论文标题
通过功能融合和多任务学习，无参考图像质量评估
No-Reference Image Quality Assessment via Feature Fusion and Multi-Task Learning
论文作者
论文摘要
Blind or no-reference image quality assessment (NR-IQA) is a fundamental, unsolved, and yet challenging problem due to the unavailability of a reference image.对于每天影响数十亿观众的流媒体和社交媒体行业至关重要。尽管以前的NR-IQA方法利用了不同的特征提取方法，但性能瓶颈仍然存在。 In this paper, we propose a simple and yet effective general-purpose no-reference (NR) image quality assessment (IQA) framework based on multi-task learning.我们的模型采用失真类型和主观人类得分来预测图像质量。我们提出了一种功能融合方法来利用失真信息来改善质量得分估计任务。 In our experiments, we demonstrate that by utilizing multi-task learning and our proposed feature fusion method, our model yields better performance for the NR-IQA task. To demonstrate the effectiveness of our approach, we test our approach on seven standard datasets and show that we achieve state-of-the-art results on various datasets.
Blind or no-reference image quality assessment (NR-IQA) is a fundamental, unsolved, and yet challenging problem due to the unavailability of a reference image. It is vital to the streaming and social media industries that impact billions of viewers daily. Although previous NR-IQA methods leveraged different feature extraction approaches, the performance bottleneck still exists. In this paper, we propose a simple and yet effective general-purpose no-reference (NR) image quality assessment (IQA) framework based on multi-task learning. Our model employs distortion types as well as subjective human scores to predict image quality. We propose a feature fusion method to utilize distortion information to improve the quality score estimation task. In our experiments, we demonstrate that by utilizing multi-task learning and our proposed feature fusion method, our model yields better performance for the NR-IQA task. To demonstrate the effectiveness of our approach, we test our approach on seven standard datasets and show that we achieve state-of-the-art results on various datasets.
