3D image quality estimation (ANN) based on depth/disparity and 2D metrics

2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI) Pub Date : 2013-11-01 DOI:10.1109/CINTI.2013.6705177

Dragan D. Kukolj, D. Dordevic, David Okolišan, I. Ostojic, Dragana D. Sandić-Stanković, C. Hewage

{"title":"3D image quality estimation (ANN) based on depth/disparity and 2D metrics","authors":"Dragan D. Kukolj, D. Dordevic, David Okolišan, I. Ostojic, Dragana D. Sandić-Stanković, C. Hewage","doi":"10.1109/CINTI.2013.6705177","DOIUrl":null,"url":null,"abstract":"Immersive image/video services will be soon available to the mass market due to the technological advancement of 3D video technologies, which include 3D-Ready TV monitors at affordable prices. However, in order to provide demanding customers with a better service over resource limited (e.g., bandwidth) and unreliable communication channels, system parameters need to be changed “on the fly”. Measured 3D video quality can be used as feedback information to fine tune the system parameters. The main aim of this paper is to analyze and present impact of objective image quality assessment metrics on perception of 3D image/video. Neural Network statistical estimator was used to examine the correlation between objective measures on input image base and Differential Mean Opinion Score (DMOS) of used image base. For this purpose part of LIVE 3D Image Quality Database [7] was used. The results suggest that comparison of the neural network DMOS estimators based on full-reference and no-reference objective metrics shown very similar behavior and accuracy.","PeriodicalId":439949,"journal":{"name":"2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CINTI.2013.6705177","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 5

Abstract

Immersive image/video services will be soon available to the mass market due to the technological advancement of 3D video technologies, which include 3D-Ready TV monitors at affordable prices. However, in order to provide demanding customers with a better service over resource limited (e.g., bandwidth) and unreliable communication channels, system parameters need to be changed “on the fly”. Measured 3D video quality can be used as feedback information to fine tune the system parameters. The main aim of this paper is to analyze and present impact of objective image quality assessment metrics on perception of 3D image/video. Neural Network statistical estimator was used to examine the correlation between objective measures on input image base and Differential Mean Opinion Score (DMOS) of used image base. For this purpose part of LIVE 3D Image Quality Database [7] was used. The results suggest that comparison of the neural network DMOS estimators based on full-reference and no-reference objective metrics shown very similar behavior and accuracy.

查看原文本刊更多论文

基于深度/视差和二维度量的三维图像质量估计

由于3D视频技术的技术进步，包括价格合理的3D电视显示器，沉浸式图像/视频服务将很快进入大众市场。然而，为了在资源有限(例如，带宽)和不可靠的通信通道上为要求苛刻的客户提供更好的服务，需要“动态”更改系统参数。测量的三维视频质量可以作为反馈信息来微调系统参数。本文的主要目的是分析和展示客观图像质量评估指标对3D图像/视频感知的影响。使用神经网络统计估计器检验输入图像库的客观度量与使用图像库的差分平均意见评分(DMOS)之间的相关性。为此，使用LIVE 3D图像质量数据库[7]的一部分。结果表明，基于全参考和无参考客观指标的神经网络DMOS估计器的性能和精度非常相似。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI)

自引率

0.00%

发文量