Title: Deep learning based on multimedia encoding to enhance video quality

Authors: Nagendra Panini Challa; C. Shanmuganathan; M. Shobana; Ch. Venkata Sasi Deepthi; N. Bharathiraja

Addresses: School of Computer Science and Engineering, VIT-AP University, Amaravati, India ' Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, 600089, India ' Department of Networking and Communications, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, 603203, India ' Department of Computer Science and Engineering, Shri Vishnu Engineering College for Women, Bhimavaram, 534202, India ' Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India

Abstract: Over the years, video compression has become a key method of sharing information and communicating in the world of media. The future image quality of the ISO standards groups will expect high quality images. Also, visual video codecs (VVC) have optimised encoding decisions that use the same average square error, or total square difference, for decades. At the same time, the sudden increase in deep learning (DL) methods raises the issue of whether DL could actually profoundly change the way the clip was programmed. We developed a new visual quality measurement (VQM) to find ways to make it better. The proposed approach could significantly reduce the encoding compute load while maintaining almost the same enhanced rate distortion optimisation (ERDO) efficiency as the previous encoder, based on experimental measurements. To further improve efficiency, the proposed methodology could be combined with fast motion search algorithms and filtration methods.

Keywords: multimedia; deep learning; visual quality measurement; VQM; quality; encoding optimiser; visual video codecs; VVC; enhanced rate distortion optimisation; ERDO.

DOI: 10.1504/IJCNDS.2023.133160

International Journal of Communication Networks and Distributed Systems, 2023 Vol.29 No.5, pp.475 - 492

Received: 30 May 2022
Accepted: 11 Jul 2022
Published online: 01 Sep 2023 *

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