@article {10.3844/jcssp.2022.589.598,
article_type = {journal},
title = {An Efficient Video Compression Framework using Deep Convolutional Neural Networks (DCNN)},
author = {Kumar, Kommerla Siva and Madhavi, P. Bindhu and Janaki, K.},
volume = {18},
number = {7},
year = {2022},
month = {Jul},
pages = {589-598},
doi = {10.3844/jcssp.2022.589.598},
url = {https://thescipub.com/abstract/jcssp.2022.589.598},
abstract = {In the current world, video streaming has grown in popularity and now accounts for a large percentage of internet traffic, making it challenging for service providers to broadcast videos at high rates while utilizing less storage space. To follow inefficient analytical coding design, previous video compression prototypes require non-learning-based designs. As a result, we propose a DCNN technique that integrates OFE-Net, MVE-Net, MVD-Net, MC-Net, RE-Net, and RD-Net for getting an ideal collection of frames by linking each frame pixel with preceding and following frames, then finding linked blocks and minimizing un needed pixels. In terms of MS-SIM and PSNR, the proposed DCNN approach produces good video quality at low bit rates.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}