Paper A Hybrid MCDA Optimization Approach for Image Compression in Web Performance Enhancement

Introduction: The surge in demand for modern applications has high resolution images which causes a web performance optimization problem. Most traditional client-side and server-side image optimization processes tend to overlook the complete solution that puts into account load time, response time, and bandwidth usage. This research proposes an adaptive decision-making paradigm for image compression based on a hybrid optimization framework combining Multi-Criteria Decision Analysis (MCDA) with Entropy Weighting + TOPSIS and Optimization Theory (Lagrange Multiplier Method). Analysis on real-world datasets shows that hybrid optimization, with its integrated strategies, more than standalone methods, validating the proposed optimization framework strategy principles by claiming over 92% efficiency ranking in performance evaluation. Removing these modifications does not stand the claim of improvement. This result is claimed through ANOVA statistical tests proving that the claimed improvements are in fact relevant. Machine learning dynamic image adaptation algorithms will be included in future work.