Monte Carlo Optimization of a Combined Image Quality Assessment for Compressed Images Evaluation
- 30 April 2021
- journal article
- research article
- Published by International Information and Engineering Technology Association in Traitement du Signal
- Vol. 38 (2), 281-289
- https://doi.org/10.18280/ts.380204
Abstract
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.Keywords
Funding Information
- DGRSDT of the Algerian Ministry of Higher Education and Research
- University Research-Training Projects (PRFU) (A25N01UN080120180002)
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