Training Quality-Aware Filters for No-Reference Image Quality Assessment
Training Quality-Aware Filters for No-Reference Image Quality Assessment
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DOI：
10.1109/mmul.2014.50
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发表时间：
2014-09
期刊：
影响因子：
3.2
通讯作者：
Lin Zhang;Zhongyi Gu;Xiaoxu Liu;Hongyu Li;Jianwei Lu
中科院分区：
文献类型：
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作者：
Lin Zhang;Zhongyi Gu;Xiaoxu Liu;Hongyu Li;Jianwei Lu
随着数字成像和通信技术使用的快速增长，现在对快速实用的图像质量评估 (IQA) 算法的需求很大，这些算法可以像人类一样一致地预测图像质量。作者提出了一种通用的无参考图像质量评估 (NR-IQA)，其目标是开发一种不需要有关非失真参考图像和失真类型的先验知识的模型。关键是使用学习质量感知滤波器（QAF）获得有效的图像表示。与其他回归模型不同，它们还使用随机森林来训练特征空间的映射。在 LIVE 和 CSIQ 数据集上进行的大量实验表明，所提出的 NR-IQA 度量 QAF 在预测精度和泛化能力方面比其他最先进的方法能够实现更好的预测性能。
With the rapid increase of digital imaging and communication technology usage, there's now great demand for fast and practical image quality assessment (IQA) algorithms that can predict an image's quality as consistently as humans. The authors propose a general-purpose, no-reference image quality assessment (NR-IQA) with the goal of developing a model that does not require prior knowledge about nondistorted reference images and the types of distortions. The key is to obtain effective image representations using learning quality-aware filters (QAFs). Unlike other regression models, they also use a random forest to train the mapping from the feature space. Extensive experiments conducted on the LIVE and CSIQ datasets demonstrate that the proposed NR-IQA metric QAF can achieve better prediction performance than the other state-of-the-art approaches in terms of both prediction accuracy and generalization capability.