Artificial Intelligence-Enabled Image Processing for Energy Optimization in Computer Vision Systems

This research investigates the integration of Deep Q-Networks (DQN) and Ant Colony Optimization (ACO) in developing a hybrid framework aimed at optimizing energy consumption in computer vision systems, while ensuring performance in image processing tasks. With the increasing computational demands of modern applications, energy efficiency has become critical. The proposed methodology combines DQN’s ability to enhance decision-making processes with ACO’s exploration and exploitation strategies, resulting in efficient energy management. Key findings indicate that the DQN-ACO approach reduces energy consumption by 30% compared to baseline methods, while simultaneously improving accuracy by 8.24% and processing speed by 20%. Moreover, the framework exhibits strong adaptability in dynamic environments, leading to improvements in throughput and overall system performance. The outcomes of this research have significant implications for industries such as autonomous driving, surveillance, and mobile computing, where energy efficiency is paramount. By advancing both artificial intelligence and computer vision, this study contributes to the development of sustainable technology solutions, laying the foundation for future innovations in energy-efficient intelligent systems.