Real-Time Video Frame Interpolation Using GANs for Enhanced Streaming Service

This work presents a novel approach to real-time video frame interpolation using Generative Adversarial Networks (GANs) to enhance streaming services. We developed a custom GAN architecture comprising a generator, which predicts intermediate frames through transposed convolutions, and a discriminator, which evaluates frame authenticity. The model processes video frames by resizing them to 64 × 64 pixels and normalizing pixel values. The training alternates between updating the generator and discriminator to minimize adversarial loss, improving the generator's ability to create realistic frames. Our results, tracked through loss curves and visual comparisons, demonstrate high-quality frame interpolation, enhancing visual smoothness and continuity in video streams. This method showcases the potential of GANs for real-time video processing and future innovations in the field.