The Impact of AI Algorithm Optimization on Content Quality Enhancement in Short Video and Micro-Film Production

Abstract

The rapid evolution of artificial intelligence (AI) is fundamentally reshaping the landscape of short-form video and micro-film production. This paper provides a systematic analysis of the pivotal role played by AI algorithm optimization in enhancing the overall quality of this dynamic media format. We specifically investigate its transformative impact across three core dimensions: content creation, post-production efficiency, and end-user engagement. By leveraging advanced deep learning architectures, including Generative Adversarial Networks (GANs) for visual synthesis, Convolutional Neural Networks (CNNs) for scene understanding and aesthetic enhancement, and Recurrent Neural Networks (RNNs) for narrative structure prediction, AI-driven tools are enabling unprecedented capabilities. These innovations facilitate not only a surge in video generation speed and a significant uplift in image resolution and stability but also the development of more coherent narrative logic and deeply immersive interactive experiences. However, the integration of AI also presents challenges, such as the risk of stylistic homogenization and emerging ethical concerns regarding deepfakes and data provenance. This study concludes that the strategic optimization of AI algorithms is a critical driver for quality advancement, pushing the boundaries of creative expression and setting new technical benchmarks for the industry. Future work should focus on developing more explainable and controllable AI systems that empower, rather than replace, human creativity.