Year: 2026
Type: article
Abstract: Multimedia processing has become an indispensable part of modern digital ecosystems, powering applications in education, entertainment, research, and social media.Operations such as compression, format conversion, trimming, merging, and audio extraction are frequently required but remain inaccessible to many users due to the complexity of FFmpeg, one of the most widely used multimedia frameworks.FFmpeg's command-line interface is powerful but challenging for non-technical users, as it demands memorization of long, non-intuitive commands.To overcome these limitations, this project proposes the FFmpeg AI Assistant, an offline application that translates natural language instructions into accurate FFmpeg commands.The assistant not only executes tasks like video compression, trimming, and conversion but also explains the underlying commands, thereby doubling as a learning platform.Unlike existing GUI-based or cloud-based tools, the proposed assistant is fully offline, ensuring privacy, faster performance, and usability in low-connectivity environments.It is designed to be lightweight, crossplatform, and accessible for both technical and nontechnical users.The project integrates AI-powered natural language processing (NLP) for command generation, intelligent media analysis for optimal parameter selection, and a simple GUI for user interaction.The outcome is an educational, secure, and efficient multimedia assistant that empowers users while minimizing resource requirements.This study aims to bridge the gap between user-friendly media processing and advanced command-line operations, creating a scalable and future-ready solution for everyday multimedia challenges.
Institution: Raisoni Group of Institutions
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7134251179
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