Benchmark Dataset and Pair-Wise Ranking Method for Quality Evaluation of Night-Time Image Enhancement

IF 8.4 1区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Transactions on Multimedia Pub Date : 2024-04-22 DOI:10.1109/TMM.2024.3391907

Xuejin Wang;Leilei Huang;Hangwei Chen;Qiuping Jiang;Shaowei Weng;Feng Shao

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引用次数: 0

Abstract

Night-time image enhancement (NIE) aims at boosting the intensity of low-light regions while suppressing noises or light effects in night-time images, and numerous efforts have been made for this task. However, few explorations focus on the quality evaluation issue of enhanced night-time images (ENTIs), and how to fairly compare the performance of different NIE algorithms remains a challenging problem. In this paper, we firstly construct a new Real-world Night-Time Image Enhancement Quality Assessment (i.e., RNTIEQA) dataset that includes two typical types of night-time scenes (i.e., extremely low light and uneven light scenes), and carry out human subjective studies to compare the quality of ENTIs obtained by a set of representative NIE algorithms. Afterwards, a new objective ranking method that comprehensively considering image intrinsic and impairment attributes is proposed for automatically predicting the quality of ENTIs. Experimental results on our RNTIEQA dataset demonstrate that the proposed method outperforms the off-the-shelf competitors. Our dataset and code will be released at https://github.com/Leilei-Huang-work/RNTIEQA-dataset .

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用于夜间图像增强质量评估的基准数据集和配对排序法

夜间图像增强（NIE）旨在增强低照度区域的强度，同时抑制夜间图像中的噪声或光效应。然而，很少有人关注增强夜景图像（ENTI）的质量评估问题，如何公平地比较不同 NIE 算法的性能仍然是一个具有挑战性的问题。本文首先构建了一个新的真实世界夜间图像增强质量评估（即 RNTIEQA）数据集，其中包括两种典型的夜间场景（即光线极弱和光线不均的场景），并进行了人类主观研究，以比较一组具有代表性的 NIE 算法所获得的 ENTI 的质量。随后，提出了一种综合考虑图像内在属性和损伤属性的新的客观排名方法，用于自动预测 ENTI 的质量。在我们的 RNTIEQA 数据集上的实验结果表明，所提出的方法优于现成的竞争对手。我们的数据集和代码将在 https://github.com/Leilei-Huang-work/RNTIEQA-dataset 上发布。

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来源期刊

IEEE Transactions on Multimedia 工程技术-电信学

CiteScore

11.70

自引率

11.00%

发文量

576

审稿时长

5.5 months

期刊介绍： The IEEE Transactions on Multimedia delves into diverse aspects of multimedia technology and applications, covering circuits, networking, signal processing, systems, software, and systems integration. The scope aligns with the Fields of Interest of the sponsors, ensuring a comprehensive exploration of research in multimedia.