Abstract
Video compression in medical video streaming is one of the key technologies associated with mobile healthcare. Seamless delivery of medical video streams over a resource constrained network emphasizes the need of a video codec that requires minimum bitrates and maintains high perceptual quality. This paper presents a comparative study between High Efficiency Video Coding (HEVC) and its potential successor Versatile Video Coding (VVC) in the context of healthcare. A large-scale subjective experiment comprising of twenty-four non-expert participants is presented for eight different test conditions in Full High Definition (FHD) videos. The presented analysis highlights the impact of compression artefacts on the perceptual quality of HEVC and VVC processed videos. Our results and findings show that VVC clearly outperforms HEVC in terms of achieving higher compression, while maintaining high quality in FHD videos. VVC requires upto 40% less bitrate for encoding an FHD video at excellent perceptual quality. We have provided rate-quality curves for both encoders and a degree of overlap across both codecs in terms of perceptual quality. Overall, there is a 71% degree of overlap in terms of quality between VVC and HEVC compressed videos for eight different test conditions.
Highlights
Telehealth or telemedicine is the remote provision of healthcare services that is done by transferring medical data on communication channels
Observing the bar-graphs in Fig. 8 for each High Ef ciency Video Coding (HEVC) compressed video sequence, it can be observed that the videos compressed at Quantization Parameter (QP) = 27 have differential MOS (DMOS) between 1 to 20 which corresponds to excellent quality according to the continuous quality scale in Tab. 3
Similar trend can be observed for videos compressed at QP = 29 except for the PVS Rainbow as its DMOS lies between 20 to 40 which translates to good quality
Summary
Telehealth or telemedicine is the remote provision of healthcare services that is done by transferring medical data on communication channels. Mobile healthcare is one of the key aspects of telemedicine in which clinicians perform a range of different clinical tasks remotely, while a patient is in a mobile platform [1] Such an example is the rising demand of pre-hospital medical treatments, which includes remote guidance to the in-ambulance paramedical staff from a physician. Modern communication networks such as 5G networks possess the potential of ful lling these requirements, still bandwidth is considered a precious and scarce resource [2] This necessitates the importance of data compression in Telemedicine, as huge amounts of medical data need to be transmitted over communication channels but need to be stored for further post-processing tasks such as clinical diagnosis
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