Multi-Task Rank Learning for Image Quality Assessment
文献类型：会议论文
| 作者 | Long Xu; Jia Li; Weisi Lin; Yongbing Zhang; Lin Ma; Yuming Fang; Yun Zhang; Yihua Yan |
| 出版日期 | 2015 |
| 会议名称 | ICASSP2015 |
| 会议地点 | Brisbane, Australia |
| 英文摘要 | In practice, multiple types of distortions are associated with an image quality degradation process. The existing machine learning (ML) based image quality assessment (IQA) approaches generally established a unified model for all distortion types, or each model is trained independently for each distortion type by using single-task learning, which lead to the poor generalization ability of the models as applied to practical image processing. There are often the underlying cross relatedness amongst these single-task learnings in IQA, which is ignored by the previous approaches. To solve this problem, we propose a multi-task learning framework to train IQA models simultaneously across individual tasks each of which concerns one distortion type. These relatedness can be therefore exploited to improve the generalization ability of IQA models from single-task learning. In addition, pairwise image quality rank instead of image quality rating is optimized in learning task. By mapping image quality rank to image quality rating, a novel no-reference (NR) IQA approach can be derived. The experimental results confirm that the proposed Multi-task Rank Learning based IQA (MRLIQ) approach is prominent among all state-of-the-art NR-IQA approaches. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/6976]  |
| 专题 | 深圳先进技术研究院_数字所 |
| 作者单位 | 2015 |
| 推荐引用方式 GB/T 7714 | Long Xu,Jia Li,Weisi Lin,et al. Multi-Task Rank Learning for Image Quality Assessment[C]. 见:ICASSP2015. Brisbane, Australia. |
入库方式： OAI收割
来源：深圳先进技术研究院
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