4,599 research outputs found
Open-Source Telemedicine Platform for Wireless Medical Video Communication
An m-health system for real-time wireless communication of medical video based on open-source software is presented. The objective is to deliver a low-cost telemedicine platform which will allow for reliable remote diagnosis m-health applications such as emergency incidents, mass population screening, and medical education purposes. The performance of the proposed system is demonstrated using five atherosclerotic plaque ultrasound videos. The videos are encoded at the clinically acquired resolution, in addition to lower, QCIF, and CIF resolutions, at different bitrates, and four different encoding structures. Commercially available wireless local area network (WLAN) and 3.5G high-speed packet access (HSPA) wireless channels are used to validate the developed platform. Objective video quality assessment is based on PSNR ratings, following calibration using the variable frame delay (VFD) algorithm that removes temporal mismatch between original and received videos. Clinical evaluation is based on atherosclerotic plaque ultrasound video assessment protocol. Experimental results show that adequate diagnostic quality wireless medical video communications are realized using the designed telemedicine platform. HSPA cellular networks provide for ultrasound video transmission at the acquired resolution, while VFD algorithm utilization bridges objective and subjective ratings
Optimized Adaptive Streaming Representations based on System Dynamics
Adaptive streaming addresses the increasing and heterogenous demand of
multimedia content over the Internet by offering several encoded versions for
each video sequence. Each version (or representation) has a different
resolution and bit rate, aimed at a specific set of users, like TV or mobile
phone clients. While most existing works on adaptive streaming deal with
effective playout-control strategies at the client side, we take in this paper
a providers' perspective and propose solutions to improve user satisfaction by
optimizing the encoding rates of the video sequences. We formulate an integer
linear program that maximizes users' average satisfaction, taking into account
the network dynamics, the video content information, and the user population
characteristics. The solution of the optimization is a set of encoding
parameters that permit to create different streams to robustly satisfy users'
requests over time. We simulate multiple adaptive streaming sessions
characterized by realistic network connections models, where the proposed
solution outperforms commonly used vendor recommendations, in terms of user
satisfaction but also in terms of fairness and outage probability. The
simulation results further show that video content information as well as
network constraints and users' statistics play a crucial role in selecting
proper encoding parameters to provide fairness a mong users and to reduce
network resource usage. We finally propose a few practical guidelines that can
be used to choose the encoding parameters based on the user base
characteristics, the network capacity and the type of video content
Understanding the Perceived Quality of Video Predictions
The study of video prediction models is believed to be a fundamental approach
to representation learning for videos. While a plethora of generative models
for predicting the future frame pixel values given the past few frames exist,
the quantitative evaluation of the predicted frames has been found to be
extremely challenging. In this context, we study the problem of quality
assessment of predicted videos. We create the Indian Institute of Science
Predicted Videos Quality Assessment (IISc PVQA) Database consisting of 300
videos, obtained by applying different prediction models on different datasets,
and accompanying human opinion scores. We collected subjective ratings of
quality from 50 human participants for these videos. Our subjective study
reveals that human observers were highly consistent in their judgments of
quality of predicted videos. We benchmark several popularly used measures for
evaluating video prediction and show that they do not adequately correlate with
these subjective scores. We introduce two new features to effectively capture
the quality of predicted videos, motion-compensated cosine similarities of deep
features of predicted frames with past frames, and deep features extracted from
rescaled frame differences. We show that our feature design leads to state of
the art quality prediction in accordance with human judgments on our IISc PVQA
Database. The database and code are publicly available on our project website:
https://nagabhushansn95.github.io/publications/2020/pvqaComment: Project website:
https://nagabhushansn95.github.io/publications/2020/pvqa.htm
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