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基于多层BP神经网络的无参考视频质量客观评价

姚军财 申静 黄陈蓉

姚军财, 申静, 黄陈蓉. 基于多层BP神经网络的无参考视频质量客观评价. 自动化学报, 2021, x(x): 1−14 doi: 10.16383/j.aas.c190539
引用本文: 姚军财, 申静, 黄陈蓉. 基于多层BP神经网络的无参考视频质量客观评价. 自动化学报, 2021, x(x): 1−14 doi: 10.16383/j.aas.c190539
Yao Jun-Cai, Shen Jing, Huang Chen-Rong. No reference video quality objective assessment based on multilayer BP neural network. Acta Automatica Sinica, 2021, x(x): 1−14 doi: 10.16383/j.aas.c190539
Citation: Yao Jun-Cai, Shen Jing, Huang Chen-Rong. No reference video quality objective assessment based on multilayer BP neural network. Acta Automatica Sinica, 2021, x(x): 1−14 doi: 10.16383/j.aas.c190539

基于多层BP神经网络的无参考视频质量客观评价

doi: 10.16383/j.aas.c190539
基金项目: 国家自然科学基金(61301237)、江苏省自然科学基金面上项目(BK20201468)、南京工程学院高层次引进人才基金(YKJ201981)和西安交通大学博士后基金(2018M633512).
详细信息
    作者简介:

    姚军财:博士, 南京工程学院计算机工程学院教授.主要研究方向为图像和视频处理, 计算机视觉与模式识别. 本文通信作者. E-mail: yjc4782@163.com

    申静:南京工程学院计算机工程学院副教授. 主要研究方向为图像和视频处理、多媒体技术和人工智能. E-mail: shenjingtg@163.com

    黄陈蓉:博士, 南京工程学院计算机工程学院教授. 主要研究方向为图像分割和编码、计算机视觉与模式识别. E-mail: huangcr@njit.edu.cn

  • 中图分类号: TN919.81

No Reference Video Quality Objective Assessment Based on Multilayer BP Neural Network

Funds: Supported by National Natural Science Foundation of China (61301237), Natural Science Foundation of Jiangsu Province, China (BK20201468), Scientific Research Foundation for Advanced Talents, Nanjing Institute of Technology (YKJ201981), and Postdoctoral Science Foundation of Xi'an Jiaotong University (2018M633512).
  • 摘要: 机器学习在视频质量评价(Video quality assessment, VQA)模型回归方面具有较大的优势, 能够较大地提高构建模型的精度. 基于此, 设计了合理的多层BP神经网络, 并以提取的失真视频的内容特征、编解码失真特征、传输失真特征及其视觉感知效应特征参数为输入, 通过构建的数据库中的样本对其进行训练学习, 构建了一个无参考VQA模型. 在模型构建中, 首先采用图像的亮度和色度及其视觉感知、图像的灰度梯度期望值、图像的模糊程度、局部对比度、运动矢量及其视觉感知、场景切换特征、比特率、初始时延、单次中断时延、中断频率和中断平均时长共11个特征, 来描述影响视频质量的4个主要方面, 并对建立的两个视频数据库中的大量视频样本, 提取其特征参数; 再以该特征参数作为输入, 对设计的多层BP神经网络进行训练, 从而构建VQA模型; 最后, 对所提模型进行测试, 同时与14种现有的VQA模型进行对比分析, 研究其精度、复杂性和泛化性能. 实验结果表明: 所提模型的精度明显高于其14种现有模型的精度, 其最低高出幅度为4.34%; 且优于该14种模型的泛化性能, 同时复杂性处于该15种模型中的中间水平. 综合分析所提模型的精度、泛化性能和复杂性表明, 所提模型是一种较好的基于机器学习的VQA模型.
  • 图  1  基于多层BP神经网络的无参考视频质量客观评价方法流程图

    Fig.  1  Flow chart of no reference video quality objective evaluation method based on multilayer BP neural network

    图  2  设置的多层BP神经网络结构图

    Fig.  2  Designed multilayer BP neural network structure

    图  3  LIVEour数据库中(80%训练, 20%测试)实验结果

    Fig.  3  Experimental results in LIVEour database (80% training, 20% testing)

    图  4  VIPSLour数据库中(80%训练, 20%测试)实验结果

    Fig.  4  Experimental results in VIPSLour database (80% training, 20% testing)

    图  5  LIVEour100%训练和VIPSLour20%测试的实验结果

    Fig.  5  Experimental results from training by 100% samples in LIVEour and testing 20% samples in VIPSLour

    图  6  VIPSLour中训练(100%样本)LIVEour中测试(20%样本)的实验结果

    Fig.  6  Experimental results from training by 100% samples in VIPSLour database and testing 20% samples in LIVEour database

    图  7  采用现有6种基于机器学习的VQA模型评价结果的PLCC和SROCC

    Fig.  7  PLCC and SROCC of VQA results with 6 existing models based on machine learning

    图  8  不同训练和测试样本比例下采用所提BP-VQA模型评价结果的PLCC和SROCC

    Fig.  8  PLCC and SROCC from Applying the proposed BP-VQA model to evaluate video quality under different training and test sample ratios

    图  9  所提BP-VQA模型与6种现有FR-VQA模型的精度对比

    Fig.  9  Accuracy comparison between the proposed BP-VQA model and six existing FR-VQA models

    图  10  所提模型与10种现有VQA模型的运算耗时对比

    Fig.  10  Comparisons of the computational time between the proposed model and 10 existing VQA models

    图  11  所提模型与8种现有模型的泛化性能对比

    Fig.  11  Comparison of generalization performance between the proposed model and eight existing models

    表  1  所提视频特征及其参数描述

    Table  1  Video features and description of their parameters

    信息描述特征特征名称参数值描述
    空域信息及其感知特征1图像局部对比度对比度平均值
    对比度最大值
    特征2亮度色度视觉感知亮度色度感知平均值
    亮度色度感知最大值
    特征3图像模糊度模糊度平均值
    模糊度最大值
    特征4图像灰度梯度分布及其视觉感知(内容复杂性视觉感知)结合HVS的灰度梯度期望平均值
    结合HVS的灰度梯度期望值的最大值
    每次中断时前3帧的结合HVS的灰度梯度期望平均值
    时域信息及其感知特征5运动信息及其感知结合MCSFst的运动矢量平均值
    结合MCSFst的运动矢量最大值
    特征6场景切换复杂性变化对比感知平均值
    复杂性变化对比感知最大值
    编解码特征7码率比特率
    传输时延特征8初始时延初始中断(缓冲)时延时长
    特征9中间中断时延中间单次中断(缓冲)时延时长
    特征10平均中断时长多次中断平均中断时长
    特征11中断频率单位时间中断次数
    下载: 导出CSV

    表  2  计算的4个相关性参数值

    Table  2  Calculated results of four correlation parameters

    样本数据库PLCCSROCCRMSEOR
    LIVEour(80%训练、20%测试)0.98860.98423.09050.0437
    VIPSLour(80%训练、20%测试)0.98420.978993.43890.04463
    下载: 导出CSV

    表  3  计算的4个相关性参数值

    Table  3  Calculated results of four correlation parameters

    样本说明(100%训练, 20%测试)PLCCSROCCRMSEOR
    LIVEour训练和VIPSLour测试0.90530.84437.78740.0940
    VIPSLour训练和LIVEour测试0.88930.85828.51380.1125
    下载: 导出CSV

    表  4  计算的4个相关性参数值

    Table  4  Calculated results of four correlation parameters

    样本说明PLCCSROCCRMSEOR
    LIVEour中90%训练和10%测试0.98970.98192.87920.03375
    LIVEour中70%训练和30%测试0.97750.97534.65180.07064
    LIVEour中50%训练和50%测试0.96630.95875.56810.07566
    LIVEour中30%训练和70%测试0.95040.94566.34640.08892
    VIPSLour中90%训练和10%测试0.98470.97153.46950.04362
    VIPSLour中70%训练和30%测试0.97510.96944.36010.05401
    VIPSLour中50%训练和50%测试0.96680.96485.13160.06715
    VIPSLour中30%训练和70%测试0.94710.94346.39540.07859
    下载: 导出CSV

    表  5  计算的4个相关性参数值

    Table  5  Calculated results of four correlation parameters

    样本(训练、测试)比例说明PLCCSROCCRMSEOR
    LIVEour80%和VIPSL20%0.88760.85888.44420.1116
    LIVEour50%和VIPSL50%0.87350.80668.43220.0970
    VIPSLour80%和LIVE20%0.87800.84869.35770.1267
    VIPSLour50%和LIVE50%0.85070.840310.71000.1449
    下载: 导出CSV

    表  6  所提BP-VQA模型与3种NR-VQA模型的精度对比

    Table  6  Accuracy comparison between the proposed BP-VQA model and three existing NR-VQA models

    数据库MetricLIVE DatabaseLIVEour BP-VQAVIPSLour BP-VQA
    NVSMC-VQABRVPVC
    PLCC0.7320.79270.85470.96630.9668
    SROCC0.7030.7720.8260.95870.9648
    下载: 导出CSV
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  • 收稿日期:  2019-07-19
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