Knowledge Commons of Institute of Automation,CAS
Learning Deep Vector Regression Model for No-reference Image Quality Assessment | |
Jie, Gu1,2; Gaofeng, Meng1; Lingfeng, Wang1; Chunhong, Pan1 | |
2017 | |
会议名称 | The 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
会议日期 | March 5-9, 2017 |
会议地点 | New Orleans, USA |
摘要 | The goal of no-reference image quality assessment (NR-IQA) is to estimate human perceived image quality without access to either reference image or prior knowledge about distortion type. Previous approaches for this problem are typically based on a regression framework that maps the image features directly to a quality score. In contrast, psychological evidence shows that humans prefer to evaluate visual quality with qualitative descriptions, e.g., using a five-grade ordinal scale: "excellent", "good", "fair", "poor" and "bad". Based on this observation, we propose a vector regression model that predicts five belief scores rather than a single quality score. The belief scores are designed to indicate the confidences of the test image being assigned with these five quality grades. In addition, with the purpose of more extensive applications, a saliency-based pooling strategy is presented to convert the predicted confidences into objective quality scores. Extensive experiments performed on two benchmark datasets demonstrate that our approach achieves state-of-the-art performance and shows great generalization ability. |
关键词 | Image quality assessment perceptual image quality CNN vector regression saliency-based pooling |
收录类别 | EI |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/15341 |
专题 | 多模态人工智能系统全国重点实验室_先进时空数据分析与学习 模式识别国家重点实验室 |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Jie, Gu,Gaofeng, Meng,Lingfeng, Wang,et al. Learning Deep Vector Regression Model for No-reference Image Quality Assessment[C],2017. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
ICASSP2017.pdf(881KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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