With the rapid evolution in modern multimedia networks and systems, services such as telemedicine and tele-surgery are becoming more popular. Quality estimation and monitoring of medical videos is becoming important not only in the field of research, but also in real-time applications and services. The state-of-the-art video quality metric (VQM) called Video Multimethod Assessment Fusion (VMAF) is a promising solution for quality estimation of videos impaired by compression and scaling artifacts. The metric was developed by Netflix for entertainment video content and its good performance does not necessarily extend to medical video. This paper focuses on evaluating the performance of VMAF in the context of quality assessment (QA) for medical videos. We consider in this paper medical video compressed via High Efficiency Video Coding (HEVC) and refer in particular to medical ultrasound videos and wireless capsule endoscopy (WCE) videos for the performance estimation of VMAF. The correlation between the subjective scores of these two datasets and VMAF’s quality estimates is studied and presented. The results show that VMAF outperforms other state-of-the-art VQMs in the context of WCE videos, but this is not the case for medical ultrasound videos.
The computer you’ll wear on your face in 10 years: Snap’s new Spectacles 3 camera glasses are an important step toward the #AR glasses that could one day replace the smartphone as our go-to computing device https://t.co/tke90bffPh #DigitalHealth— Exponential Medicine (@ExponentialMed) August 15, 2019