论文标题
Greenbiqa：轻巧的盲图质量评估方法
GreenBIQA: A Lightweight Blind Image Quality Assessment Method
论文作者
论文摘要
近年来，深层神经网络（DNNS）在具有大型预训练模型的盲图质量评估（BIQA）方面取得了巨大成功。他们的解决方案不能轻易部署在移动设备或边缘设备上，并且需要轻巧的解决方案。在这项工作中，我们提出了一种新型的BIQA模型，称为GreenBiQA，其目的是高性能，低计算复杂性和较小的模型大小。 Greenbiqa采用了一种无监督的功能生成方法和一种监督的功能选择方法来提取高质量感知的功能。然后，它训练XGBoost回归器，以预测测试图像的质量得分。我们在四个流行的IQA数据集上进行实验，其中包括两个合成渗透和两个正宗启动数据集。实验结果表明，GreenBiQA在绩效方面具有竞争力，对具有较低复杂性和较小模型尺寸的最先进的DNN。
Deep neural networks (DNNs) achieve great success in blind image quality assessment (BIQA) with large pre-trained models in recent years. Their solutions cannot be easily deployed at mobile or edge devices, and a lightweight solution is desired. In this work, we propose a novel BIQA model, called GreenBIQA, that aims at high performance, low computational complexity and a small model size. GreenBIQA adopts an unsupervised feature generation method and a supervised feature selection method to extract quality-aware features. Then, it trains an XGBoost regressor to predict quality scores of test images. We conduct experiments on four popular IQA datasets, which include two synthetic-distortion and two authentic-distortion datasets. Experimental results show that GreenBIQA is competitive in performance against state-of-the-art DNNs with lower complexity and smaller model sizes.
