The Battle of the Video Codecs in the Healthcare Domain - A Comparative Performance Evaluation Study Leveraging VVC and AV1
Open accessAbstract
Video compression is the core technology in mobile (mHealth) and electronic (eHealth) health video streaming applications. With global video traffic projected to reach 82% of all Internet traffic by 2022, there is a strong need to develop efficient compression algorithms to accommodate expected future growth. For the first time in decades, and especially since ISO/IEC MPEG and ITU-T VCEG expert groups strategically joined forces to develop the highly successful H.264/AVC standard, we have two distinct initiatives competing for the best performing video codec. On the one hand, we have the Alliance for Open Media (AOM) that support a new, royalty free video codec generation, termed AV1, based on VP8 and VP9 efforts. On the other hand, the Joint Video Exploration Team (JVET) has been developing the Versatile Video Codec (VVC) as the successor of the High Efficiency Video Coding (HEVC) standard. At the same time, the breadth of applications utilizing video codecs, involving significant content variability and moving across the video resolution ladder, to satisfy different constraints, have resulted in mixed literature results, with respect to the best performing codec. In this paper, we compare the performance of emerging VVC and AV1 codecs, along with popular HEVC implementations, namely the HEVC Test Model (HM) and x265, as well as earlier, VP9 codec, and investigate their suitability for medical applications. To the best of our knowledge, this is the first performance comparison of emerging VVC and AV1 video codecs for use in the healthcare domain. Experimental evaluation based on three datasets (ultrasound, emergency scenery, and general-purpose videos) demonstrate that VVC outperforms all rival codecs while AV1 achieves better compression efficiency than HEVC in all cases but low-resolution ($560\times448$ @40Hz) ultrasound videos of the common carotid artery. Furthermore, the use of video despeckling prior to ultrasound video compression can provide significant bitrate savings.
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To achieve semantic interoperability, it is necessary to develop and optimize software that endorses and relies on interoperability profiles and standards (IHE, HL7 FHIR, DICOM) and clinical terminology coding (SNOMED CT, ICD).
“To effectively incorporate this information into the EHRs and achieve semantic interoperability, it is necessary to develop and optimize software that endorses and relies on interoperability profiles and standards.”
From AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020) · cites this paper as [48]
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References 43 · Cited by 27
- Authors
- Panayides, A. S., Pattichis, M. S., Pantziaris, M., Constantinides, A. G., & Pattichis, C. S.
- Affiliations
- University of Cyprus (CY)
- University of New Mexico (US)
- Cyprus Institute of Neurology and Genetics (CY)
- Imperial College London (GB)
- ORCID
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 11469-11481
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3000708736
- Cited by
- 27
- References
- 43
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 5 · 2023: 3 · 2022: 2 · 2021: 8 · 2020: 2
- FWCI
- 2.03
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- European Regional Development Fund (POST-DOC/0916/0023)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Video Coding and Compression Technologies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Data Compression Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Keywords
- video codecs, Versatile Video Coding, AV1, HEVC, H.264/AVC, ultrasound video, bitrate savings, mobile health, e-health, x265, VP9, low-resolution video
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