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

作     者：Bouida, Ahmed Khelifi, Mustapha Beladgham, Mohammed Hamlili, Fatima-Zohra 

作者机构：Univ TAHRI Mohammed Bechar Informat Proc & Telecommun Lab LTIT Bechar 08000 Algeria 

出 版 物：《TRAITEMENT DU SIGNAL》 (Trait. Signal)

年 卷 期：2021年第38卷第2期

页      面：281-289页

核心收录：

学科分类：0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：DGRSDT of the Algerian Ministry of Higher Education and Research University Research-Training Projects (PRFU) [A25N01UN080120180002] 

主　　题：image quality assessment combined FR-IQA texture analysis edge evaluation image wavelet compression 

摘      要：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 theMonte 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.