无损压缩中基于块的梯度预测模式

doi:10.3969/j.issn.1000-3428.2013.06.064

计算机工程 ›› 2013, Vol. 39 ›› Issue (6): 290-294.doi: 10.3969/j.issn.1000-3428.2013.06.064

无损压缩中基于块的梯度预测模式

王春洁^1,2，沈燕飞^2,3，卢毓海^1,2，朱珍民^2,3，周应权¹

(1. 湘潭大学信息工程学院，湖南湘潭 411105；2. 中国科学院计算技术研究所，北京 100190； 3. 移动计算与新型终端北京市重点实验室，北京 100190)

收稿日期:2012-07-18 出版日期:2013-06-15 发布日期:2013-06-14
作者简介:王春洁(1986－)，女，硕士、CCF会员，主研方向：多媒体技术，视频编码；沈燕飞，助理研究员、博士；卢毓海，硕士；朱珍民，正研级高级工程师；周应权，副教授
基金项目:
国家自然科学基金资助项目(61001123)

Gradient-adjusted Prediction Mode of Lossless Compression Based on Block

WANG Chun-jie ^1,2, SHEN Yan-fei ^2,3, LU Yu-hai ^1,2, ZHU Zhen-min ^2,3, ZHOU Ying-quan¹

(1. College of Information Engineering, Xiangtan University, Xiangtan 411105, China; 2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; 3. Key Laboratory of Mobile Computing and Pervasive Device of Beijing, Beijing 100190, China)

Received:2012-07-18 Online:2013-06-15 Published:2013-06-14

摘要/Abstract

摘要： 为应对自然图像纹理属性的多方向性要求，提出一种基于块的梯度预测模式。对不同的块采用水平、垂直、左斜以及右斜4种扫描方式，并根据图像纹理属性自适应选择扫描方式。块中每个像素则通过加权不同扫描方式下的相邻像素预测得到。实验结果表明，与CALIC单一水平扫描方式的梯度预测方法相比，该方法能有效提高预测精度，且预测残差的零阶熵可降低3%~12%。

关键词: 梯度预测, 扫描模式, 压缩, 纹理属性, 无损编码

Abstract: For multi-directional texture properties of natural images, this paper proposes a new Gradient-adjusted Prediction (GAP) method of lossless compression based on block. For each block, four scan modes are adopted including horizontal vertical, right down and left down direction and the scan mode is selected by an adaptive coding scheme based on the texture property of the image. Each pixel within a block is predicted by a weighted sum of its different neighbor pixels provided by different scan mode. Experimental results show that the suggested method can make higher accurate prediction and have a significant reduction of zero-order entropy in 3%~12%, as compared to CALIC with only one horizontal scan mode.

Key words: Gradient-adjusted Prediction(GAP), scan mode, compression, texture property, lossless coding

中图分类号:

TP391.41

王春洁, 沈燕飞, 卢毓海, 朱珍民, 周应权. 无损压缩中基于块的梯度预测模式[J]. 计算机工程, 2013, 39(6): 290-294.

WANG Chun-Ji, CHEN Yan-Fei, LEI Yu-Hai, SHU Zhen-Min, ZHOU Ying-Quan. Gradient-adjusted Prediction Mode of Lossless Compression Based on Block[J]. Computer Engineering, 2013, 39(6): 290-294.

参考文献

[1] Jackson D J, Hannah S J. Comparative Analysis of Image Com- pression Techniques[C]//Proc. of the 25th South-eastern Sym- posium on System Theory. Tuscaloosa, Alabama: [s. n.], 1993.
[2] Subramanya A. Image Compression Technique[J]. IEEE Po- tentials, 2001, 20(1): 19-23.
[3] 高文. 多媒体数据压缩技术[M]. 北京: 北京电子工业出版社, 1994.
[4] Wu Xiaolin, Memon N. Context-based, Adaptive, Lossless Image Coding[J]. IEEE Trans. on Communications, 1997, 45(4): 437-444.
[5] Weinberger M J, Seroussi G, Sapiro G. LOCO-I: A Low Com- plexity, Context-based, Lossless Image Compression Algorithm[C]//Proc. of Conference on Data Compression. Washington D. C., USA: IEEE Computer Society, 1996.
[6] ITU-TT.87 ISO/IEC 14495-1-1998 ITU-T SG8, Lossless and Near-lossless Compression of Continuous-tone Still Images[S]. ITU-T Study Group, 1998.
[7] 沈兰荪, 魏海. 图像的无损压缩研究[J]. 数据采集与处理, 1999, 14(4): 485-490.
[8] Miano J. Compressed Image File Formats: JPEG, PNG, GIF, XBM, BMP[M]. New York, USA: ACM Press, 1999.
[9] Tsai Yi-Chen, Lee Ming-Sui, Shen Meiyin, et al. A Quad-tree Decomposition Approach to Cartoon Image Compression[C]// Proc. of the 8th Workshop on Multimedia Signal Processing. [S. l.]: IEEE Press, 2006.
[10] Witten I H, Neal R M. Using Peano Curves for Bilevel Display of Continuous-tone Images[J]. IEEE Computer Graphics and Applications, 1982, 2(3): 47-52.
[11] Nguyen P T, Quinqueton J. Space Filling Curves and Texture Analysis[C]//Proc. of International Conference on Pattern Recognition. Munich, Germany: [s. n.], 1982.
[12] Ansari A, Fineberg A. Image Data Compression and Ordering Using Peano Scan and Lot[J]. IEEE Trans. on Consumer Electronics, 1992, 38(3): 436-445.
[13] Matias Y, Shamir A. A Video Scrambling Technique Based on Space Filling Curves[C]//Proc. of Conference on the Theory and Applications of Cryptographic Techniques on Advances in Cryptology. London, UK: Springer-Verlag, 1987: 398-417.
[14] Ouni T, Lassoued A, Abid M. Adapted Scan Based Lossless Image Compression[C]//Proc. of International Conference on Signal Processing, Image Processing and Pattern Recognition. Jeju Island, Korea: [s. n.], 2011: 82-91.
[15] Ouni T, Lassoued A, Ktari J, et al. Scan Based Lossless Image Compression Scheme[C]//Proc. of the 7th International Conference on Signal-image Technology and Internet-based Systems. Dijon, France: [s. n.], 2011.
编辑顾逸斐

无损压缩中基于块的梯度预测模式

Gradient-adjusted Prediction Mode of Lossless Compression Based on Block

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 10

[1]	刘瑄, 池明旻. 基于多粒度特征蒸馏的遥感图像场景分类研究[J]. 计算机工程, 2021, 47(1): 224-229,238.
[2]	吴涛, 任淑霞, 张书博. 基于三角形子图的复杂网络过滤压缩算法[J]. 计算机工程, 2020, 46(5): 70-77.
[3]	赵鸿图, 李成. 基于马尔科夫链的随机测量矩阵研究[J]. 计算机工程, 2020, 46(4): 241-246.
[4]	倪志文, 马小虎, 孙霄, 边丽娜. 结合显式和隐式特征交互的深度融合模型[J]. 计算机工程, 2020, 46(3): 87-92,98.
[5]	李亚, 张雨楠, 彭程, 杨俊钦, 刘淼. 基于多任务学习的人脸属性识别方法[J]. 计算机工程, 2020, 46(3): 229-236.
[6]	伍伟鑫, 韩京宇, 朱曼. 基于差分编码的RDF分组压缩[J]. 计算机工程, 2020, 46(11): 117-123.
[7]	王豫新, 高美凤. 一种改进的固件增量更新算法[J]. 计算机工程, 2020, 46(10): 210-215.
[8]	付健, 孔芳. 融入结构化信息的端到端中文指代消解[J]. 计算机工程, 2020, 46(1): 45-51.
[9]	何相甫, 范志辉, 王辉, 翟智博, 李静. IPv6与ZigBee互联网关的设计与实现[J]. 计算机工程, 2019, 45(7): 154-158.
[10]	张文涛,许治国,郑霖,杨超. 基于深度自编码多维特征融合的慢动目标检测[J]. 计算机工程, 2019, 45(5): 143-148,154.
[11]	封晨波,覃亚丽,陈辉,常丽萍,薛林林. 基于改进非局部均值的分数阶全变分算法[J]. 计算机工程, 2019, 45(4): 241-247.
[12]	张爱华,胡憬韬,贾彬彬. 基于压缩采样脉搏信号的抗运动干扰心率提取算法[J]. 计算机工程, 2019, 45(4): 302-306,310.
[13]	刘余福,郎文辉,贾光帅. HXDSP平台上矩阵乘法的实现与性能分析[J]. 计算机工程, 2019, 45(4): 25-29.
[14]	王楠,李智,程欣宇,陈怡,罗昊. 基于均值比例与压缩感知的视频双水印算法[J]. 计算机工程, 2019, 45(3): 250-255,261.
[15]	吴大鹏,肖博文,闫俊杰. 基于部分支撑集检测的WBAN数据处理机制[J]. 计算机工程, 2019, 45(3): 148-154.