Real-ESRGAN in Neural Rendering Frameworks for High-Fidelity Video Upscaling

As digital video content continues to grow, the need for enhanced visual quality has become increasingly important. However, utilizing the Real-ESRGAN video module can often lead to issues such as frame skipping and inconsistent enhancement results. This paper tackles these issues by introducing a novel approach that enhances individual frames of a video separately. By converting the original video into distinct frames and applying Real-ESRGAN for enhancement, we ensure that each frame is treated individually, thereby eliminating frame skips and achieving uniform enhancement across the video. After processing, the frames are reassembled into a cohesive video output. Our extensive evaluations reveal significant improvements in visual fidelity compared to conventional enhancement techniques. This research highlights the promise of integrating neural rendering methods in various fields, including film, gaming, and online streaming, and provides valuable contributions to the evolving landscape of video processing.