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.

Blind Assessment of Wavelet-Compressed Images Based on Subband Statistics of Natural Scenes

2018 ◽  
pp. 1322-1337
Author(s):  
Yingchun Guo ◽  
Gang Yan ◽  
Cuihong Xue ◽  
Yang Yu
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Correlation Coefficient ◽  
Image Quality Assessment ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Subjective Assessment ◽  
Natural Scenes ◽  
Assessment Scores ◽  
Compressed Images

This paper presents a no-reference image quality assessment metric that makes use of the wavelet subband statistics to evaluate the levels of distortions of wavelet-compressed images. The work is based on the fact that for distorted images the correlation coefficients of the adjacent scale subbands change proportionally with respect to the distortion of a compressed image. Subband similarity is used in this work to measure the correlations of the adjacent scale subbands of the same wavelet orientations. The higher the image quality is (i.e., less distortion), the greater the cosine similarity coefficient will be. Statistical analysis is applied to analyze the performance of the metric by evaluating the relationship between the human subjective assessment scores and the subband cosine similarities. Experimental results show that the proposed blind method for the quality assessment of wavelet-compressed images has sufficient prediction accuracy (high Pearson Correlation Coefficient, PCCs), sufficient prediction monotonicity (high Spearman Correlation Coefficient SCCs) and sufficient prediction consistency (low outlier ratios) and less running time. It is simple to calculate, has a clear physical meaning, and has a stable performance for the four image databases on which the method was tested.

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

2021 ◽  
Vol 38 (2) ◽  
pp. 281-289
Author(s):  
Ahmed Bouida ◽  
Mustapha Khelifi ◽  
Mohammed Beladgham ◽  
Fatima-Zohra Hamlili
Keyword(s):  
Monte Carlo ◽  
Image Quality ◽  
Image Compression ◽  
Image Quality Assessment ◽  
Image Data ◽  
Optimization Method ◽  
Data Systems ◽  
Monte Carlo Optimization ◽  
Compressed Images ◽  
Quality Aspects

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.

scholarly journals Image Quality Assessment for Different Wavelet Compression Techniques in a Visual Communication Framework

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Nuha A. S. Alwan ◽  
Zahir M. Hussain
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Communication Channel ◽  
Image Quality Assessment ◽  
Additive White Gaussian Noise ◽  
Structural Similarity ◽  
Wavelet Compression ◽  
Communication Framework ◽  
Compressed Images ◽  
Qam Modulation

Images with subband coding and threshold wavelet compression are transmitted over a Rayleigh communication channel with additive white Gaussian noise (AWGN), after quantization and 16-QAM modulation. A comparison is made between these two types of compression using both mean square error (MSE) and structural similarity (SSIM) image quality assessment (IQA) criteria applied to the reconstructed image at the receiver. The two methods yielded comparable SSIM but different MSE measures. In this work, we justify our results which support previous findings in the literature that the MSE between two images is not indicative of structural similarity or the visibility of errors. It is found that it is difficult to reduce the pointwise errors in subband-compressed images (higher MSE). However, the compressed images provide comparable SSIM or perceived quality for both types of compression provided that the retained energy after compression is the same.

FFDM image quality assessment using computerized image texture analysis

2010 ◽  
Author(s):  
Rachelle Berger ◽  
Ann-Katherine Carton ◽  
Andrew D. A. Maidment ◽  
Despina Kontos
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Texture Analysis ◽  
Image Quality Assessment ◽  
Image Texture ◽  
Image Texture Analysis

scholarly journals An Ingenious Application-Specific Quality Assessment Methods for Compressed Wireless Capsule Endoscopy Images

2020 ◽  
Vol 4 (1) ◽  
pp. 18
Author(s):  
Kinde Anlay Fante ◽  
Fetulhak Abdurahman ◽  
Mulugeta Tegegn Gemeda
Keyword(s):  
Image Quality ◽  
Image Compression ◽  
Quality Assessment ◽  
Capsule Endoscopy ◽  
Image Quality Assessment ◽  
Wireless Capsule Endoscopy ◽  
Assessment Methods ◽  
Reference Image ◽  
Assessment Techniques ◽  
Wireless Capsule

<p>Image quality assessment methods are used in different image processing applications. Among them, image compression and image super-resolution can be mentioned in wireless capsule endoscopy (WCE) applications. The existing image compression algorithms for WCE employ the generalpurpose image quality assessment (IQA) methods to evaluate the quality of the compressed image. Due to the specific nature of the images captured by WCE, the general-purpose IQA methods are not optimal and give less correlated results to that of subjective IQA (visual perception). This paper presents improved image quality assessment techniques for wireless capsule endoscopy applications. The proposed objective IQA methods are obtained by modifying the existing full-reference image quality assessment techniques. The modification is done by excluding the noninformative regions, in endoscopic images, in the computation of IQA metrics. The experimental results demonstrate that the proposed IQA method gives an improved peak signal-tonoise ratio (PSNR) and structural similarity index (SSIM). The proposed image quality assessment methods are more reliable for compressed endoscopic capsule images.</p>

Sign in / Sign up

Export Citation Format

Share Document