International Journal of Science, Engineering and TechnologyAuthors: Ms. Pallavi D G, Smt. Preethi H U
Abstract: The proliferation of video content across social media, marketing, and entertainment has created an unprecedented demand for efficient, high-quality video editing. Traditional editing remains a labour-intensive, skill-dependent process, creating a significant bottleneck. This paper introduces a comprehensive AI-driven video editing framework that automates and enhances key aspects of post-production. Our proposed methodology integrates computer vision, natural language processing, and reinforcement learning to create a system capable of understanding narrative intent, analysing raw footage, and producing edited sequences according to dynamic stylistic and technical rules. We detail a multi-stage pipeline comprising: 1) Content Analysis (scene detection, shot classification, emotion/object recognition), 2) Narrative Structuring (based on a learned or user-provided "beat sheet"), and 3) Automated Editing (shot selection, sequencing, and basic transitions). We conducted experiments comparing AI-edited sequences against human-edited baselines for tasks like highlight reel generation, documentary-style assembly, and social media clip creation. Quantitative metrics (continuity preservation, pacing consistency, aesthetic composition score) and qualitative user evaluations demonstrate that our framework achieves 88% user satisfaction for specific, well-defined editing tasks and reduces editing time by approximately 70%. However, for complex narrative work, human oversight remains crucial. The findings indicate that AI is best positioned as a collaborative tool—an "automated assistant"—that handles technical and repetitive tasks, freeing human editors to focus on creative direction and emotional nuance.
