EzStream: Video Compression and Textual Representation for Multimedia Sensor Networks

In an age characterized by the relentless growth of multimedia data, the efficient management and processing of video content have emerged as pressing challenges. This paper introduces “EzStream,” a dynamic video-to-text conversion system that transcends the conventional boundaries of compression techniques. Rather than merely reducing file sizes, EzStream delves deeper by converting video content into a textual format, thereby encapsulating the essence of the information embedded within. What sets EzStream apart is its adaptability, as it can be retrained to cater to a diverse array of applications, ensuring that the extracted text aligns precisely with specific task requirements. Our extensive experimentation, grounded in the Human Motion Database (HMDB) dataset, has unveiled a pivotal insight: the selection of an effective network architecture profoundly influences encoding efficiency. We present a comprehensive analysis of EzStream's performance, showcasing the tangible impact of architectural choices on video encoding. Our results underscore a series of transformative benefits, including remarkable reductions in file sizes, expedited processing times, and enhancements in video quality. This paper serves as a beacon, illuminating the indispensable role of network architecture in video processing. It highlights how this architectural foundation empowers EzStream to excel in encoding, ultimately reshaping the landscape of multimedia data management. In an era marked by the exponential growth of multimedia content, the innovation embodied in EzStream not only meets but anticipates the escalating demands for resource-efficient multimedia processing, promising more agile and adaptive solutions for information extraction and utilization.