Optical image compression based on adaptive directional prediction discrete wavelet transform
Optical image compression based on adaptive directional prediction discrete wavelet transform
复制标题
基于自适应方向预测离散小波变换的光学图像压缩
DOI：
10.1007/s10043-013-0081-7
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发表时间：
2013-11
期刊：
影响因子：
1.2
通讯作者：
Qiu, Bingchang
中科院分区：
文献类型：
--
作者：
Zhang, Libao;Qiu, Bingchang
传统的提升小波变换不能有效地重构图像的非水平和非垂直高频信息。提出了一种基于自适应方向预测离散小波变换（ADP-DWT）的图像压缩方法。我们首先设计一个方向预测模型来获得提升小波的最佳变换方向。然后，我们执行方向提升变换沿着的最佳变换方向。在高频子带的非水平和非垂直方向上，边缘和纹理能量可以被降低。最后，对小波系数进行分层树集合分割（SPIHT）编码。该方法兼有自适应方向提升（ADL）和方向自适应离散小波变换（DA-DWT）的优点，且计算复杂度远低于这两种方法。对于纹理或边缘规则、精细的图像，ADP-DWT的编码精度优于ADL和DA-DWT。
The traditional lifting wavelet transform cannot effectively reconstruct the nonhorizontal and nonvertical high-frequency information of an image. In this paper, we present a new image compression method based on adaptive directional prediction discrete wavelet transform (ADP-DWT). We first design a directional prediction model to obtain the optimal transform direction of the lifting wavelet. Then, we execute the directional lifting transform along the optimal transform direction. The edge and texture energy can be reduced in the nonhorizontal and nonvertical directions of the high-frequency sub-bands. Finally, the wavelet coefficients are coded with the set partitioning in hierarchical trees (SPIHT) algorithm. The new method holds the advantages of both adaptive directional lifting (ADL) and direction-adaptive discrete wavelet transform (DA-DWT), and the computational complexity is far lower than that in these methods. For the images containing regular and fine textures or edges, the coding preformance of ADP-DWT is better than that of ADL and DA-DWT.
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DOI：
10.1109/83.869176
发表时间：
2000-10
期刊：
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
影响因子：
--
作者：
Ö. N. Gerek;A. Cetin
通讯作者：
Ö. N. Gerek;A. Cetin
DOI：
10.1007/978-3-030-12931-6_4
发表时间：
2019-05
期刊：
Atomic Force Microscopy
影响因子：
--
作者：
Wesley C. Sanders
通讯作者：
Wesley C. Sanders
影响因子：
1.3
作者：
D. Taubman;M. Marcellin
通讯作者：
D. Taubman;M. Marcellin
影响因子：
10.6
作者：
Chang, Chuo-Ling;Girod, Bernd
通讯作者：
Girod, Bernd
影响因子：
10.6
作者：
Ding, Wenpeng;Wu, Feng;Li, Houqiang
通讯作者：
Li, Houqiang