Video streaming

B

Educational interventions for the management of cancer-related fatigue in adults

Background: Cancer-related fatigue is reported as the most common and distressing symptom experienced by patients with cancer. It can exacerbate the experience of other symptoms, negatively affect mood, interfere with the ability to carry out everyday activities, and negatively impact on quality of life. Educational interventions may help people to manage this fatigue or to cope with this symptom, and reduce its overall burden. Despite the importance of education for managing cancer-related fatigue there are currently no systematic reviews examining this approach. Objectives: To determine the effectiveness of educational interventions for managing cancer-related fatigue in adults. Search methods: We searched the Cochrane Central Register of Controlled Trials (CENTRAL), and MEDLINE, EMBASE, CINAHL, PsycINFO, ERIC, OTseeker and PEDro up to 1st November 2016. We also searched trials registries. Selection criteria: We included randomised controlled trials (RCTs) of educational interventions focused on cancer-related fatigue where fatigue was a primary outcome. Studies must have aimed to evaluate the effect of educational interventions designed specifically to manage cancer-related fatigue, or to evaluate educational interventions targeting a constellation of physical symptoms or quality of life where fatigue was the primary focus. The studies could have compared educational interventions with no intervention or wait list controls, usual care or attention controls, or an alternative intervention for cancer-related fatigue in adults with any type of cancer. Data collection and analysis: Two review authors independently screened studies for inclusion and extracted data. We resolved differences in opinion by discussion. Trial authors were contacted for additional information. A third independent person checked the data extraction. The main outcome considered in this review was cancer-related fatigue. We assessed the evidence using GRADE and created a 'Summary of Findings' table. Main results: We included 14 RCTs with 2213 participants across different cancer diagnoses. Four studies used only 'information-giving' educational strategies, whereas the remainder used mainly information-giving strategies coupled with some problem-solving, reinforcement, or support techniques. Interventions differed in delivery including: mode of delivery (face to face, web-based, audiotape, telephone); group or individual interventions; number of sessions provided (ranging from 2 to 12 sessions); and timing of intervention in relation to completion of cancer treatment (during or after completion). Most trials compared educational interventions to usual care and meta-analyses compared educational interventions to usual care or attention controls. Methodological issues that increased the risk of bias were evident including lack of blinding of outcome assessors, unclear allocation concealment in over half of the studies, and generally small sample sizes. Using the GRADE approach, we rated the quality of evidence as very low to moderate, downgraded mainly due to high risk of bias, unexplained heterogeneity, and imprecision. There was moderate quality evidence of a small reduction in fatigue intensity from a meta-analyses of eight studies (1524 participants; standardised mean difference (SMD) -0.28, 95% confidence interval (CI) -0.52 to -0.04) comparing educational interventions with usual care or attention control. We found low quality evidence from twelve studies (1711 participants) that educational interventions had a small effect on general/overall fatigue (SMD -0.27, 95% CI -0.51 to -0.04) compared to usual care or attention control. There was low quality evidence from three studies (622 participants) of a moderate size effect of educational interventions for reducing fatigue distress (SMD -0.57, 95% CI -1.09 to -0.05) compared to usual care, and this could be considered clinically significant. Pooled data from four studies (439 participants) found a small reduction in fatigue interference with daily life (SMD -0.35, 95% CI -0.54 to -0.16; moderate quality evidence). No clear effects on fatigue were found related to type of cancer treatment or timing of intervention in relation to completion of cancer treatment, and there were insufficient data available to determine the effect of educational interventions on fatigue by stage of disease, tumour type or group versus individual intervention. Three studies (571 participants) provided low quality evidence for a reduction in anxiety in favour of the intervention group (mean difference (MD) -1.47, 95% CI -2.76 to -0.18) which, for some, would be considered clinically significant. Two additional studies not included in the meta-analysis also reported statistically significant improvements in anxiety in favour of the educational intervention, whereas a third study did not. Compared with usual care or attention control, educational interventions showed no significant reduction in depressive symptoms (four studies, 881 participants, SMD -0.12, 95% CI -0.47 to 0.23; very low quality evidence). Three additional trials not included in the meta-analysis found no between-group differences in the symptoms of depression. No between-group difference was evident in the capacity for activities of daily living or physical function when comparing educational interventions with usual care (4 studies, 773 participants, SMD 0.33, 95% CI -0.10 to 0.75) and the quality of evidence was low. Pooled evidence of low quality from two of three studies examining the effect of educational interventions compared to usual care found an improvement in global quality of life on a 0-100 scale (MD 11.47, 95% CI 1.29 to 21.65), which would be considered clinically significant for some. No adverse events were reported in any of the studies. Authors' conclusions: Educational interventions may have a small effect on reducing fatigue intensity, fatigue's interference with daily life, and general fatigue, and could have a moderate effect on reducing fatigue distress. Educational interventions focused on fatigue may also help reduce anxiety and improve global quality of life, but it is unclear what effect they might have on capacity for activities of daily living or depressive symptoms. Additional studies undertaken in the future are likely to impact on our confidence in the conclusions. The incorporation of education for the management of fatigue as part of routine care appears reasonable. However, given the complex nature of this symptom, educational interventions on their own are unlikely to optimally reduce fatigue or help people manage its impact, and should be considered in conjunction with other interventions. Just how educational interventions are best delivered, and their content and timing to maximise outcomes, are issues that require further research

Constructing a no-reference H.264/AVC bitstream-based video quality metric using genetic programming-based symbolic regression

In order to ensure optimal quality of experience toward end users during video streaming, automatic video quality assessment becomes an important field-of-interest to video service providers. Objective video quality metrics try to estimate perceived quality with high accuracy and in an automated manner. In traditional approaches, these metrics model the complex properties of the human visual system. More recently, however, it has been shown that machine learning approaches can also yield competitive results. In this paper, we present a novel no-reference bitstream-based objective video quality metric that is constructed by genetic programming-based symbolic regression. A key benefit of this approach is that it calculates reliable white-box models that allow us to determine the importance of the parameters. Additionally, these models can provide human insight into the underlying principles of subjective video quality assessment. Numerical results show that perceived quality can be modeled with high accuracy using only parameters extracted from the received video bitstream

Loss-resilient Coding of Texture and Depth for Free-viewpoint Video Conferencing

Free-viewpoint video conferencing allows a participant to observe the remote 3D scene from any freely chosen viewpoint. An intermediate virtual viewpoint image is commonly synthesized using two pairs of transmitted texture and depth maps from two neighboring captured viewpoints via depth-image-based rendering (DIBR). To maintain high quality of synthesized images, it is imperative to contain the adverse effects of network packet losses that may arise during texture and depth video transmission. Towards this end, we develop an integrated approach that exploits the representation redundancy inherent in the multiple streamed videos a voxel in the 3D scene visible to two captured views is sampled and coded twice in the two views. In particular, at the receiver we first develop an error concealment strategy that adaptively blends corresponding pixels in the two captured views during DIBR, so that pixels from the more reliable transmitted view are weighted more heavily. We then couple it with a sender-side optimization of reference picture selection (RPS) during real-time video coding, so that blocks containing samples of voxels that are visible in both views are more error-resiliently coded in one view only, given adaptive blending will erase errors in the other view. Further, synthesized view distortion sensitivities to texture versus depth errors are analyzed, so that relative importance of texture and depth code blocks can be computed for system-wide RPS optimization. Experimental results show that the proposed scheme can outperform the use of a traditional feedback channel by up to 0.82 dB on average at 8% packet loss rate, and by as much as 3 dB for particular frames

No-reference bitstream-based visual quality impairment detection for high definition H.264/AVC encoded video sequences

Ensuring and maintaining adequate Quality of Experience towards end-users are key objectives for video service providers, not only for increasing customer satisfaction but also as service differentiator. However, in the case of High Definition video streaming over IP-based networks, network impairments such as packet loss can severely degrade the perceived visual quality. Several standard organizations have established a minimum set of performance objectives which should be achieved for obtaining satisfactory quality. Therefore, video service providers should continuously monitor the network and the quality of the received video streams in order to detect visual degradations. Objective video quality metrics enable automatic measurement of perceived quality. Unfortunately, the most reliable metrics require access to both the original and the received video streams which makes them inappropriate for real-time monitoring. In this article, we present a novel no-reference bitstream-based visual quality impairment detector which enables real-time detection of visual degradations caused by network impairments. By only incorporating information extracted from the encoded bitstream, network impairments are classified as visible or invisible to the end-user. Our results show that impairment visibility can be classified with a high accuracy which enables real-time validation of the existing performance objectives