An Efficient Video Compression Framework using Deep Convolutional Neural Networks (DCNN)

Journal of computer sciences

article

Kommerla Siva Kumar¹  P. Bindhu Madhavi²  K. Janaki³ 

[1] Department of Computer Science and Engineering, R.V.R and J.C College of Engineering;Department of AI and ML, The Oxford College of Engineering;Department of Computer Science and Engineering-AI, Faculty of Engineering, Jain Deemed to be University

关键词: Deep Neural Networks;    Encoding;    Decoding;    Video Compression;   

DOI  :  10.3844/jcssp.2022.589.598

学科分类：计算机科学（综合）

来源: Science Publications

【 摘 要 】

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.

【 授权许可】

CC BY