Journal of Computer Applications ›› 2013, Vol. 33 ›› Issue (06): 1697-1700.DOI: 10.3724/SP.J.1087.2013.01697

• Network and distributed techno • Previous Articles     Next Articles

Lossless image coding method with resolution scalable code-stream

LI Shigao1,QIN Qianqing2   

  1. 1. School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan Hubei 430023, China
    2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan Hubei 430079, China
  • Received:2012-12-28 Revised:2013-02-19 Online:2013-06-01 Published:2013-06-05
  • Contact: LI Shigao

支持分辨率渐进码流的无损图像编码方法

李诗高1,秦前清2   

  1. 1. 武汉工业学院 数学与计算机学院,武汉 430023
    2. 武汉大学 测绘遥感信息工程国家重点实验室,武汉 430079
  • 通讯作者: 李诗高
  • 作者简介:李诗高(1979-),男,湖北监利人,讲师,主要研究方向:图像处理、图像压缩;秦前清(1961-),男,湖北洪湖人,教授,主要研究方向:图像处理。
  • 基金资助:

    国家自然科学基金资助项目(51175135);国家自然科学基金资助项目(51175135)

Abstract: This paper proposed a new decomposition scheme for lossless image compression by incorporating edge-directed adaptive prediction with wavelet lifting scheme. A vertical one-Dimension Discrete Wavelet Transform (1D-DWT) was first applied to images by means of lifting scheme. Second, edge-directed adaptive prediction procedure was applied to those high-frequency sub-band coefficients generated by the previous DWT. And then, a similar horizontal decomposition was performed in the low-frequency sub-band generated by vertical decomposition. A multi-resolution representation was thus acquired by an iterative repetition at the produced low-resolution approximation. Unlike the well-known coder CALIC and JPEG-LS, this scheme can provide a resolution scalable code-stream due to DWT. In addition, the experimental results indicate, due to the edge-directed prediction, this decomposition scheme has achieved noticeably better performance of lossless compression than JPEG2000 which supports resolution scalability.

Key words: lossless image compression, multi-resolution representation, resolution scalability, edge-directed prediction, Discrete Wavelet Transform (DWT)

摘要: 针对无损图像压缩编码,提出了一种新颖的图像分解去相关方法。当前的无损图像编码方法主要有CALIC和JPEG-LS,两者都在空域直接作预测,导致编码码流不具有分辨率可伸缩性。结合小波提升模式与边缘自适应预测研究实现了一种比二维小波变换性能更好的分解方法。首先,对图像的每一列样值进行一维小波分解;然后,对高频子带进行边缘自适应预测,减少残留的信息。针对低频子带图像进行同样的两步操作,就完成了对图像的一次二维分解。对低频图像进行多次迭代操作后即形成了对图像的一个多分辨率分解。实验结果表明,与JPEG2000的无损模式相比,由于边缘自适应预测的引入,提出的分解模式获得了明显的编码增益。

关键词: 无损图像压缩, 多分辨率表示, 分辨率渐进性, 边缘自适应预测, 小波变换

CLC Number: