Data path design and image quality aspects of the next generation multifunctional printer

2008 ◽  
Author(s):  
M. H. H. Brassé ◽  
S. P. R. C. de Smet
Keyword(s):  
Image Quality ◽  
Data Path ◽  
Next Generation ◽  
Path Design ◽  
Quality Aspects

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.

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