ZTE

ZTE Communications ›› 2015, Vol. 13 ›› Issue (2): 36-40.DOI: 10.3969/j.issn.1673-5188.2015.02.007

• Research Paper • Previous Articles     Next Articles

A Visual Lossless Image-Recompression Framework

Ping Lu1, Xia Jia1, Hengliang Zhu2, Ming Liu1, Shouhong Ding2, and Lizhuang Ma2   

  1. 1. ZTE Corporation, Shenzhen 518057, China;
    2. Shanghai Jiao Tong University, Shanghai 200240, China
  • Received:2015-03-16 Online:2015-06-25 Published:2015-06-25
  • About author:Ping Lu (lu.ping@zte.com.cn) received the ME degree in automatic control theory and applications from South East University. He is the chief executive of the Cloud Computing & IT R&D Institute of ZTE Corporation. His research interests include augmented reality and multimedia services technologies.
    Xia Jia (jia.xia@zte.com.cn) received the MS degree from Dalian University of Technology in 2001. She is a leader of Multimedia Technology Research Team at the Cloud Computing & IT R&D Institute of ZTE Corporation. Her research interests include computer vision and its application field.
    Hengliang Zhu (hengliang_zhu@163.com) received the MS degree from Fujian Normal University, China in 2010. He is now a PhD candidate in the Department of Computer Science and Engineering, Shanghai Jiao Tong University, China. His current research interests include image and video editing, computer vision, computer graphics, and digital media technology.
    Ming Liu (liu.ming83@zte.com.cn) received the BE and MS degrees from Harbin Engineering University, China in 2008 and 2011. He is now a senior engineer at the Cloud Computing & IT R&D Institute of ZTE Corporation. His research interests include augmented reality, visual search, and deep learning.
    Shouhong Ding (dingsh1987@yahoo.com.cn) received the BS and MS degrees from Dalian University of Technology, China in 2008 and 2011. He is currently a PhD candidate in the Department of Computer Science and Engineering, Shanghai Jiao Tong University, China. His current research interests include image and video editing, computer vision, computer graphics, and digital media technology.
    Lizhuang Ma (ma-lz@cs.sjtu.edu.cn) received the PhD degree from Zhejiang University, China in 1991. He is now a full professor and the head of Digital Media Technology and Data Reconstruction Lab at Shanghai Jiao Tong University, China. He is also the chairman of the Center of Information Science and Technology for Traditional Chinese Medicine at Shanghai Traditional Chinese Medicine University. His research interests include computer -aided geometric design, computer graphics, scientific data visualization, computer animation, digital media technology, and theory and applications for computer graphics, CAD/CAM.
  • Supported by:
    This research work was supported in part by China "973" Program under Grant No.2014CB340303.

A Visual Lossless Image-Recompression Framework

Ping Lu1, Xia Jia1, Hengliang Zhu2, Ming Liu1, Shouhong Ding2, and Lizhuang Ma2   

  1. 1. ZTE Corporation, Shenzhen 518057, China;
    2. Shanghai Jiao Tong University, Shanghai 200240, China
  • 作者简介:Ping Lu (lu.ping@zte.com.cn) received the ME degree in automatic control theory and applications from South East University. He is the chief executive of the Cloud Computing & IT R&D Institute of ZTE Corporation. His research interests include augmented reality and multimedia services technologies.
    Xia Jia (jia.xia@zte.com.cn) received the MS degree from Dalian University of Technology in 2001. She is a leader of Multimedia Technology Research Team at the Cloud Computing & IT R&D Institute of ZTE Corporation. Her research interests include computer vision and its application field.
    Hengliang Zhu (hengliang_zhu@163.com) received the MS degree from Fujian Normal University, China in 2010. He is now a PhD candidate in the Department of Computer Science and Engineering, Shanghai Jiao Tong University, China. His current research interests include image and video editing, computer vision, computer graphics, and digital media technology.
    Ming Liu (liu.ming83@zte.com.cn) received the BE and MS degrees from Harbin Engineering University, China in 2008 and 2011. He is now a senior engineer at the Cloud Computing & IT R&D Institute of ZTE Corporation. His research interests include augmented reality, visual search, and deep learning.
    Shouhong Ding (dingsh1987@yahoo.com.cn) received the BS and MS degrees from Dalian University of Technology, China in 2008 and 2011. He is currently a PhD candidate in the Department of Computer Science and Engineering, Shanghai Jiao Tong University, China. His current research interests include image and video editing, computer vision, computer graphics, and digital media technology.
    Lizhuang Ma (ma-lz@cs.sjtu.edu.cn) received the PhD degree from Zhejiang University, China in 1991. He is now a full professor and the head of Digital Media Technology and Data Reconstruction Lab at Shanghai Jiao Tong University, China. He is also the chairman of the Center of Information Science and Technology for Traditional Chinese Medicine at Shanghai Traditional Chinese Medicine University. His research interests include computer -aided geometric design, computer graphics, scientific data visualization, computer animation, digital media technology, and theory and applications for computer graphics, CAD/CAM.
  • 基金资助:
    This research work was supported in part by China "973" Program under Grant No.2014CB340303.

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Abstract: In this paper, we propose a novel image recompression framework and image quality assessment (IQA) method to efficiently recompress Internet images. With this framework image size is significantly reduced without affecting spatial resolution or perceptible quality of the image. With the help of IQA, the relationship between image quality and image evaluation scores can be quickly established, and the optimal quality factor can be obtained quickly and accurately within a pre determined perceptual quality range. This process ensures the image’s perceptual quality, which is applied to each input image. The test results show that, using the proposed method, the file size of images can be reduced by about 45%-60% without affecting their visual quality. Moreover, our new image-recompression framework can be used in to many different application scenarios.

Key words: image recompression, image quality assessment, user experi-ence, visual lossless

摘要： In this paper, we propose a novel image recompression framework and image quality assessment (IQA) method to efficiently recompress Internet images. With this framework image size is significantly reduced without affecting spatial resolution or perceptible quality of the image. With the help of IQA, the relationship between image quality and image evaluation scores can be quickly established, and the optimal quality factor can be obtained quickly and accurately within a pre determined perceptual quality range. This process ensures the image’s perceptual quality, which is applied to each input image. The test results show that, using the proposed method, the file size of images can be reduced by about 45%-60% without affecting their visual quality. Moreover, our new image-recompression framework can be used in to many different application scenarios.

关键词: image recompression, image quality assessment, user experi-ence, visual lossless