Application of Compromising Evolution in Multi-objective Image Error Concealment

TL;DR

This paper introduces a novel Compromising Evolution method that enhances multi-objective optimization for image error concealment, overcoming limitations of traditional convex approaches in complex image enhancement tasks.

Contribution

The paper proposes a modified genetic algorithm utilizing compromise principles to effectively solve multi-objective image error concealment problems.

Findings

The method successfully solves complex multi-objective optimization in image concealment.

Simulation results demonstrate improved performance over traditional approaches.

The approach effectively handles problems lacking generative models.

Abstract

Numerous multi-objective optimization problems encounter with a number of fitness functions to be simultaneously optimized of which their mutual preferences are not inherently known. Suffering from the lack of underlying generative models, the existing convex optimization approaches may fail to derive the Pareto optimal solution for those problems in complicated domains such as image enhancement. In order to obviate such shortcomings, the Compromising Evolution Method is proposed in this report to modify the Simple Genetic Algorithm by utilizing the notion of compromise. The simulation results show the power of the proposed method solving multi-objective optimizations in a case study of image error concealment.