Subjective MPEG2 compressed video quality assessment: Application to Tele-surgery

Nouri, Nedia; Abraham, Denis; Moureaux, Jean-Marie; Dufaut, Michel; Hubert, Jacques; Perez, Manuela

doi:10.1109/isbi.2010.5490064

Cited by 18 publications

(6 citation statements)

References 4 publications

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“…The DSCQS method is adopted in our tests because in this method the subjective scores are less sensitive to the context, i.e., the ordering and the level of impaired sequences has less influence on the subjective ratings [23]. The DSCQS method is widely used in medical video subjective quality evaluation, for instance, in [24], [8], [25].…”

Section: B Subjective Testmentioning

confidence: 99%

“…For instance in [7] an index to measure the decrease in diagnostic content of compressed echocardiograms was developed based on the inputs from cardiologists. [8] recommended acceptable compression ratios using MPEG-2 for surgical videos. Very few works in the literature have focused on evaluating the performance of state of the art objective video quality metrics in the context of medical videos, in particular medical ultrasound videos.…”

Section: Introductionmentioning

confidence: 99%

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A Study on Quality Assessment for Medical Ultrasound Video Compressed via HEVC

Razaak

Martini

Savino

2014

IEEE J. Biomed. Health Inform.

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Abstract-The Quality of Experience (QoE) and Quality of Service (QoS) provided in the healthcare sector are critical in evaluating the reliable delivery of the healthcare services provided. Medical images and videos play a major role in modern e-health services and have become an integral part of medical data communication systems. The quality evaluation of medical images and videos is an essential process, and one of the ways of addressing it is via the use of quality metrics. In this paper, we evaluate the performance of seven state of the art video quality metrics with respect to compressed medical ultrasound video sequences. We study the performance of each video quality metric in representing the diagnostic quality of the video, by evaluating the correlation of each metric with the subjective opinions of medical experts. The results indicate that the Visual Information Fidelity (VIF), Structural Similarity Index Metric (SSIM), and Universal Quality Index (UQI) metrics show good correlation with the subjective scores provided by medical experts. The tests also investigate the performance of the emerging video compression standard, High Efficiency Video Coding (HEVC), for medical ultrasound video compression. The results show that, using HEVC, a diagnostically reliable compressed ultrasound video can be obtained for compression with values of the quantization parameter, QP, upto 35.Index Terms-Medical video quality evaluation, HEVC, service science, objective & subjective video quality assessment, medical ultrasound videos, video compression.

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Section: B Subjective Testmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Study on Quality Assessment for Medical Ultrasound Video Compressed via HEVC

Razaak

Martini

Savino

2014

IEEE J. Biomed. Health Inform.

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“…A decade ago, Nouri et al published a short paper in which they aimed to define compression thresholds for the telesurgery context (Nouri et al 2010). To this purpose, they extracted four videos from different operations, including a movement of surgical laparoscopic instrument, a blood clot, a fat area, and a compress application.…”

Section: Endoscopic Laparoscopic and Open Surgeriesmentioning

confidence: 99%

Comparative study of the methodologies used for subjective medical image quality assessment

et al. 2021

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Healthcare professionals have been increasingly viewing medical images and videos in their routine clinical practice, and this in a wide variety of environments. Both the perception and interpretation of medical visual information, across all branches of practice or medical specialties (e.g., diagnostic, therapeutic, or surgical medicine), career stages, and practice settings (e.g., emergency care), appear to be critical for patient care. However, medical images and videos are not self-explanatory and, therefore, need to be interpreted by humans, i.e., medical experts. In addition, various types of degradations and artifacts may appear during image acquisition or processing, and consequently affect medical imaging data. Such distortions tend to impact viewers' quality of experience, as well as their clinical practice. It is accordingly essential to better understand how medical experts perceive the quality of visual content. Thankfully, progress has been made in the recent literature towards such understanding. In this article, we present an up-to-date state of the art of relatively recent (i.e., not older than ten years old) existing studies on the subjective quality assessment of medical images and videos, as well as research works using task-based approaches. Furthermore, we discuss the merits and drawbacks of the methodologies used, and we provide recommendations about experimental designs and statistical processes to evaluate the perception of medical images and videos for future studies, which could then be used to optimise the visual experience of image readers in real clinical practice. Finally, we tackle the issue of the lack of available annotated medical image and video quality databases, which appear to be indispensable for the development of new dedicated objective metrics.

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“…Before providing an overview of the VQA methods, let us emphasize here that most of the developed works have been devoted to natural video data [10]- [14] and only few works have focused on medical data, but most often dedicated to coding and transmission problems. [15]- [17].…”

Section: Introductionmentioning

confidence: 99%

A Neural Network based Framework for Effective Laparoscopic Video Quality Assessment

Khan,

Beghdadi,

Kaaniche

et al. 2022

Preprint

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Video quality assessment is a challenging problem having a critical significance in the context of medical imaging. For instance, in laparoscopic surgery, the acquired video data suffers from different kinds of distortion that not only hinder surgery performance but also affect the execution of subsequent tasks in surgical navigation and robotic surgeries. For this reason, we propose in this paper neural network-based approaches for distortion classification as well as quality prediction. More precisely, a Residual Network (ResNet) based approach is firstly developed for simultaneous ranking and classification task. Then, this architecture is extended to make it appropriate for the quality prediction task by using an additional Fully Connected Neural Network (FCNN). To train the overall architecture (ResNet and FCNN models), transfer learning and end-to-end learning approaches are investigated. Experimental results, carried out on a new laparoscopic video quality database, have shown the efficiency of the proposed methods compared to recent conventional and deep learning based approaches.

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Subjective MPEG2 compressed video quality assessment: Application to Tele-surgery

Cited by 18 publications

References 4 publications

A Study on Quality Assessment for Medical Ultrasound Video Compressed via HEVC

A Study on Quality Assessment for Medical Ultrasound Video Compressed via HEVC

Comparative study of the methodologies used for subjective medical image quality assessment

A Neural Network based Framework for Effective Laparoscopic Video Quality Assessment

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