Blind VQA Pooling Spatial-Temporal Perceptual Characteristics

WU Ze-min; PENG Tao-pin; TIAN Chang; HU Lei; WANG Lu-meng

doi:10.3969/j.issn.0372-2112.2017.03.008

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Blind VQA Pooling Spatial-Temporal Perceptual Characteristics

更新时间：2025-07-16

- Blind VQA Pooling Spatial-Temporal Perceptual Characteristics
- Acta Electronica Sinica Vol. 45, Issue 3, Pages: 557-564(2017)
- 作者机构：
  
  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
  3. 中国人民解放军理工大学通信工程学院,江苏,南京,210007
  4. 电子科技大学外国语学院,四川,成都,611731
- 作者简介：
- 基金信息：
  
  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)
- DOI：10.3969/j.issn.0372-2112.2017.03.008
  CLC： TP391
- Published Online：25 March 2017，
  
  Published：2017
- 稿件说明：
移动端阅览
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.
DOI：

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. DOI： 10.3969/j.issn.0372-2112.2017.03.008.

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.

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.

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