On zerotree quantization for embedded wavelet packet image coding

Rajpoot, Nasir M.; Meyer, F.G.; Wilson, Roland; Coifman, Ronald R.

doi:10.1109/icip.1999.822901

Cited by 12 publications

(5 citation statements)

References 9 publications

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“…However, it is difficult to define the parent-child relationships for the diverse structure of WP subbands. Furthermore, even if one can define the hierarchical tree structure for arbitrary WP decompositions, as Meyer et al [11,16] did in their work, the possibility of a tree being insignificant will be reduced further by the fact that WP transform can magnify the dynamic range of high frequency coefficients. Rajpoot and Meyer [16] also observed that the occurrence probability of zerotree in WP decomposition is much lower than that in wavelet decomposition, which explains why their compatible zerotree quantization scheme for WP subbands is not efficient.…”

Section: Comparison Of the Set-partitioning Schemesmentioning

confidence: 95%

“…These constrained basis selection methods may result in a suboptimal WP decomposition that influences the overall coding efficiency. Rajpoot and Meyer et al [11,16] presented a solution to the conflict problem by making great efforts to define and testify the parent-child relationships for arbitrary WP structures. In their work, a set of rules was made to define a zerotree structure, which was termed as compatible zerotree.…”

Section: Introductionmentioning

confidence: 99%

“…However, as shown in Figure 1, one has to face the difficulty in defining the parent-offspring relationships, which naturally occur in the octave band decomposition but not with arbitrary WP structures. Specifically, the diverse packet structures may cause the problem of parenting conflict [11] , which means that multiple coefficients declare to be the parent of one child coefficient simultaneously. Cho and Ra [12] solved this problem by merging the decomposed four subbands back into one subband, as if the packet decomposition was not made.…”

Section: Introductionmentioning

confidence: 99%

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A wavelet packet based block-partitioning image coding algorithm with rate-distortion optimization

Yang

2008

Sci. China Ser. F-Inf. Sci.

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As an elegant generalization of wavelet transform, wavelet packet (WP) provides an effective representation tool for adaptive waveform analysis. Recent work shows that image-coding methods based on WP decomposition can achieve significant gain over those based on a usual wavelet transform. However, most of the work adopts a tree-structured quantization scheme, which is a successful technique for wavelet image coding, but not appropriate for WP subbands. This paper presents an image-coding algorithm based on a rate-distortion optimized wavelet packet decomposition and on an intraband block-partitioning scheme. By encoding each WP subband separately with the block-partitioning algorithm and the JPEG2000 context modeling, the proposed algorithm naturally avoids the difficulty in defining parent-offspring relationships for the WP coefficients, which has to be faced when adopting the tree-structured quantization scheme. The experimental results show that the proposed algorithm significantly outperforms SPIHT and JPEG2000 schemes and also surpasses state-of-the-art WP image coding algorithms, in terms of both PSNR and visual quality. basis selection, bitplane coding, context modeling, set partition, wavelet packet (WP)

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Section: Comparison Of the Set-partitioning Schemesmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A wavelet packet based block-partitioning image coding algorithm with rate-distortion optimization

Yang

2008

Sci. China Ser. F-Inf. Sci.

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“…In other words, the coefficients of a salient feature in the image will only appear at a single scale that characterizes the feature. Another technical difficulty [21] comes from the fact that we usually cannot define the "parent" subband in a general wavelet packet tree. Because of all these issues, we limit the context to be a spatial context.…”

Section: B Ordering Of the Coefficients And Entropy Codingmentioning

confidence: 99%

Fast adaptive wavelet packet image compression

Meyer

Averbuch

Strömberg

2000

IEEE Trans. on Image Process.

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118

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Wavelets are ill-suited to represent oscillatory patterns: rapid variations of intensity can only be described by the small scale wavelet coefficients, which are often quantized to zero, even at high bit rates. Our goal is to provide a fast numerical implementation of the best wavelet packet algorithm in order to demonstrate that an advantage can be gained by constructing a basis adapted to a target image. Emphasis is placed on developing algorithms that are computationally efficient. We developed a new fast two-dimensional (2-D) convolution decimation algorithm with factorized nonseparable 2-D filters. The algorithm is four times faster than a standard convolution-decimation. An extensive evaluation of the algorithm was performed on a large class of textured images. Because of its ability to reproduce textures so well, the wavelet packet coder significantly out performs one of the best wavelet coder on images such as Barbara and fingerprints, both visually and in term of PSNR.

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“…LSP 过程对于 LSP (在以前位平面已经重要的系数)中的每一个像素注意不包括由上两个过程在本次位平面刚加入的像素, 执行: 行定义, 如Meyer等人 [11,16] 所作的那样, 一棵树是不重要集合的概率也会因为小波包变换会放大高频系数的幅值范围而进一步降低. Rajpoot和Meyer [16]…”

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一个基于小波包分解的率失真最优化块分割图像编码算法

杨¹,

许²

2008

SSIF

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On zerotree quantization for embedded wavelet packet image coding

Cited by 12 publications

References 9 publications

A wavelet packet based block-partitioning image coding algorithm with rate-distortion optimization

A wavelet packet based block-partitioning image coding algorithm with rate-distortion optimization

Fast adaptive wavelet packet image compression

一个基于小波包分解的率失真最优化块分割图像编码算法

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