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

2020 ◽  
Vol 19 (3) ◽  
pp. 1325
Author(s):  
Ahmed Bouida ◽  
Mohammed Beladgham ◽  
Abdesselam Bassou ◽  
Ismahane Benyahia
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Second Generation ◽  
Image Quality Assessment ◽  
Quality Analysis ◽  
Reference Image ◽  
Important Place ◽  
Compressed Images ◽  
Biometric Systems ◽  
Second Generation Wavelet

<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>

scholarly journals Evaluation of Textural Degradation in Compressed Medical and Biometric Images by Analyzing Image Texture Features and Edges

2020 ◽  
Vol 37 (5) ◽  
pp. 753-762
Author(s):  
Ahmed Bouida ◽  
Mohammed Beladgham ◽  
Abdesselam Bassou ◽  
Ismahane Benyahia ◽  
Abdelmalek Ahmed-Taleb ◽  
...  
Keyword(s):  
Image Quality ◽  
Image Compression ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Texture Features ◽  
Image Texture ◽  
Compression Process ◽  
Compressed Images ◽  
Biometric Systems ◽  
Second Generation Wavelet

The importance of image compression is now essential during transmission or storage processes in various data applications, especially in medical and biometric systems. To perform the effectiveness of the compression process on images and evaluate degradation caused by this process, image quality assessment becomes an important tool in image services. We note that the objective criteria in image quality depend especially on the image type and image texture composition. The actual tendency is to find metrics making better qualification on errors in compressed images and correlate with the human visual system. This paper presents an investigation to examine and evaluate image compression degradation by the use of a new tendency concept of image quality assessment based on texture and edge analysis. To perform and practice this evaluation, we compress the medical and biometric images using second-generation wavelet compression algorithms and study the degradation of texture information in these images.

Nonlinear Enhancement Algorithm for Infrared Image Based on Second Generation Wavelet Transform

2009 ◽  
Vol 29 (2) ◽  
pp. 353-356 ◽  
Author(s):  
秦翰林 Qin Hanlin ◽  
周慧鑫 Zhou Huixin ◽  
刘上乾 Liu Shangqian ◽  
卢泉 Lu Quan
Keyword(s):  
Wavelet Transform ◽  
Second Generation ◽  
Infrared Image ◽  
Second Generation Wavelet Transform ◽  
Second Generation Wavelet ◽  
Enhancement Algorithm

scholarly journals Quaternion wavelet transform based full reference image quality assessment for multiply distorted images

PLoS ONE ◽  
2018 ◽  
Vol 13 (6) ◽  
pp. e0199430 ◽  
Author(s):  
Chaofeng Li ◽  
Yifan Li ◽  
Yunhao Yuan ◽  
Xiaojun Wu ◽  
Qingbing Sang
Keyword(s):  
Wavelet Transform ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Reference Image ◽  
Full Reference ◽  
Quaternion Wavelet Transform

Mechanical faulty signal denoising using a redundant non-linear second-generation wavelet transform

2011 ◽  
Vol 225 (4) ◽  
pp. 799-808 ◽  
Author(s):  
N Li ◽  
R Zhou ◽  
X Z Zhao
Keyword(s):  
Wavelet Transform ◽  
Second Generation ◽  
Selection Criterion ◽  
Signal Denoising ◽  
Mechanical Equipment ◽  
Denoising Method ◽  
Second Generation Wavelet Transform ◽  
Non Linear ◽  
Early Fault Detection ◽  
Second Generation Wavelet

Denoising and extraction of the weak signals are crucial to mechanical equipment fault diagnostics, especially for early fault detection, in which cases fault features are very weak and masked by the noise. The wavelet transform has been widely used in mechanical faulty signal denoising due to its extraordinary timefrequency representation capability. However, the mechanical faulty signals are often non-stationary, with the structure varying significantly within each scale. Because a single wavelet filter cannot mimic the signal structure of an entire scale, the traditional wavelet-based signal denoising method cannot achieve an ideal effect, and even worse some faulty information of the raw signal may be lost in the denoising process. To overcome this deficiency, a novel mechanical faulty signal denoising method using a redundant non-linear second generation wavelet transform is proposed. In this method, an optimal prediction operator is selected for each transforming sample according to the selection criterion of minimizing each individual prediction error. Consequently, the selected predictor can always fit the local characteristics of the signals. The signal denoising results from both simulated signals and experimental data are presented and both support the proposed method.

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