融合空时感知特性的无参考视频质量评估算法

doi:10.3969/j.issn.0372-2112.2017.03.008

电子学报 ›› 2017, Vol. 45 ›› Issue (3): 557-564.DOI: 10.3969/j.issn.0372-2112.2017.03.008

融合空时感知特性的无参考视频质量评估算法

吴泽民¹, 彭韬频¹, 田畅¹, 胡磊¹, 王露萌²

1. 中国人民解放军理工大学通信工程学院, 江苏南京 210007;
2. 电子科技大学外国语学院, 四川成都 611731

收稿日期:2015-03-02 修回日期:2015-05-25 出版日期:2017-03-25
- 通讯作者:
- 田畅
- 作者简介:
- 吴泽民 男,1973年生,四川成都人,2002年毕业于解放军理工大学通信工程学院,获博士学位.现为解放军理工大学通信工程学院副教授,研究方向为数据链技术、雷达数据融合、图像视频分析;彭韬频 男,1989年生,四川内江人,2012年毕业于电子科技大学新信息对抗技术系,获学士学位.现为解放军理工大学硕士研究生,研究方向为视频质量分析
- 基金资助:
- 光电控制技术重点实验室和航空科学基金联合资助项目 (No.20125286005）; 国家自然科学基金青年基金 (No.61501509）

Blind VQA Pooling Spatial-Temporal Perceptual Characteristics

WU Ze-min¹, PENG Tao-pin¹, TIAN Chang¹, HU Lei¹, WANG Lu-meng²

1. College of Communications Engineering, PLA University of Science and Technology, Nanjing, Jiangsu 210007, China;
2. School of Foreign Languages, University of Electronic Science and Technology of China, Chengdu, Sichuang 611731, China

Received:2015-03-02 Revised:2015-05-25 Online:2017-03-25 Published:2017-03-25
- Supported by:
- Project supported by the Key Laboratory of Optoelectronic Control Technology and Aviation Science Foundation (No.20125286005); Youth Fund of National Natural Science Foundation of China (No.61501509)

摘要/Abstract

摘要：

本文通过简化视频质量评估中人眼感知模型的复杂性，提出了一种新的无参考视频质量评估模型.首先通过分别抽取视频的空间域和时间域特征，然后按照视频局部块、视频帧、视频段等从细到粗的不同粒度，模拟人眼感知特性进行多重加权汇聚，最终得到整段视频的特征向量描述.本方法以支持向量回归器为评估模型训练工具，通过有监督的视频样本库训练，以无参考方式完成未知视频的质量评估.实验结果表明，该评估算法的性能不但要优于当前已知最经典的无参考评估算法Video BLLINDS，而且与部分参考评估算法相当.

关键词: 视频质量评估, 人眼视觉系统, 离散余弦变换, 支持向量回归器

Abstract:

This paper proposes a no-reference video quality assessment model by reducing the complexity of the human visual system(HVS).The characteristics of spatial domain and temporal domain of the videos are firstly extracted.Then multi-weight convergence is conducted by simulating visual perception according to different granularity from fine-grained to coarse-grained of video local block,video frame,video segment,etc.Finally the feature vector of the whole video is achieved.The support vector regression(SVR) is taken as quality assessment tool in this algorithm.The quality assessment of the unknown video is obtained without reference after supervised training.The experiments we have done show that the algorithm is not only superior to all of the other no-reference quality assessment algorithms,but also can be compared to part-reference algorithms.

Key words: video quality assessment, human visual system, discrete cosine transform(DCT), support vector regressor(SVR)

中图分类号:

TP391

吴泽民, 彭韬频, 田畅, 等. 融合空时感知特性的无参考视频质量评估算法[J]. 电子学报, 2017, 45(3): 557-564.

WU Ze-min, PENG Tao-pin, TIAN Chang, et al. Blind VQA Pooling Spatial-Temporal Perceptual Characteristics[J]. Acta Electronica Sinica, 2017, 45(3): 557-564.

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融合空时感知特性的无参考视频质量评估算法

Blind VQA Pooling Spatial-Temporal Perceptual Characteristics

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