基于视频时域感知特性的恰可察觉失真模型

邢亚芬; 殷海兵; 王鸿奎; 骆琼华

doi:10.11959/j.issn.1000-0801.2022030

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基于视频时域感知特性的恰可察觉失真模型

研究与开发 | 更新时间：2024-06-05

- 基于视频时域感知特性的恰可察觉失真模型
- Video temporal perception characteristics based just noticeable difference model
- 电信科学 2022年38卷第2期页码：92-102
- 作者机构：
  
  1. 杭州电子科技大学通信工程学院，浙江杭州 310018
  2. 华中科技大学电子信息与通信学院，湖北武汉 430074
- 作者简介：
  
  [ "邢亚芬（1997- ），女，杭州电子科技大学硕士生，主要研究方向为感知视频编码" ]
  [ "殷海兵（1974- ），男，博士，杭州电子科技大学教授，主要研究方向为数字视频编解码" ]
  [ "王鸿奎（1990- ），男，华中科技大学博士生，主要研究方向为感知视频编码" ]
  [ "骆琼华（1998- ），女，杭州电子科技大学硕士生，主要研究方向为感知视频编码" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61972123);国家自然科学基金资助项目;The National Natural Science Foundation of China(61931008);国家自然科学基金资助项目;The National Natural Science Foundation of China(62031009);浙江省“尖兵”“领雁”研发攻关计划项目;Zhejiang Provincial “Pioneer” and“Leading Goose” Research and Development Project(2022C01068)
- DOI：10.11959/j.issn.1000-0801.2022030
  中图分类号： TN919
- 网络出版日期：2022-02，
  
  纸质出版日期：2022-02-20
- 稿件说明：
移动端阅览
邢亚芬, 殷海兵, 王鸿奎, 等. 基于视频时域感知特性的恰可察觉失真模型[J]. 电信科学, 2022,38(2):92-102.

Yafen1 XING, Haibing YIN, Hongkui WANG, et al. Video temporal perception characteristics based just noticeable difference model[J]. Telecommunications science, 2022, 38(2): 92-102.
邢亚芬, 殷海兵, 王鸿奎, 等. 基于视频时域感知特性的恰可察觉失真模型[J]. 电信科学, 2022,38(2):92-102. DOI： 10.11959/j.issn.1000-0801.2022030.

Yafen1 XING, Haibing YIN, Hongkui WANG, et al. Video temporal perception characteristics based just noticeable difference model[J]. Telecommunications science, 2022, 38(2): 92-102. DOI： 10.11959/j.issn.1000-0801.2022030.

摘要

现有的时域恰可察觉失真（just noticeable distortion，JND）模型对时域特征参量的作用刻画尚不够充分，导致空时域JND模型精度不够理想。针对此问题，提出能准确刻画视频时域特性的特征参量以及异质特征参量同质化融合方法，并基于此改进时域JND模型。关注前景/背景运动、时域持续时间、时域预测残差波动强度、帧间预测残差等特征参量，用来刻画视频内容的时域特征；基于人眼视觉系统（human visual system， HVS）特性探索感知概率密度函数，将异质特征参量统一映射到自信息和信息熵尺度上，实现同质化融合度量；从能量分配的角度探究视觉注意与掩蔽的耦合方法，并据此构建时域JND权重模型。在空域JND阈值的基础上，融合时域权重以得到更加准确的空时域JND模型。为了评估空时域JND模型的性能，进行了主观质量评估实验，与现有的JND模型相比，在感知质量接近的情况下，提出的空时域JND模型能够容忍更多失真，具有更强的掩藏噪声的能力。

Abstract

The existing temporal domain JND(just noticeable distortion) models are not sufficient to depict the interaction between temporal parameters and HVS characteristics

leading to insufficient accuracy of the spatial-temporal JND model.To solve this problem

feature parameters that can accurately describe the temporal characteristics of the video were explored and extracted

as well as a homogenization method for fusing heterogeneous feature parameters

and the temporal domain JND model based on this was improved.The feature parameters were investigated including foreground and background motion

temporal duration along the motion trajectory

residual fluctuation intensity along motion trajectory and adjacent inter-frame prediction residual

etc.

which were used to characterize the temporal characteristics.Probability density functions for these feature parameters in the perception sense according to the HVS(human visual system) characteristics were proposed

and uniformly mapping the heterogeneous feature parameters to the scales of self-information and information entropy to achieve a homogeneous fusion measurement.The coupling method of visual attention and masking was explored from the perspective of energy distribution

and the temporal-domain JND weight model was constructed accordingly.On the basis of the spatial JND threshold

the temporal domain weights was integrated to develop a more accurate spatial-temporal JND model.In order to evaluate the performance of the spatiotemporal JND model

a subjective quality evaluation experiment was conducted.Experimental results justify the effectiveness of the proposed model.

关键词

Keywords

references

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