适于硬件实现的无损图像压缩

王建军; 刘波

doi:10.3788/OPE.20111904.0922

您当前的位置：

首页 >

文章列表页 >

适于硬件实现的无损图像压缩

信息科学 | 更新时间：2020-08-12

- 适于硬件实现的无损图像压缩
- Hardware implementation of lossless image compression
- 光学精密工程 2011年19卷第4期页码：922-928
- 作者机构：
  
  1. 中国人民武装警察部队工程学院,陕西西安,710086
  2. 中国科学院空间科学与应用研究中心, 北京 1001909
- 作者简介：
- 基金信息：
  
  国家863高技术研究发展计划资助项目(No.2007AA12Z127)();中国科学院空间科学与应用研究中心青年创新基金资助项目(No.O8211DA29S)()
- DOI：10.3788/OPE.20111904.0922
  中图分类号： TP391.4
- 收稿日期：2010-06-11，
  
  修回日期：2010-09-03，
  
  网络出版日期：2011-04-26，
  
  纸质出版日期：2011-04-26
- 稿件说明：
移动端阅览
王建军, 刘波. 适于硬件实现的无损图像压缩[J]. 光学精密工程, 2011,19(4): 922-928

WANG Jian-jun, LIU Bo. Hardware implementation of lossless image compression[J]. Editorial Office of Optics and Precision Engineering, 2011,19(4): 922-928
王建军, 刘波. 适于硬件实现的无损图像压缩[J]. 光学精密工程, 2011,19(4): 922-928 DOI： 10.3788/OPE.20111904.0922.

WANG Jian-jun, LIU Bo. Hardware implementation of lossless image compression[J]. Editorial Office of Optics and Precision Engineering, 2011,19(4): 922-928 DOI： 10.3788/OPE.20111904.0922.

摘要

针对常见嵌入式小波编码算法硬件实现困难、成本较高等问题

提出了一种适用于硬件实现的无损图像压缩算法。该算法根据子带属性的不同将小波系数分为1个低频子块和3个高频子块

然后使用不同的方法分别进行量化编码。对于低频子块

首先使用脉冲差分编码调制(DPCM)方法压缩其数据动态

然后使用改进的比特位平面编码算法编码输出对应码流;对于各高频子块

则使用提出的改进集合树分裂(SPIHT)算法分别进行量化编码。在改进的SPIHT算法中

通过加入A类集合的分类优化了码流输出;通过消除链表

降低了内存需求并避免了内存的动态管理;通过使用集合极值矩阵

避免了扫描过程中的重复判断

提高了编码效率。实验结果表明

与传统SPIHT算法相比

本文算法可使各国际标准测试图像的编码比特率均降低0.14 bit/pixel以上

而编码速度提高3倍以上。该算法具有实时性高、内存需求低、适于硬件实现的特点。

Abstract

A hardware implementation method for lossless image compression is proposed to overcome the difficulties of embedded wavelet coding methods in hardware implementation and high costs. Firstly

the algorithm divides wavelet coefficients into a low frequency block and three high frequency blocks according to sub-band properties

and then uses different methods to code respectively. In the low frequency block coding method

the Difference Pulse coding Modulation(DPCM) is firstly used to reduce coefficients' dynamic range. Then

a modified bit plane coding method is used to output the bit stream. In the high frequency block coding method

the proposed modified Set Partitioning in Hierarchical trees(MSPIHT) algorithm is used to code three high frequency blocks respectively with their thresholds.The MSPIHT optimizes the outputted bit stream by using a type of A set judge

reduces memory requirement and avoids memory dynamic management by eliminating the lists of SPIHT algorithm.Moreover

the MSPIHT avoids repeated calculation in scanning process and enhances the coding efficiency by adopting MMVS. Experiment results show that the bit-rates of all international standard testing images have reduced more than 1.4 bit/pixel and the coding speed has increased more than three times as compared with that SPHIT. It is concludes that the proposed algorithm is super in real-time performance

low memory requirement and fit for hardware implementation.

关键词

Keywords

references

PAN H, SIU W C, LAW N F. A fast and low memory image coding algorithm based on lifting wavelet transform and modified SPIHT [J]. Signal Processing: Image Communication, 2008,23(1):146-161.[2] JYOTHESWAR J, MAHAPATRA S. Efficient FPGA implementation of DWT and modified SPIHT for lossless image compression [J]. Journal of Systems Architecture, 2007,53(4):369378.[3] KAVITHA S, MOHAMMED S, ROOMI M, et al.. Lossless compression through segmentation on low depth-of-field images [J]. Digital Signal Processing, 2009,19(1):59-65.[4] ADAMS M D, KOSSENTINI F. Lossless image compression with projection-based and adaptive reversible integer wavelet transforms [J]. IEEE Trans. Image Process, 2003,12(5):489-499.[5] SHAPIRO J M. Embedded image coding using zero-trees of wavelet coefficients [J]. IEEE Trans. Signal Process, 1993, 41(12):3445-3462.[6] SAID A, PEARLMAN W A. A new fast and efficient image codec based on set partitioning in hierarchical trees[J]. IEEE Trans. Circ. Syst. Video Technol, 1996,6(3):243-250.[7] TAUBMAN D. High performance scalable image compression with EBCOT [J]. IEEE Trans. Image Processing, 2000,9(7):1158-1170.[8] 郝燕玲,刘营. 应用于JPEG2000的离散小波变换并行超大规模集成结构[J]. 光学精密工程,2009,17(5):1181-1186. HAO Y L, LIU Y. Parallel VLSI architecture of discrete wavelet transform for JPEG2000 [J]. Opt. Precision Eng.,2009,17(5):1181-1186. (in Chinese)[9] 郑启枣, 刘鹏. 基于线性预测的动态阈值JPEG2000码率控制算法[J]. 浙江大学学报(工学版), 2008, 42(8):1135-1140. ZHENG Q Z, LIU P. Dynamic threshold JPEG2000 rate control algorithm based on linear prediction [J]. Journal of Zhejiang University (Engineering Science), 2008,42(8):1135-1140.(in Chinese)[10] 徐勇,徐智勇,张启衡,等. 适于硬件实现的低复杂度图像压缩[J]. 光学精密工程,2009,17(9):2262-2267. XU Y, XU ZH Y, ZHANG Q H, et al... Low complexity image compression scheme for hardware implementation [J]. Opt. Precision Eng., 2009,17(9):2262-2267. (in Chinese)[11] BRAHIMI T, MELIT A, KHELIFIB F. An improved SPIHT algorithm for lossless image coding [J]. Digital Signal Processing, 2009,19(7):220228.[12] 孙蕾,罗建书,谷德峰. 基于谱间预测和码流预分配的高光谱图像压缩算法[J]. 光学精密工程,2008,16(4):752-757. SUN L, LUO J SH, GU D F. Hyperspectral image compression algorithm based on prediction between bands and rate pre-allocation [J]. Opt. Precision Eng., 2008,16(4):752-757. (in Chinese)

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

无人机侦察图像压缩

基于自适应指数哥伦布编码的图像压缩算法

精确质量控制的遥感图像JPEG2000压缩方法

基于快速方向预测的高分辨率遥感影像压缩

基于内容的高光谱图像无损压缩