Video Compression using Deep Neural Networks
| Title | Video Compression using Deep Neural Networks |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | P, Dayananda, Subramanian, Siddharth, Suresh, Vijayalakshmi, Shivalli, Rishab, Sinha, Shrinkhla |
| Conference Name | 2022 Fourth International Conference on Cognitive Computing and Information Processing (CCIP) |
| Keywords | Adaptation models, Codecs, Deep Learning, deep video, h264, h265, image interpolation, interpolation, Metrics, Neural Network, Neural networks, pubcrawl, resilience, Resiliency, Scalability, Video compression, video on demand |
| Abstract | Advanced video compression is required due to the rise of online video content. A strong compression method can help convey video data effectively over a constrained bandwidth. We observed how more internet usage for video conferences, online gaming, and education led to decreased video quality from Netflix, YouTube, and other streaming services in Europe and other regions, particularly during the COVID-19 epidemic. They are represented in standard video compression algorithms as a succession of reference frames after residual frames, and these approaches are limited in their application. Deep learning's introduction and current advancements have the potential to overcome such problems. This study provides a deep learning-based video compression model that meets or exceeds current H.264 standards. |
| DOI | 10.1109/CCIP57447.2022.10058645 |
| Citation Key | p_video_2022 |