No-Reference Quality Prediction of Distorted/Decompressed Images Using ANFIS

I. De, J. Sil
{"title":"No-Reference Quality Prediction of Distorted/Decompressed Images Using ANFIS","authors":"I. De, J. Sil","doi":"10.1109/ICCTD.2009.59","DOIUrl":null,"url":null,"abstract":"Assessing quality of distorted/decompressed images without reference to the original image is difficult because extracted features are not exact and complex relationship exists between image features and its visual quality. The paper aims at assessing the quality of distorted/decompressed images without any reference to the original image by developing a fuzzy inference system (FIS). Five benchmark images are decompressed with varied codebook size and divided into different regions. Several statistical features of these regions and mean opinion score (MOS) based quality of images are applied as input and output, respectively of the FIS rule generation process. The parameters of the FIS are tuned to improve accuracy in quality prediction by implementing an adaptive network based fuzzy inference system (ANFIS). The error between the computed output of the FIS (predicted quality) and the supplied target value (quality obtained under ideal conditions of decompression) is minimized using supervised learning algorithm. Quality of decompressed and various noise incorporated test images are predicted without reference to the original image producing output comparable with other no reference techniques. Results are validated with the objective and subjective image quality measures.","PeriodicalId":269403,"journal":{"name":"2009 International Conference on Computer Technology and Development","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2009-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Conference on Computer Technology and Development","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCTD.2009.59","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

Assessing quality of distorted/decompressed images without reference to the original image is difficult because extracted features are not exact and complex relationship exists between image features and its visual quality. The paper aims at assessing the quality of distorted/decompressed images without any reference to the original image by developing a fuzzy inference system (FIS). Five benchmark images are decompressed with varied codebook size and divided into different regions. Several statistical features of these regions and mean opinion score (MOS) based quality of images are applied as input and output, respectively of the FIS rule generation process. The parameters of the FIS are tuned to improve accuracy in quality prediction by implementing an adaptive network based fuzzy inference system (ANFIS). The error between the computed output of the FIS (predicted quality) and the supplied target value (quality obtained under ideal conditions of decompression) is minimized using supervised learning algorithm. Quality of decompressed and various noise incorporated test images are predicted without reference to the original image producing output comparable with other no reference techniques. Results are validated with the objective and subjective image quality measures.
基于ANFIS的失真/解压缩图像无参考质量预测
由于提取的图像特征不准确,图像特征与视觉质量之间存在复杂的关系,因此在不参考原始图像的情况下对失真/解压缩图像进行质量评估是困难的。本文旨在通过开发一个模糊推理系统(FIS)来评估不参考原始图像的失真/解压缩图像的质量。用不同的码本大小对5个基准图像进行解压,并将其划分到不同的区域。这些区域的一些统计特征和基于平均意见评分(MOS)的图像质量分别作为FIS规则生成过程的输入和输出。通过实现基于自适应网络的模糊推理系统(ANFIS),对模糊推理系统的参数进行了调整,提高了质量预测的精度。利用监督学习算法使FIS的计算输出(预测质量)与提供的目标值(理想解压缩条件下获得的质量)之间的误差最小化。在不参考原始图像的情况下,预测了解压和各种噪声合并的测试图像的质量,产生的输出与其他无参考技术相当。通过客观和主观图像质量测量对结果进行了验证。
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
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信