Authors - Sambhram Pattanayak, Pallavi Mishra, Ruhi Sethi, Prachi Trivedi Abstract - Rapid advances in Artificial Intelligence (AI) have significantly transformed high-end camera systems, particularly in autofocus, exposure control, and image processing. This study examines the growing integration of AI in high-end camera systems, focusing on its impact on these areas. By leveraging deep learning models and edge-based computational frameworks, modern cameras perform real-time scene analysis, subject recognition, and predictive parameter optimization. The research adopts a hybrid methodology that combines controlled experimental evaluation, particularly for AI-assisted autofocus, with a systematic review of contemporary industry and academic developments. Key performance indicators such as focus accuracy, response latency, exposure consistency, and subject-tracking reliability are analyzed under challenging conditions, including low light, dynamic motion, and complex scene compositions. The results demonstrate that AI-driven imaging systems significantly outperform traditional manual and semi-automated approaches by improving precision, reducing operational complexity, and enabling intelligent decision-making at the point of capture. The study also highlights AI’s dual role as a technical enabler and a creative support tool, allowing photographers and cinematographers to focus more on artistic expression while maintaining high technical standards. Overall, the research contributes to the evolving field of computational cinematography by offering a balanced evaluation of AI’s technical benefits and its implications for creative workflows.
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