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

Bouida, Ahmed; Beladgham, Mohammed; Bassou, Abdesselam; Benyahia, Ismahane

doi:10.11591/ijeecs.v19.i3.pp1325-1339

Cited by 6 publications

(9 citation statements)

References 36 publications

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“…•Lifting Quincunx wavelet transform with SPIHT-Z encoder (QWT-SPIHTZ) algorithm used from Bouida et al [52] and Benyahia et al [61].…”

Section: Resultsmentioning

confidence: 99%

“…A recent form of performing a textural analysis, textural image quality was proposed in our previous work [52]. It helps us to calculate the textural image quality by using the elementary (GLCM) texture features like Contrast, Correlation, Energy, Entropy, or Homogeneity between original and compressed images.…”

Section: Figure 1 Classification Of Texture Analysis Techniquesmentioning

confidence: 99%

“…Using this quality parameter helps us to determine the textural degradation according to the texture parameters with the GLCM texture features, more details are presented in the work [52].…”

Section: Figure 1 Classification Of Texture Analysis Techniquesmentioning

confidence: 99%

See 2 more Smart Citations

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

Bouida¹,

Beladgham²,

Bassou³

et al. 2020

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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.

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“…•Lifting Quincunx wavelet transform with SPIHT-Z encoder (QWT-SPIHTZ) algorithm used from Bouida et al [52] and Benyahia et al [61].…”

Section: Resultsmentioning

confidence: 99%

Section: Figure 1 Classification Of Texture Analysis Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

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

Bouida¹,

Beladgham²,

Bassou³

et al. 2020

Self Cite

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show abstract

“…In a second way, and to understand the compressed image textural quality, we use a proposed image quality that uses the Gray Level Co-Occurrence Matrix (GLCM) textural features [31]. This parameter estimates properties of images relating to second-order statistics measuring the probability of appearance of pairs of pixel values located at a distance in the image using Contrast, Correlation, Energy, Entropy, and Homogeneity.…”

Section: Metric Fusion Conceptmentioning

confidence: 99%

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

Bouida¹,

Khelifi²,

Beladgham³

et al. 2021

Self Cite

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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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“…Texture can obtain realistic details without increasing the complexity of geometric model. Since it was proposed, it has been widely studied and applied [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Texture Image Compression Algorithm Based on Self-Organizing Neural Network

Han¹

2022

Computational Intelligence and Neuroscience

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With the rapid development of science and technology, human beings have gradually stepped into a brand-new digital era. Virtual reality technology has brought people an immersive experience. In order to enable users to get a better virtual reality experience, the pictures produced by virtual skillfully must be realistic enough and support users' real-time interaction. So interactive real-time photorealistic rendering becomes the focus of research. Texture mapping is a technology proposed to solve the contradiction between real time and reality. It has been widely studied and used since it was proposed. However, due to limited bandwidth and memory storage, it brings challenges to the stain dyeing of many large texture images, so texture compression is introduced. Texture compression can improve the utilization rate of cache but also greatly reduce the pressure on data transmission caused by the system, which largely solves the problem of real-time rendering of realistic graphics. Due to the particularity of texture image compression, it is necessary to consider not only the quality of texture image after compression ratio and decompression but also whether the algorithm is compatible with mainstream graphics cards. On this basis, we put forward the texture image compression method based on self-organizing mapping, the experiment results show that our method has achieved good results, and it is superior to other methods in most performance indexes.

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

Cited by 6 publications

References 36 publications

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

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

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

Texture Image Compression Algorithm Based on Self-Organizing Neural Network

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