结合空间域与变换域特征提取的盲立体图像质量评价

陈勇; 金曼莉; 朱凯欣; 刘焕淋; 陈东

doi:10.11999/JEIT200694

结合空间域与变换域特征提取的盲立体图像质量评价

doi: 10.11999/JEIT200694

1.
重庆邮电大学工业物联网与网络化控制教育部重点实验室重庆 400065
2.
重庆邮电大学通信与信息工程学院重庆 400065

基金项目: 国家自然科学基金 (51977021)

详细信息

作者简介:
陈勇：男，1963年生，博士，教授，主要从事图像处理

金曼莉：女，1997年生，硕士生，主要从事无参考图像质量评价

朱凯欣：女，1994年生，硕士，主要从事立体图像质量评价

刘焕淋：女，1970年生，博士生导师，教授，主要从事信号处理等方面的研究

陈东：男，1996年生，硕士，主要从事无参考图像质量评价与图像增强

通讯作者:
陈勇　chenyong@cqupt.edu.cn

中图分类号: TN911.73; TP391.41
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- 被引次数: 0
出版历程
- 收稿日期: 2020-08-06
- 修回日期: 2021-07-23
- 网络出版日期: 2021-08-27
- 刊出日期: 2021-10-18

Blind Stereo Image Quality Evaluation Based on Spatial Domain and Transform Domain Feature Extraction

1.
Key Laboratory of Industrial Internet of Things & Network Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (51977021)

摘要

摘要: 针对立体图像质量预测准确性不足的问题，该文提出了一种结合空间域和变换域提取质量感知特征的无参考立体图像质量评价模型。在空间域和变换域分别提取输入的左、右视图的自然场景统计特征，并在变换域提取合成独眼图的自然场景统计特征，然后将其输入到支持向量回归(SVR)中，训练从特征域到质量分数域的预测模型，并以此建立SIQA客观质量评价模型。在4个公开的立体图像数据库上与一些主流的立体图像质量评价算法进行对比，以在LIVE 3D Phase I图像库中的性能测试为例，Spearman秩相关系数、皮尔逊线性相关系数和均方根误差分别达到0.967,0.946和5.603，验证了所提算法的有效性。
- 立体图像 /
- 无参考 /
- 空间域特征 /
- 变换域特征
Abstract: For the problem of insufficient accuracy of stereo image quality prediction, a blind stereoscopic image quality assessment model combining spatial domain and transform domain to extract quality-aware features is proposed. Firstly, the statistical features of the natural scenes in the left and right views are extracted respectively in space domain and transformation domain, and statistical features of natural scenes from synthetic monocular images is extracted in transformation domain. Finally, Support Vector Regression (SVR) is used to train a stereoscopic image quality evaluation model from the feature domain to the quality score domain, so as to establish SIQA objective quality evaluation model. The performance of the proposed method is compared with some state-of-the-art full-reference, reduced-reference and no-reference stereoscopic image quality evaluation algorithms on the four public stereo image databases, taking the performance test in live 3D phase I image library as an example. SROCC of 0.967, PLCC of 0.946 and RMSE of 5.603 are achieved, which verifies the effectiveness of the proposed algorithm.
- Stereoscopic image /
- No reference /
- Spatial domain characteristics /
- Transform domain features

HTML全文

图 1 SIQA模型的整体框架

下载: 全尺寸图片幻灯片

图 2 左视图、右视图、合成独眼图及其相应的MSCN系数的统计分布直方图

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图 3 左右视图及纵向相关系数图的统计分布直方图

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图 4 参考图像及失真图像经DCT后的能量分布图

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图 5 不同频率子带的DCT系数

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图 6 所提模型在4个数据库中图像预测值和主观评分的散点图

下载: 全尺寸图片幻灯片

表 1 LIVE 3D Phase I和II图像库中的性能测试

失真类型	PLCC	SROCC	RMSE
WN	0.964(0.988)	0.958(0.964)	6.004(4.954)
JP2K	0.924 (0.927)	0.907(0.915)	6.518(5.361)
JPEG	0.730 (0.903)	0.785(0.900)	7.010(5.337)
Gblur	0.972 (0.979)	0.937(0.962)	5.412(3.478)
FF	0.943 (0.945)	0.913(0.921)	5.433(3.679)
All	0.967 (0.949)	0.946(0.935)	5.603(3.501)

下载: 导出CSV

表 2 LIVE 3D Phase I和II图像库中整体性能比较

对比算法		LIVE 3D Phase I			LIVE 3D Phase II
对比算法		PLCC	SROCC	RMSE	PLCCS	ROCCR	MSE
Lin^[29]	FR	0.936	0.931	5.744	0.911	0.893	4.647
Khan^[23]	FR	0.927	0.916	–	0.932	0.922	–
Chen FR^[14]	FR	0.833	0.915	6.268	0.770	0.888	4.892
Jiang^[24]	FR	0.945	0.933	5.276	0.916	0.903	4.523
Ma^[25]	RR	0.930	0.929	6.024	0.921	0.917	4.390
SINQ^[8]	NR	0.955	0.935	4.781	0.936	0.931	3.959
Zhou^[8]	NR	0.941	0.921	5.540	0.923	0.919	4.262
Karimi^[26]	NR	0.956	0.940	4.998	0.923	0.913	4.436
Yang-SAE^[27]	NR	0.961	0.949	–	0.938	0.928	–
Fezza^[13]	NR	–	–	–	0.925	0.908	3.018
BRISQUE^[28]	NR	0.910	0.901	6.793	0.782	0.770	7.038
本文算法	NR	0.967	0.946	5.603	0.949	0.935	3.501

下载: 导出CSV

表 3 跨库性能对比实验

方法	LIVE 3D Phase I/Phase II		LIVE 3D Phase II/Phase I
方法	PLCC	SROCC	PLCC	SROCC
Yang-SAE^[27]	0.861	0.846	0.860	0.845
BRISQUE^[28]	0.595	0.458	0.572	0.556
CNN-based^[30]	0.227	0.208	0.743	0.741
本文算法	0.853	0.849	0.883	0.868

下载: 导出CSV

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