Title: Deep learning-based video coding optimisation of H.265

Authors: C. Karthikeyan; Tammineedi Venkata Satya Vivek; S. Lakshmi Narayanan; S. Markkandan; D. Vijendra Babu; Shilpa Laddha

Addresses: Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation (Deemed to be University), Vaddeswaram, Guntur, Andhra Pradesh, India ' International School of Technology and Sciences (For Women), Rajanagaram, Rajahmundry, Andhra Pradesh-533294, India ' Department of ECE, Gojan School of Business and Technology, Chennai, India ' Department of ECE, SRM TRP Engineering College, Tamil Nadu, India ' Department of Electronics & Communication Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Mission's Research Foundation, Paiyanoor 603-104, Tamil Nadu, India ' Department of Information Technology, Government College of Engineering, Aurangabad, Maharashtra, India

Abstract: Today's multi-media applications need high video quality with low bitrates. However, it is restricted in its capacity to provide higher quality than earlier coding methods. Deep learning (DL) approaches for video coding have shown compression capacities equal to or better than traditional methods, including high-efficiency video coding (HEVC) methods. The trade-off between compression efficiency and encoding/decoding complexity, optimisation for perceptual nature of semantic dependability, specialisation, and universality, the federalised layout of various deep toolkits, etc. remains unclear. HEVC encoding is more efficient than previous standards. Improved efficiency is driven by intra image prediction, which incorporates more prior directions (35 modes) than previous standards. Its high efficiency comes from balancing encoder complexity and dependability. This article presents DL, which uses a convolutional neural network to predict the best model with the least rate-distortion (RD) and further promotes study into deep learning video coding (DLVC).

Keywords: deep learning video coding; DLVC; high-efficiency video coding; HEVC/H.264; rate-distortion; rate-distortion optimisation; RDO.

DOI: 10.1504/IJESMS.2023.127392

International Journal of Engineering Systems Modelling and Simulation, 2023 Vol.14 No.1, pp.52 - 57

Received: 31 Jul 2021
Accepted: 02 Sep 2021
Published online: 03 Dec 2022 *