The exponential growth of e-commerce platforms has led to massive-scale image asset generation and distribution demands, requiring highly efficient compression and adaptive delivery mechanisms. Traditional image compression techniques struggle to maintain optimal trade-offs between visual quality, bandwidth consumption, and latency under dynamic user conditions. This paper proposes an AI-driven image compression and adaptive optimization framework designed for high-volume e-commerce asset delivery systems. The framework integrates deep learning-based image enhancement, edge-cloud collaborative offloading, and adaptive bitrate optimization to ensure efficient and scalable content distribution. Drawing from advancements in fog computing, mobile edge architectures, and deep neural image processing, the study synthesizes methodologies that enable intelligent compression decisions based on device capability, network conditions, and content semantics. The research also highlights the role of machine learning in optimizing image transmission pipelines, as widely explored in related domains such as cybersecurity and multimedia systems (Alanazi, 2020). Experimental reasoning and system modeling demonstrate that AI-based compression significantly improves throughput, reduces latency, and enhances perceptual image quality compared to conventional methods. The findings underscore the importance of adaptive, context-aware systems in modern e-commerce infrastructures.

AI-driven compression, e-commerce imaging, edge computing, fog computing, deep learning, adaptive optimization, image delivery, cloud offloading, super-resolution, neural compression

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