Improved Intra Prediction Algorithm for HEVC with Conventional and Convolutional Neural Network Approach

Q4 Mathematics
Swamy T N, Ramesha K
{"title":"Improved Intra Prediction Algorithm for HEVC with Conventional and Convolutional Neural Network Approach","authors":"Swamy T N, Ramesha K","doi":"10.17762/msea.v71i2.86","DOIUrl":null,"url":null,"abstract":"Video compression has evolved since last decade and has extended its employability to many applications such as high definition televisions, video conferencing etc. High definition videos have nearly two times of bit rate than standard definition videos. Hence, video compression is an important approach to obtain information with lesser data size. The paper proposes an improved approach for intra-prediction in HEVC with modified resolution enhancement approach and intra prediction is considered for all 35 modes. The obtained results shows that the similarity between input and output is nearly same and PSNR average is around 42. Further, a deep learning approach has also been incorporated and coding unit depth is predicted for intra prediction. The developed model has provided 85% accuracy maintaining the similar PSNR.","PeriodicalId":37943,"journal":{"name":"Philippine Statistician","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Philippine Statistician","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17762/msea.v71i2.86","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Mathematics","Score":null,"Total":0}
引用次数: 0

Abstract

Video compression has evolved since last decade and has extended its employability to many applications such as high definition televisions, video conferencing etc. High definition videos have nearly two times of bit rate than standard definition videos. Hence, video compression is an important approach to obtain information with lesser data size. The paper proposes an improved approach for intra-prediction in HEVC with modified resolution enhancement approach and intra prediction is considered for all 35 modes. The obtained results shows that the similarity between input and output is nearly same and PSNR average is around 42. Further, a deep learning approach has also been incorporated and coding unit depth is predicted for intra prediction. The developed model has provided 85% accuracy maintaining the similar PSNR.
基于传统和卷积神经网络的HEVC内预测改进算法
近十年来,视频压缩技术不断发展,并已扩展到许多应用领域,如高清电视、视频会议等。高清晰度视频的比特率几乎是标准清晰度视频的两倍。因此,视频压缩是一种以较小的数据量获取信息的重要方法。本文提出了一种改进的HEVC内预测方法,改进了分辨率增强方法,并考虑了所有35种模式的内预测。得到的结果表明,输入和输出的相似度基本相同,平均PSNR在42左右。此外,还采用了深度学习方法,并对编码单元深度进行了预测。所开发的模型在保持相似的PSNR的情况下提供了85%的精度。
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
Philippine Statistician
Philippine Statistician Mathematics-Statistics and Probability
CiteScore
0.50
自引率
0.00%
发文量
92
期刊介绍: The Journal aims to provide a media for the dissemination of research by statisticians and researchers using statistical method in resolving their research problems. While a broad spectrum of topics will be entertained, those with original contribution to the statistical science or those that illustrates novel applications of statistics in solving real-life problems will be prioritized. The scope includes, but is not limited to the following topics:  Official Statistics  Computational Statistics  Simulation Studies  Mathematical Statistics  Survey Sampling  Statistics Education  Time Series Analysis  Biostatistics  Nonparametric Methods  Experimental Designs and Analysis  Econometric Theory and Applications  Other Applications
×
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:604180095
Book学术官方微信
小红书