无损压缩的JPEG编码相册

Hao Wu, Xiaoyan Sun, Jingyu Yang, Feng Wu
{"title":"无损压缩的JPEG编码相册","authors":"Hao Wu, Xiaoyan Sun, Jingyu Yang, Feng Wu","doi":"10.1109/VCIP.2014.7051625","DOIUrl":null,"url":null,"abstract":"The explosion in digital photography poses a significant challenge when it comes to photo storage for both personal devices and the Internet. In this paper, we propose a novel lossless compression method to further reduce the storage size of a set of JPEG coded correlated images. In this method, we propose jointly removing the inter-image redundancy in the feature, spatial, and frequency domains. For each album, we first organize the images into a pseudo video by minimizing the global predictive cost in the feature domain. We then introduce a disparity compensation method to enhance the spatial correlation between images. Finally, the redundancy between the compensated signal and the corresponding target image is adaptively reduced in the frequency domain. Moreover, our proposed scheme is able to losslessly recover not only raw images but also JPEG files. Experimental results demonstrate the efficiency of our proposed lossless compression, which achieves more than 12% bit-saving on average compared with JPEG coded albums.","PeriodicalId":166978,"journal":{"name":"2014 IEEE Visual Communications and Image Processing Conference","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Lossless compression of JPEG coded photo albums\",\"authors\":\"Hao Wu, Xiaoyan Sun, Jingyu Yang, Feng Wu\",\"doi\":\"10.1109/VCIP.2014.7051625\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The explosion in digital photography poses a significant challenge when it comes to photo storage for both personal devices and the Internet. In this paper, we propose a novel lossless compression method to further reduce the storage size of a set of JPEG coded correlated images. In this method, we propose jointly removing the inter-image redundancy in the feature, spatial, and frequency domains. For each album, we first organize the images into a pseudo video by minimizing the global predictive cost in the feature domain. We then introduce a disparity compensation method to enhance the spatial correlation between images. Finally, the redundancy between the compensated signal and the corresponding target image is adaptively reduced in the frequency domain. Moreover, our proposed scheme is able to losslessly recover not only raw images but also JPEG files. Experimental results demonstrate the efficiency of our proposed lossless compression, which achieves more than 12% bit-saving on average compared with JPEG coded albums.\",\"PeriodicalId\":166978,\"journal\":{\"name\":\"2014 IEEE Visual Communications and Image Processing Conference\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE Visual Communications and Image Processing Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/VCIP.2014.7051625\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE Visual Communications and Image Processing Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VCIP.2014.7051625","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

摘要

当涉及到个人设备和互联网的照片存储时,数码摄影的爆炸式增长给照片存储带来了重大挑战。在本文中,我们提出了一种新的无损压缩方法,以进一步减少一组JPEG编码的相关图像的存储大小。在该方法中,我们提出了在特征域、空间域和频域共同去除图像间冗余的方法。对于每个相册,我们首先通过最小化特征域的全局预测代价将图像组织成一个伪视频。然后引入视差补偿方法来增强图像之间的空间相关性。最后,在频域自适应降低补偿后的信号与目标图像之间的冗余。此外,我们提出的方案不仅可以无损地恢复原始图像,还可以无损地恢复JPEG文件。实验结果证明了我们提出的无损压缩方法的有效性,与JPEG编码的相册相比,平均节省了12%以上的比特。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Lossless compression of JPEG coded photo albums
The explosion in digital photography poses a significant challenge when it comes to photo storage for both personal devices and the Internet. In this paper, we propose a novel lossless compression method to further reduce the storage size of a set of JPEG coded correlated images. In this method, we propose jointly removing the inter-image redundancy in the feature, spatial, and frequency domains. For each album, we first organize the images into a pseudo video by minimizing the global predictive cost in the feature domain. We then introduce a disparity compensation method to enhance the spatial correlation between images. Finally, the redundancy between the compensated signal and the corresponding target image is adaptively reduced in the frequency domain. Moreover, our proposed scheme is able to losslessly recover not only raw images but also JPEG files. Experimental results demonstrate the efficiency of our proposed lossless compression, which achieves more than 12% bit-saving on average compared with JPEG coded albums.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A joint 3D image semantic segmentation and scalable coding scheme with ROI approach Disocclusion hole-filling in DIBR-synthesized images using multi-scale template matching Rate-distortion optimised transform competition for intra coding in HEVC Robust image registration using adaptive expectation maximisation based PCA Non-separable mode dependent transforms for intra coding in HEVC
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1