A Customized Framework to Recompress Massive Internet Images

Recently, device storage capacity and transmission bandwidth requirements are facing a heavy burden on account of massive internet images. Generally, to improve user experience and save costs as much as possible, a lot of internet applications always focus on how to achieve appropriate image reeompr...

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Bibliographic Details
Published inJournal of computer science and technology Vol. 27; no. 6; pp. 1129 - 1139
Main Author 丁守鸿 黄飞跃 谢志峰 吴永坚 盛斌 马利庄
Format Journal Article
LanguageEnglish
Published Boston Springer US 01.11.2012
Springer Nature B.V
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China%Tencent Research, Shanghai 200233, China
Subjects
Algorithms
Analysis
Artificial Intelligence
Assessments
Bandwidths
Compression tests
Computer Science
Customization
Customizing
Data compression
Data Structures and Information Theory
Digital imaging
Electronic commerce
Image compression
Image processing systems
Image quality
Information Systems Applications (incl.Internet)
Internet
Performance evaluation
Quality standards
Recompression
Regular Paper
Software Engineering
Studies
Theory of Computation
User experience
Web portals
主观评价
互联网
再压缩
图片
定制
应用程序
框架
质量评价方法
image quality assessment
image recompression
massive internet image
Online AccessGet full text
ISSN1000-9000
1860-4749
DOI10.1007/s11390-012-1291-3

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Summary:Recently, device storage capacity and transmission bandwidth requirements are facing a heavy burden on account of massive internet images. Generally, to improve user experience and save costs as much as possible, a lot of internet applications always focus on how to achieve appropriate image reeompression. In this paper, we propose a novel framework to efficiently customize image recompression according to a variety of applications. First of all, we evaluate the input image＇s compression level and predict an initial compression level which is very close to the final output of our system using a prior learnt from massive images. Then, we iteratively recompress the input image to different levels and measure the perceptual similarity between the input image and the new result by a block-based coding quality method. According to the output of the quality assessment method, we can update the target compression level, or switch to the subjective evaluation, or return the final recompression result in our system pipeline control. We organize subjective evaluations based on different applications and obtain corresponding assessment report. At last, based on the assessment report, we set up a series of appropriate parameters for customizing image recompression. Moreover, our new framework has been successfully applied to many commercial applications, such as web portals, e-commerce, online game, and so on.
Bibliography:massive internet image, image recompression, image quality assessment
11-2296/TP
Recently, device storage capacity and transmission bandwidth requirements are facing a heavy burden on account of massive internet images. Generally, to improve user experience and save costs as much as possible, a lot of internet applications always focus on how to achieve appropriate image reeompression. In this paper, we propose a novel framework to efficiently customize image recompression according to a variety of applications. First of all, we evaluate the input image＇s compression level and predict an initial compression level which is very close to the final output of our system using a prior learnt from massive images. Then, we iteratively recompress the input image to different levels and measure the perceptual similarity between the input image and the new result by a block-based coding quality method. According to the output of the quality assessment method, we can update the target compression level, or switch to the subjective evaluation, or return the final recompression result in our system pipeline control. We organize subjective evaluations based on different applications and obtain corresponding assessment report. At last, based on the assessment report, we set up a series of appropriate parameters for customizing image recompression. Moreover, our new framework has been successfully applied to many commercial applications, such as web portals, e-commerce, online game, and so on.
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ISSN:1000-9000
1860-4749
DOI:10.1007/s11390-012-1291-3