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Formal photograph compression algorithm based on object segmentation

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Abstract

Small storage space for photographs in formal documents is increasingly necessary in today’s needs for huge amounts of data communication and storage. Traditional compression algorithms do not sufficiently utilize the distinctness of formal photographs. That is, the object is an image of the human head, and the background is in unicolor. Therefore, the compression is of low efficiency and the image after compression is still space-consuming. This paper presents an image compression algorithm based on object segmentation for practical high-efficiency applications. To achieve high coding efficiency, shape-adaptive discrete wavelet transforms are used to transformation arbitrarily shaped objects. The areas of the human head and its background are compressed separately to reduce the coding redundancy of the background. Two methods, lossless image contour coding based on differential chain, and modified set partitioning in hierarchical trees (SPIHT) algorithm of arbitrary shape, are discussed in detail. The results of experiments show that when bit per pixel (bpp)is equal to 0.078, peak signal-to-noise ratio (PSNR) of reconstructed photograph will exceed the standard of SPIHT by nearly 4dB.

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Authors and Affiliations

  1. Laboratory. for Biomedical Image Processing, Institute for Pattern Recognition & Artificial Intelligence, Huazhong University of Science & Technology, Wuhan, 430074, PRC

    Li Zhu & Guo-You Wang

  2. School of Mechanical and Aeropace Engineering, Nanyang Technological Vnlversity, Sigapore, 639798, Sigapore

    Chen Wang

Authors
  1. Li Zhu
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  2. Guo-You Wang
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  3. Chen Wang
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Corresponding author

Correspondence to Guo-You Wang.

Additional information

This work was supported by National Natural Science Foundation of China (No. 60372066).

Li Zhu graduated from Huazhong University of Science and Technology (HUST), PRC, in 2000. He received the M. Sc. degree from HUST, PRC, in 2003. He is currently a Ph. D. candidate at Institute for Pattern Recognition and Artificial Intelligence, HUST.

His research interests include pattern recognition, digital image process, image compression, digital video coding, quality assessment, and computational vision.

Guo-You Wang received his B. Sc. and M. Sc. degrees in electronics and information engineering from the Huazhong University of Science and Technology, PRC, in 1988 and 1992, respectively. Currently, he is a professor in the Institute for Pattern Recognition and Artificial Intelligence at Huazhong University of Science and Technology (HUST), PRC. He received the Best Paper Award of National Natural Science Foundation of China in October 2007.

His research interests include artificial intelligence, image processing, robotics, and control theory.

Chen Wang received his B. Sc. and M. Sc. degrees in control science and engineering from Huazhong University of Science and Technology (HUST), PRC, in 2003 and 2006, respectively. He is currently a Ph. D. candidate at the School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore.

His research interests include digital image processing and multimedia digital signal processing.

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Zhu, L., Wang, GY. & Wang, C. Formal photograph compression algorithm based on object segmentation. Int. J. Autom. Comput. 5, 276–283 (2008). https://doi.org/10.1007/s11633-008-0276-8

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  • DOI: https://doi.org/10.1007/s11633-008-0276-8

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