Utilizing generative adversarial networks (GANs) for realistic video stream augmentation and enhancement

Abstract In an era where visual media dominates, enhancing the quality of video streams is paramount. Leveraging the power of Generative Adversarial Networks (GANs), our research introduces a groundbreaking method for augmenting and enhancing video streams, transforming them into remarkably high-quality experiences even under constrained conditions. This manuscript delves into the utilization of GANs to not only improve but also to revolutionize the fidelity of video content, particularly beneficial in situations of limited bandwidth or inferior source quality. The results demonstrate a significant enhancement in video quality. Quantitative assessments show an average of 40% improvement in peak signal-to-noise ratio (PSNR) and a 35% enhancement in the Structural Similarity Index (SSIM) across various test videos. Qualitatively, the enhanced streams exhibit notably sharper images, richer colors, and reduced artifacts, leading to a more immersive viewing experience. Furthermore, user studies indicate a 75% increase in viewer satisfaction scores for videos enhanced using our GAN model compared to their original versions.