A comparative study of recent improvements in wavelet-based image coding schemes

Boujelbene, Rania; Jemâa, Yousra Ben; Zribi, Mourad

doi:10.1007/s11042-018-6262-4

Cited by 19 publications

(18 citation statements)

References 52 publications

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“…Even if more recent coding/compression schemes may be employed for the same goal, e.g., Set Partitioning in Hierarchical Trees (SPIHT), Set Partitioned Embedded Block (SPECK), and Embedded Block Coding with Optimized Truncation (EBCOT), the simplicity of EZW makes it more suitable to appreciate the approximation abilities of the different types of wavelets. Furthermore, it has been shown that by properly modifying the EZW algorithm, it can reach similar, or even superior, performance with respect to the other techniques [44].…”

Section: First Application: Compressionmentioning

confidence: 99%

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.

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Section: First Application: Compressionmentioning

confidence: 99%

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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“…By the use of filters bank (High and Low pass), the image can be decomposed into four sub-bands namely LL (low-low band), HL (horizontal), LH (vertical), and HH (diagonal). So that, the result of multiresolution decomposition is a simultaneous image representation on various resolution levels; a low-resolution image or approximation and a higherresolution image or details, Figure 1 [15]. This will generate the hierarchical pyramidal structure.…”

Section: Wavelet-based Image Compressionmentioning

confidence: 99%

Quality and texture analysis of biometric images compressed with second-generation wavelet transforms and SPIHT-Z encoder

Bouida

Beladgham

Bassou

et al. 2020

IJEECS

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<span>In biometric systems, compression takes important place especially in order to reduce the size of the information stored or transmitted through the distributed biometric systems. It is also noted that the compression techniques induce loss of information in the compressed images that can affect the effectiveness of biometric systems. The main objective of our contribution is to examine the efficacy of the used method to offer an optimal compression quality in these kind of images without considerable distortion. In order to evaluate the efficacy of the compression process, we use two kinds of evaluation, full-reference image quality assessment and a new proposed textural quality analysis of the compressed images. In this paper, we use a second-generation wavelet transform to improve the compression study in biometric images. The basic idea of this algorithm is the quincunx wavelet transform coupled to a modified progressive encoder called SPIHT-Z encoding.</span>

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“…Unless the JPEG standards that use the discrete cosine transform and the JPEG2000 operate with the wavelet transform, most techniques proposed in the literature attempt to present and use other combinations of algorithms to optimize and minimize compression-induced degradation by having high compression ratios. Most of these techniques use Wavelet transforms of one kind or another [3,4]. The compression algorithms that reduce the image size induce a modification and a distortion in the present data in these images.…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo Optimization of a Combined Image Quality Assessment for Compressed Images Evaluation

Bouida¹,

Khelifi²,

Beladgham³

et al. 2021

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In image processing, using compression is very important in various applications, especially those using data quantities in transmission and storing. This importance becomes most required with the evolution of image quantities and the big data systems explosion. The image compression allows reducing the required binary volume of image data by encoding the image for transmission goal or database saving. The principal problem with image compression when reducing its size is the degradation that enters the image. This degradation can affect the quality of use of the compressed image. To evaluate and qualify this quality, we investigate the use of textural combined image quality metrics (TCQ) based on the fusion of full reference structural, textural, and edge evaluation metrics. To optimize this metric, we use the Monte Carlo optimization method. This approach allows us to qualify our compressed images and propose the best metric that evaluates compressed images according to several textural quality aspects.

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A comparative study of recent improvements in wavelet-based image coding schemes

Cited by 19 publications

References 52 publications

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

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

Quality and texture analysis of biometric images compressed with second-generation wavelet transforms and SPIHT-Z encoder

Monte Carlo Optimization of a Combined Image Quality Assessment for Compressed Images Evaluation

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