揭示了 IQA 和 Saliency 预测任务之间的关系，证明了前一个包含了后一个的知识。此外，我们还引入了一个名为 SACID 的适用于高亮度的压缩图像的新 SACID 数据集，并对基于经典方法和神经网络的 IQA 方法进行了大规模比较。所有补充代码和数据将在发表时提供。
URL
https://arxiv.org/abs/2405.04997
