Multimedia Image Compression Method Based on Biorthogonal Wavelet and Edge Intelligent Analysis

被引：6
作者：
Liu, Tao [1 ]
Wu, Yalin [2 ,3 ]
机构：
[1] Zhengzhou Normal Univ, Coll Informat Sci & Technol, Zhengzhou 450044, Peoples R China
[2] Jeonju Univ, Dept Smart Media, Jeonju 560759, South Korea
[3] Jiangxi Software Vocat Univ Technol, Sch Informat Engn, Nanchang 330041, Jiangxi, Peoples R China
来源：
IEEE ACCESS | 2020年 / 8卷
关键词：
Multimedia image compression; biorthogonal wavelet; multimedia transmission; discrete cosine transform; FORGERY DETECTION; DISCRETE WAVELET; DIGITAL IMAGES; TRANSFORM; MODEL; ENCRYPTION;
D O I：
10.1109/ACCESS.2020.2984263
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
At present, network image communication is still restricted by channel coding, image and multimedia transmission and other key technologies. Therefore, the transmission process needs to convert the image signal into a digital signal, and then use the relevant band compression technology to reduce these signals to narrow the occupied frequency band, that is, to reduce the amount of information to be transmitted in synchronous transmission. Wavelet transform is a powerful tool for image compression because of its low entropy, multi-resolution, decorrelation and flexible base selection. In this paper, a method of multimedia image compression based on biorthogonal wavelet packet is proposed, which includes the establishment of linear phase biorthogonal wavelet basis, the selection of 3 or 4 levels of wavelet decomposition and reconstruction stage, and the combination of improved band division and preservation strategy. Finally, a compression test is performed based on the selected wavelet basis function and the optimal decomposition and reconstruction layers of the standard test image, which enables to obtain a more ideal compression ratio.
引用
收藏
页码：67354 / 67365
页数：12
相关论文
共 43 条
[1]  
Alnazzawi T, 2019, INT J ADV COMPUT SC, V10, P191
[2]   Wavelet-based computationally-efficient computer-aided characterization of liver steatosis using conventional B-mode ultrasound images [J].
Amin, Manar N. ;
Rushdi, Muhammad A. ;
Marzaban, Raghda N. ;
Yosry, Ayman ;
Kim, Kang ;
Mahmoud, Ahmed M. .
BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2019, 52 :84-96
[3]   A new digital image tamper detection algorithm based on integer wavelet transform and secured by encrypted authentication sequence with 3D quantum map [J].
Barani, Milad Jafari ;
Valandar, Milad Yousefi ;
Ayubi, Peyman .
OPTIK, 2019, 187 :205-222
[4]   An improved wavelet-based image coder for embedded greyscale and colour image compression [J].
Brahimi, Tahar ;
Laouir, Farid ;
Boubchir, Larbi ;
Ali-Cherif, Arab .
AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS, 2017, 73 :183-192
[5]   Operator-adapted wavelets for finite-element differential forms [J].
Budninskiy, Max ;
Owhadi, Houman ;
Desbrun, Mathieu .
JOURNAL OF COMPUTATIONAL PHYSICS, 2019, 388 :144-177
[6]   A novel sparse representation model for pedestrian abnormal trajectory understanding [J].
Chen, Zhijun ;
Cai, Hao ;
Zhang, Yishi ;
Wu, Chaozhong ;
Mu, Mengchao ;
Li, Zhixiong ;
Sotelo, Miguel Angel .
EXPERT SYSTEMS WITH APPLICATIONS, 2019, 138
[7]   Enhancing textural differences using wavelet-based texture characteristics morphological component analysis: A preprocessing method for improving image segmentation [J].
Chi, Jianning ;
Eramian, Mark .
COMPUTER VISION AND IMAGE UNDERSTANDING, 2017, 158 :49-61
[8]  
Choudhary Alpa, 2018, Procedia Computer Science, V132, P1781, DOI 10.1016/j.procs.2018.05.153
[9]  
Dass Rajeshwar, 2018, Procedia Computer Science, V132, P1543, DOI 10.1016/j.procs.2018.05.118
[10]   Application of Wavelet Neural Networks for Monitoring of Extraction of Carbon Multi-Functional Medical Nano-Agents from the Body [J].
Dolenko, Tatiana ;
Efitorov, Alexander ;
Sarmanova, Olga ;
Kotova, Olga ;
Isaev, Igor ;
Laptinskiy, Kirill ;
Dolenko, Sergey ;
Burikov, Sergey .
POSTPROCEEDINGS OF THE 9TH ANNUAL INTERNATIONAL CONFERENCE ON BIOLOGICALLY INSPIRED COGNITIVE ARCHITECTURES (BICA 2018), 2018, 145 :177-183
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