Embedded Image Coding Using Zerotrees of Wavelet Coefficients

Shapiro, J.M.

doi:10.1007/0-306-47043-8_8

Cited by 50 publications

(77 citation statements)

References 27 publications

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“…Especially, all test images are decomposed up to level six. Note that wavelet‐based image coding systems often employ five or six decomposition levels; while it has been shown in [10, 14] that there is no noticeable difference in performance between the two levels. As the size and the type of the image affects the coding performance, several image sizes (256 × 256, 512 × 512) and types (natural, medical) have been considered.…”

Section: Resultsmentioning

confidence: 99%

“…For the dominant pass, if the magnitude of a coefficient is larger than a given threshold Th , the node is called significant with respect to the given threshold [14]. Otherwise, the coefficient is insignificant.…”

Section: Novel Enhanced Ezw Image Coding: Proposed Approachmentioning

confidence: 99%

“…Among the existing schemes [10–13], embedded zerotree wavelet (EZW) [14] was one of the first and powerful algorithms based on wavelet image compression. It is a remarkably effective, fast in execution and computationally simple technique for the bit stream of the wavelet‐based image compression.…”

Section: Introductionmentioning

confidence: 99%

“…The main contributions of our proposed approach are highlighted as follows: Increasing the number of coefficients not to be encoded and reducing the total number of symbols by incorporating in our algorithm two steps that aim at eliminating unnecessary symbols. Decreasing the coding bits by the use of the compressor cell operator which aims at coding a consecutive number of symbols by a small number of bits. Indeed, the implementation of the compressor cell is modified as compared to the existing one to adapt it to the new added symbols. Furthermore, the robustness of the proposed NE‐EZW compares advantageously with the EZW [14], IMP1EZW [20], SPIHT [10] and JPEG2000 [22] algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…The modified algorithm IMP1EZW presented in [20] is an efficient image coding method based on the principle of Shapiro's EZW [14]. The difference between these two algorithms lies in the significance test process used for the wavelet coefficients which allows to determine if a significant coefficients has or no significant descendants.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Enhanced embedded zerotree wavelet algorithm for lossy image coding

Boujelbene

Boubchir

Jemâa

2019

IET Image Processing

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Section: Resultsmentioning

confidence: 99%

Section: Novel Enhanced Ezw Image Coding: Proposed Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhanced embedded zerotree wavelet algorithm for lossy image coding

Boujelbene

Boubchir

Jemâa

2019

IET Image Processing

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From Lossy to Lossless Wavelet Image Coding in a Tree-Based Encoder with Resolution Scalability

Oliver

Malumbres

2006

Lecture Notes in Computer Science

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For a lossy encoder, it is important to be able to provide also lossless compression with little or no modification of the usual algorithm, so that an implementation of that algorithm can work in lossy or lossless mode, depending on the specific application, simply by varying the input parameters. In this paper, we evaluate the capability of the Lower Tree Wavelet (LTW) image encoder to work in lossless mode. LTW is a fast and multiresolution wavelet image encoder, which uses trees as a fast mode to group coefficients. In addition, general details on how to implement efficiently (i.e., with only shift and addition/subtraction operations) a reversible integer-to-integer wavelet transform are also given, as a requirement to implement a wavelet-based lossless encoder. Numerical results show that despite being general purpose (i.e., both lossy and lossless) and lacking of complex techniques (such as high-order context and predictive coding), the LTW performs as well as JPEG 2000 in lossless mode, and only 5% below LOCO-I, a specific lossless algorithm.

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A wavelet filter comparison on multiple datasets for signal compression and denoising

Gnutti

Guerrini

Adami

et al. 2021

Multidim Syst Sign Process

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In this paper, we explicitly analyze the performance effects of several orthogonal and bi-orthogonal wavelet families. For each family, we explore the impact of the filter order (length) and the decomposition depth in the multiresolution representation. In particular, two contexts of use are examined: compression and denoising. In both cases, the experiments are carried out on a large dataset of different signal kinds, including various image sets and 1D signals (audio, electrocardiogram and seismic). Results for all the considered wavelets are shown on each dataset. Collectively, the study suggests that a meticulous choice of wavelet parameters significantly alters the performance of the above mentioned tasks. To the best of authors' knowledge, this work represents the most complete analysis and comparison between wavelet filters. Therefore, it represents a valuable benchmark for future works.

show abstract

Embedded Image Coding Using Zerotrees of Wavelet Coefficients

Cited by 50 publications

References 27 publications

Enhanced embedded zerotree wavelet algorithm for lossy image coding

Enhanced embedded zerotree wavelet algorithm for lossy image coding

From Lossy to Lossless Wavelet Image Coding in a Tree-Based Encoder with Resolution Scalability

A wavelet filter comparison on multiple datasets for signal compression and denoising

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