Abstract
Video streaming applications have witnessed widespread adoption over the past decades due to the rising demand for real-time and on-demand video content across different application domains. As a result, video streaming has become the dominant source of internet traffic, while the abundance of video-driven applications will likely lead to a further increase in the near future, enabled by associated advances in video devices’ capabilities. In that context, there is a strong need to develop efficient compression and video delivery algorithms to accommodate future growth. To this end, this study presents a comparative performance evaluation of six different video codecs. More specifically, we compare the performance of the Versatile Video Coding (VVC) standard developed by the Joint Video Experts Team (JVET) and the AV1 codec developed by the Alliance for Open Media (AOM). Additionally, we assess the capacity of the newly released UVG-266 VVC encoder available from the Ultra Video Group, along with the Essential Video Coding (EVC) standard’s reference implementation. Finally, we include in our experiments the most popular High Efficiency Video Coding (HEVC) implementation, namely x265, together with the VP9 codec. Experimental evaluation based on three general-purpose video datasets (768 \(\times \) 432 and 3840 \(\times \) 2160 video resolutions) and one ultrasound video dataset (560 \(\times \) 448 video resolution) demonstrates that VVC outperforms all rival codecs to date, especially as video resolution increases, followed by AV1.
This study is partly funded by the project ‘Atherorisk’ “Identification of unstable carotid plaques associated with symptoms using ultrasonic image analysis and plaque motion analysis”, code: Excellence/0421/0292, funded by the Research and Innovation Foundation, the Republic of Cyprus.
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Valiandi, I., Panayides, A.S., Kyriacou, E., Pattichis, C.S., Pattichis, M.S. (2023). A Comparative Performance Assessment of Different Video Codecs. In: Tsapatsoulis, N., et al. Computer Analysis of Images and Patterns. CAIP 2023. Lecture Notes in Computer Science, vol 14185. Springer, Cham. https://doi.org/10.1007/978-3-031-44240-7_26
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