Research on Deep Learning Algorithms for Digital Media Content Creation

This paper presents a deep learning algorithm for digital media content creation. It is designed to efficiently produce high-quality image, audio, and video content via multimodal data fusion and an adaptive generation strategy. The algorithm integrates the Transformer architecture and the Generative Adversarial Network (GAN). It utilizes the robust feature extraction capabilities and self-attention mechanism of the Transformer to encode and blend the input multimodal data, including text descriptions, images, audio, and video. The generation module dynamically modifies parameters through the adaptive strategy to generate high-quality digital media content. Experimental results demonstrate that the Inception Score of the image and audio content generated by the algorithm in this paper reaches 8.5 and 4.2 respectively, outperforming existing methods significantly. Meanwhile, the generation efficiency is enhanced by 30%. The user satisfaction survey indicates that over 80% of users consider the generated content to be of high quality and creative. Additionally, in terms of video generation, the algorithm in this paper achieves a Video Structural Similarity Index (V -SSIM) of 0.85 and a Peak Signal-to-Noise Ratio (PSNR) of 32dB for the generated videos, surpassing the comparison algorithms in quality. The efficiency of video generation has also improved, with the average generation time shortened by 25% compared to existing methods. The research of this paper offers a novel technical approach for digital media content creation, holding significant theoretical and application value.