Performance Evaluation of AVC and HEVC for E-Learning: Optimizing Quality and Reducing Bandwidth Usage

Year 2023, , 581 - 591, 31.12.2023

Oğuz Kırat , Tarık Yerlikaya , Emir Öztürk

https://doi.org/10.24012/dumf.1335369

Abstract

E-learning has experienced a surge in popularity, particularly during and after the COVID-19 pandemic. Online learning has proven to be a vital tool for students and educators to continue academic activities while adhering to social distancing guidelines and during the times of natural disasters that disrupt the conventional learning environments. It also offers accessibility to disabled students and those facing challenges to reach to the traditional learning. But due to increased demand, it is crucial to optimize cost of transmission while minimizing bandwidth usage while maintaining high-quality video transmission. To optimize cost and reduce network load, it is essential to minimize bandwidth usage while maintaining high-quality video. In response to this need, we present a novel dataset consisting of four e-learning scenarios. We encoded this dataset using various resolutions, bit rates, and encoder presets, and evaluated it in terms of encoding time, and quality using full-reference objective quality metrics such as MSE, PSNR, and SSIM. After experimenting with more than 1400 videos and configurations of encoders and codecs, we found out that it is possible to transmit videos in exceptional quality at bitrates as low as 5 Mbps for e-learning scenarios. We also present detailed results about correlation between file size, quality and encoding time to make optimizations for specific bandwidth, target quality or encoding speed.

Keywords

AVC, Compression, E-Learning, HEVC, Video Encoding, Video Quality Assessment

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