Research and Exploration on Static Image Compression Technology Based on JPEG2000

Abstract:

This article introduces GPU-accelerated image processing parallel computing technology into standard core coding system of JPEG2000 static image compression and accelerates and designs the image compression process using CUDA acceleration principle. It also establishes the algorithm of image pixel array layered and reconstruction coding and realizes the coding of this algorithm using VC software. In order to verify the effectiveness and universal applicability of the algorithm and procedures, this paper compresses four images of different sizes and pixels in the static form of the JPEG2000. Through the comparison of the compression time, we can find that GPU hardware image processing system has a higher speedup ratio. With the increase of pixel and size, speedup ratio gradually increased which means that GPU acceleration has good adaptability.

• Periodical: Applied Mechanics and Materials (Volumes 644-650)
• Pages: 4182-4186
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.644-650.4182
• Online since: September 2014
• Authors: Hua Tian*, Ming Jun Li, Huan Huan Liu
• Keywords: GPU Acceleration, JPEG 2000, Reconstruction Coding, Speedup Ratio, Static Image, VC Software

References

[1] Jing Guo, Qingkui Chen. Fast image compression based on UDA . Computer Engineering and Design, Vol. 31 (14), (2010), pp.3302-3304.

[2] Xiaoping Fan, Zheyuan Xiong, Zhijie Chen, Shaohua Chen, Zhihua Qu. Research on wireless multimedia sensor networks video coding. Journal of Communication, Vol. 32 (9), (2011), pp.137-146.

[3] Zheyuan Xiong, Xiaoping Fan, Shaoqiang Liu, Yongzhou Li, Zhihua Qu, Zhi Zhong. A JPEG image coding algorithm suitable for wireless multimedia sensor network. Sensing Technology, Vol. 24 (10), (2011), pp.1489-1495.

[4] Maosheng Zhong, Hui Liu, Lei Liu. Quantitative calculation method of semantic relationship between vocabularies. Chinese information technology, Vol. 23 (2), (2009), pp.115-122.

[5] Xiaoli Song, Qing Wang. Preprocessing of parallelization of digital image based on GPGPU. Computer Measurement & Control, Vol. 17 (6), (2009), pp.1169-1171.

[6] Biao Hou, Feng Liu, Licheng Jiao, Huidong Bao. Image segmentation based on Wavelet domain hidden Markov tree model . The journal of Infrared and Millimeter Waves. Vol. 2, (2009).

DOI: 10.3724/sp.j.1010.2009.00156

[7] Qiang Pu, Daqing He, Wei Yang. A query language model estimation based on statistical semantic clustering. Computer Research and Development, (2009).

[8] Qiang Zhang, Yugen Sun, Ting Yang. The application of wireless sensor network in smart grid[J]. Chinese power, Vol. 43 (6), (2010), pp.31-36.

[9] Qiang Pu, Daqing He, Wei Yang. A query language model estimation based on statistical semantic clustering. Computer Research and Development, (2009).

[10] Zhiyuan Liu, Maosong Sun. Small-world effect and scale-free feature of Chinese words existing in the same network . Chinese Information Technology, Vol. 21 (6), (2007), pp.52-58.

[11] Maosheng Zhong, Hui Liu, Lei Liu. Quantitative calculation method of semantic relationship between vocabularies. Chinese information technology, Vol. 23 (2), (2009), pp.115-122.

[12] Peng Zhao, Huantong Geng, Qingsheng Cai, a K-means Clustering algorithm based on the weighted complex network features. Computer Technology and Development, Vol. 17 (9), (2007), pp.35-37.