Compression of Patient’s Video for Transmission Over Low Bandwidth Network

Delhaye, Romain; Noumeir, Rita; Kaddoum, Georges; Jouvet, Philippe

doi:10.1109/access.2019.2895783

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…This system guarantees the video quality by using the adaptive video-encoding technique and minimizes the distortion by considering the truncating distortion in the enhanced distortion-minimization algorithm. Delhaye et al [24] address the challenges of video compression for telemedicine applications in a very low bandwidth environment, such as air-to-ground transmission channels. Their method compresses the region of interest (ROI) with higher quality by using, for a specific available bit rate, different quantizers for blocs inside and outside the ROI.…”

Section: Adaptive Video Streaming For Healthcarementioning

confidence: 99%

“…Therefore, in addition to selecting an optimal reconstructed high-resolution level, the model of VSR also needs to be well designed to adapt to the available computational capability of the edge node. In addition, in the field of telemedicine or healthcare, a super-resolution (SR) technique has already been applied to promote the quality of medical images or videos [11][12][13][14][15][16][17][18][19], and many efforts also have been devoted to designing adaptive medical video-streaming strategies [7,[20][21][22][23][24], but there is still a lack of research on how to employ VSR to enhance the medical video transmission.…”

Section: Introductionmentioning

confidence: 99%

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Super-Resolution-Empowered Adaptive Medical Video Streaming in Telemedicine Systems

2022

Electronics

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Due to influence of COVID-19, telemedicine is becoming more and more important. High-quality medical videos can provide a physician with a better visual experience and increase the accuracy of disease diagnosis, but this requires a dramatic increase in bandwidth compared to that required by regular videos. Existing adaptive video-streaming approaches cannot successfully provide high-resolution video-streaming services under poor or fluctuating network conditions with limited bandwidth. In this paper, we propose a super-resolution-empowered adaptive medical video streaming in telemedicine system (named SR-Telemedicine) to provide high quality of experience (QoE) videos for the physician while saving the network bandwidth. In SR-Telemedicine, very low-resolution video chunks are first transmitted from the patient to an edge computing node near the physician. Then, a video super-resolution (VSR) model is employed at the edge to reconstruct the low-resolution video chunks into high-resolution ones with an appropriate high-resolution level (such as 720p or 1080p). Furthermore, the neural network of VSR model is designed to be scalable and can be determined dynamically. Based on the time-varying computational capability of the edge computing node and the network condition, a double deep Q-Network (DDQN)-based algorithm is proposed to jointly select the optimal reconstructed high-resolution level and the scale of the VSR model. Finally, extensive experiments based on real-world traces are carried out, and the experimental results illustrate that the proposed SR-Telemedicine system can improve the QoE of medical videos by 17–79% compared to three baseline algorithms.

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Section: Adaptive Video Streaming For Healthcarementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Super-Resolution-Empowered Adaptive Medical Video Streaming in Telemedicine Systems

2022

Electronics

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show abstract

“…Diagnostically driven video encoding has come under limelight recently and there have been some recent studies under this domain [9][10][11][12][13][14][15][16]. The aim of such kind of video encoding is to identify regions of interest (ROI) in medical videos, e.g., a tumor in a colonoscopy imagery, and then compressing these regions in a better quality than other regions.…”

Section: Medical Video Communication Systems and Applicationsmentioning

confidence: 99%

Suitability of VVC and HEVC for Video Telehealth Systems

Usman¹,

Usman²,

Mcphilips³

et al. 2021

Computers, Materials &Amp; Continua

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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 Ef ciency 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 De nition (FHD) videos. The presented analysis highlights the impact of compression artefacts on the perceptual quality of HEVC and VVC processed videos. Our results and ndings 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.

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“…A non-exhaustive list of diagnostically driven video technologies include cardiac [11], [12], abdominal aortic aneurysm [13], common carotid artery [10], and femoral artery, ultrasound videos [14], as well as trauma [15], surgery [16], and emergency scenery videos [17].…”

Section: A Diagnostically Driven Video Technologiesmentioning

confidence: 99%

The Battle of the Video Codecs in the Healthcare Domain - A Comparative Performance Evaluation Study Leveraging VVC and AV1

et al. 2020

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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 × 448@40Hz) ultrasound videos of the common carotid artery. Furthermore, the use of video despeckling prior to ultrasound video compression can provide significant bitrate savings.INDEX TERMS Video codecs, video signal processing, video coding, video compression, video quality, video despeckling, video streaming, adaptive video streaming, versatile video coding, AV1, HEVC, mHealth, eHealth. I. INTRODUCTIONVideo codecs are currently experiencing unparalleled levels of growth driven by unprecedented video traffic demands that are projected to reach 82% of all Internet traffic by The associate editor coordinating the review of this manuscript and approving it for publication was Xinfeng Zhang.

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Compression of Patient’s Video for Transmission Over Low Bandwidth Network

Cited by 5 publications

References 18 publications

Super-Resolution-Empowered Adaptive Medical Video Streaming in Telemedicine Systems

Super-Resolution-Empowered Adaptive Medical Video Streaming in Telemedicine Systems

Suitability of VVC and HEVC for Video Telehealth Systems

The Battle of the Video Codecs in the Healthcare Domain - A Comparative Performance Evaluation Study Leveraging VVC and AV1

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