计算机科学 ›› 2019, Vol. 46 ›› Issue (6A): 222-225.

• 模式识别与图像处理 • 上一篇    下一篇

一种联合Canny边缘检测和SPIHT的图像压缩方法

王亚鸽, 康晓东, 郭军, 洪睿, 李博, 张秀芳   

  1. 天津医科大学医学影像学院 天津300203
  • 出版日期:2019-06-14 发布日期:2019-07-02
  • 通讯作者: 康晓东(1964-),男,博士,教授,CCF高级会员,主要研究方向为医学图像处理、医疗信息系统集成,E-mail:2743315616@qq.com
  • 作者简介:王亚鸽(1992-),女,硕士生,主要研究方向为图像处理,E-mail:wangyage09@163.com;郭 军(1972-),男,实验师,主要研究方向为实验技术;洪 睿(1992-),男,硕士生,主要研究方向为图像处理;李 博(1986-),男,硕士生,主要研究方向为图像处理;张秀芳(1995-),女,主要研究方向为图像处理。
  • 基金资助:
    本文受天津市基金京津冀协同创新项目(17YFXTZC00020)资助。

Image Compression Method Combining Canny Edge Detection and SPIHT

WANG Ya-ge, KANG Xiao-dong, GUO Jun, HONG Rui, LI Bo, ZHANG Xiu-fang   

  1. School of Medical Imaging,Tianjin Medical University,Tianjin 300203,China
  • Online:2019-06-14 Published:2019-07-02

摘要: 针对SPIHT算法重构图像时会损失纹理细节信息的不足,提出了一种联合Canny边缘检测和SPIHT的图像压缩算法。首先,对图像进行Canny边缘检测,对提取的边缘图像进行Huffman编码及解码,得到边缘重构图像;其次,用SPIHT算法对图像进行编码,并对编码后的码流进行Huffman编码及解码,经SPIHT算法解码及小波逆变换后得到一幅重构图;最后,将得到的两幅重构图相加以恢复原图像。结果表明,在低比特率下,所提算法与SPIHT结合Huffman编码的算法相比,重构图像的PSNR值和信息熵有所提高,重构图像的信息量增多。

关键词: Canny边缘检测, SPIHT, 图像压缩, 信息熵

Abstract: To solve the problem that the reconstructed images obtained by SPIHT algorithm will lose texture details this paper proposed an image compression algorithm combining Canny edge detection and SPIHT.First,Canny edge detection is performed for the image,the extracted edge map,and edge recomposition is obtained;Secondly,SPIHT algorithm is used to encode the image,the encoded code stream is enconded and decoded by using Huffma,anda reconstructed image is obtained after SPIHT algorithm decoding and wavelet inverse transformation.inally,the two reconstructed images are added to recover the original image.The results show that the PSNR value and information entropy of reconstructed images are improved at low bites per pixel,compared with SPIHT combined with Huffman encode algorithm,and the information amount of reconstructed images is increased.

Key words: Canny edge detection, Image compression, Information entropy, SPIHT

中图分类号: 

  • TP391.41
[1]康晓东.影像信息学[M].北京:人民卫生出版社,2009:108-109.
[2]MALLAT S G.A theory for multi-resolution signal decomposition:The wavelet representation [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1989,11(7):674-693.
[3]SHAPIRO J M.Embedded image coding using zerotree of wavelet coefficient [J].IEEE Transactions on Signal Procession,1993,41(12):3445-3462.
[4]SAID A,PEARLMAN W A.A New,Fast,and Efficient Image Code Based on Set Partitioning in Hierarchical Trees[J].IEEE Transactions on Circuits and Systems for Video Technology,1996,6(3):243-250.
[5]CHEN H X,LIU Z G.Embedded and scale image coding based on virtual SPIHT[C]∥Nation Conference on Advanced Communication Technology.2011:764-767.
[6]ZHANG X J,HUANG W Y,LIU X.Improved listless zerotree coding image coding hardware algorithm based on lifting wavelet [J].Journal of Southwest Jiaotong University,2013,40(4):492-500.
[7]黄庆.无链表SPIHT图像压缩编码改进算法研究[D].南昌:南昌大学,2013.
[8]吴运泽.基于小波变换的多级树集合分裂图像压缩算法研究[D].沈阳:沈阳工业大学,2015.
[9]汤敏,陈秀梅,陈峰.基于Contourlet变换和SPIHT算法的彩色医学图像压缩[J].计算机科学,2014,41(1):303-306.
[10]王学春,刘申晓,常朝稳.基于混合域的改进SPIHT图像编码算法[J].计算机科学,2015,42(4):302-305.
[11]王文豪,姜明新,赵文东.基于Canny算子改进的边缘检测算法[J].中国科技论文,2017,12(8):910-915.
[12]王敏杰.图像边缘检测技术综述[C]∥2011年中国智能自动化学术会议论文集(第一分册).中国自动化学会智能自动化专业委员会,2011:6.
[13]CANNY J.A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence,1986,8(6):679-698.
[14]YANG S H,CHENG P F.Robust transmission of SPIHT-coded images over packet networks [J].IEEE Transactions on Circuits and Systems for Video Technology,2007,17(5):558-562.
[15]张倩妮.基于SPIHT方法的医学图像压缩算法研究[D].武汉:武汉轻工大学,2016.
[1] 夏源, 赵蕴龙, 范其林.
基于信息熵更新权重的数据流集成分类算法
Data Stream Ensemble Classification Algorithm Based on Information Entropy Updating Weight
计算机科学, 2022, 49(3): 92-98. https://doi.org/10.11896/jsjkx.210200047
[2] 周钢, 郭福亮.
基于特征选择的高维数据集成学习方法研究
Research on Ensemble Learning Method Based on Feature Selection for High-dimensional Data
计算机科学, 2021, 48(6A): 250-254. https://doi.org/10.11896/jsjkx.200700102
[3] 刘东, 王叶斐, 林建平, 马海川, 杨闰宇.
端到端优化的图像压缩技术进展
Advances in End-to-End Optimized Image Compression Technologies
计算机科学, 2021, 48(3): 1-8. https://doi.org/10.11896/jsjkx.201100134
[4] 赵钦炎, 李宗民, 刘玉杰, 李华.
基于信息熵的级联Siamese网络目标跟踪
Cascaded Siamese Network Visual Tracking Based on Information Entropy
计算机科学, 2020, 47(9): 157-162. https://doi.org/10.11896/jsjkx.190800160
[5] 刘子琦, 郭炳晖, 程臻, 杨小博, 殷子樵.
基于熵值模糊层次分析法的科技战略评价
Science and Technology Strategy Evaluation Based on Entropy Fuzzy AHP
计算机科学, 2020, 47(6A): 1-5. https://doi.org/10.11896/JsJkx.190700078
[6] 刘俊琦, 李智, 张学阳.
基于信息熵和残差神经网络的多层次船只目标鉴别方法
Multi-level Ship Target Discrimination Method Based on Entropy and Residual Neural Network
计算机科学, 2020, 47(11A): 253-257. https://doi.org/10.11896/jsjkx.191100006
[7] 张晶晶, 张爱华, 纪海峰.
基于小波与分形相结合的图像压缩编码
Image Compression Encoding Based on Wavelet Transform and Fractal
计算机科学, 2019, 46(8): 310-314. https://doi.org/10.11896/j.issn.1002-137X.2019.08.051
[8] 张昉, 赵书良, 武永亮.
面向多尺度数据挖掘的数据尺度划分方法
Data Scaling Method for Multi-scale Data Mining
计算机科学, 2019, 46(4): 57-65. https://doi.org/10.11896/j.issn.1002-137X.2019.04.009
[9] 朱佩佩, 龙敏.
基于用户间接信任及高斯填充的推荐算法
Recommendation Methods Considering User Indirect Trust and Gaussian Filling
计算机科学, 2019, 46(11A): 178-184.
[10] 郑书富,余高锋.
基于形式背景的属性转移与知识发现
Attribute Transfer and Knowledge Discovery Based on Formal Context
计算机科学, 2018, 45(6A): 117-119.
[11] 邹娜, 田金文.
多特征融合红外舰船尾流检测方法研究
Research on Multi Feature Fusion Infrared Ship Wake Detection
计算机科学, 2018, 45(11A): 172-175.
[12] 王锋, 刘吉超, 魏巍.
基于信息熵的半监督特征选择算法
Semi-supervised Feature Selection Algorithm Based on Information Entropy
计算机科学, 2018, 45(11A): 427-430.
[13] 袁小艳,王安志,潘刚,王明辉.
多尺度下幅度谱与相位谱相融合的视觉注意建模
Visual Attention Modeling Based on Multi-scale Fusion of Amplitude Spectrum and Phase Spectrum
计算机科学, 2017, 44(7): 293-298. https://doi.org/10.11896/j.issn.1002-137X.2017.07.053
[14] 冯飞,刘培学,李晓燕,严楠彬.
离散余弦变换在图像压缩算法中的研究
Research of Discrete Cosine Transform for Image Compression Algorithm
计算机科学, 2016, 43(Z11): 240-241. https://doi.org/10.11896/j.issn.1002-137X.2016.11A.055
[15] 王海燕,殷俊,潘显萌.
基于Surfacelet变换和SPIHT算法的视频压缩
Video Compression Based on Surfacelet Transform and SPIHT Algorithm
计算机科学, 2016, 43(Z11): 237-239. https://doi.org/10.11896/j.issn.1002-137X.2016.11A.054
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!