Who's Better? Who's Best? Pairwise Deep Ranking for Skill Determination

We present a method for assessing skill from video, applicable to a variety of tasks, ranging from surgery to drawing and rolling pizza dough. We formulate the problem as pairwise (who's better?) and overall (who's best?) ranking of video collections, using supervised deep ranking. We propose a novel loss function that learns discriminative features when a pair of videos exhibit variance in skill, and learns shared features when a pair of videos exhibit comparable skill levels. Results demonstrate our method is applicable across tasks, with the percentage of correctly ordered pairs of videos ranging from 70% to 83% for four datasets. We demonstrate the robustness of our approach via sensitivity analysis of its parameters. We see this work as effort toward the automated organization of how-to video collections and overall, generic skill determination in video.Comment: CVPR 201

Comparing temporal behavior of fast objective video quality measures on a large-scale database

In many application scenarios, video quality assessment is required to be fast and reasonably accurate. The characterisation of objective algorithms by subjective assessment is well established but limited due to the small number of test samples. Verification using large-scale objectively annotated databases provides a complementary solution. In this contribution, three simple but fast measures are compared regarding their agreement on a large-scale database. In contrast to subjective experiments, not only sequence-wise but also framewise agreement can be analyzed. Insight is gained into the behavior of the measures with respect to 5952 different coding configurations of High Efficiency Video Coding (HEVC). Consistency within a video sequence is analyzed as well as across video sequences. The results show that the occurrence of discrepancies depends mostly on the configured coding structure and the source content. The detailed observations stimulate questions on the combined usage of several video quality measures for encoder optimization

School-Based Telemedicine Interventions for Asthma: A Systematic Review.

BackgroundSchool health systems are increasingly investing in telemedicine platforms to address acute and chronic illnesses. Asthma, the most common chronic illness in childhood, is of particular interest given its high burden on school absenteeism.ObjectiveConduct a systematic review evaluating impact of school-based telemedicine programs on improving asthma-related outcomes.Data sourcesPubMed, Cochrane CENTRAL, CINAHL, ERIC, PsycINFO, Embase, and Google Scholar.Study eligibility criteriaOriginal research, including quasi-experimental studies, without restriction on the type of telemedicine.ParticipantsSchool-aged pediatric patients with asthma and their families.InterventionsSchool-based telemedicine.Study appraisal and synthesis methodsTwo authors independently screened each abstract, conducted full-text review, assessed study quality, and extracted information. A third author resolved disagreements.ResultsOf 371 articles identified, 7 were included for the review. Outcomes of interest were asthma symptom-free days, asthma symptom frequency, quality of life, health care utilization, school absences, and spirometry. Four of 7 studies reported significant increases in symptom-free days and/or decrease in symptom frequency. Five of 6 reported increases in at least one quality-of-life metric, 2 of 7 reported a decrease in at least 1 health care utilization metric, 1 of 3 showed reductions in school absences, and 1 of 2 reported improvements in spirometry measures.LimitationsVariability in intervention designs and outcome measures make comparisons and quantitative analyses across studies difficult. Only 2 of 7 studies were randomized controlled trials.Conclusions and implications of key findingsHigh-quality evidence supporting the use of school-based telemedicine programs to improve patient outcomes is limited. While available evidence suggests benefit, only 2 comparative trials were identified, and the contribution of telemedicine to these studies' results is unclear

PEA265: Perceptual Assessment of Video Compression Artifacts

The most widely used video encoders share a common hybrid coding framework that includes block-based motion estimation/compensation and block-based transform coding. Despite their high coding efficiency, the encoded videos often exhibit visually annoying artifacts, denoted as Perceivable Encoding Artifacts (PEAs), which significantly degrade the visual Qualityof- Experience (QoE) of end users. To monitor and improve visual QoE, it is crucial to develop subjective and objective measures that can identify and quantify various types of PEAs. In this work, we make the first attempt to build a large-scale subjectlabelled database composed of H.265/HEVC compressed videos containing various PEAs. The database, namely the PEA265 database, includes 4 types of spatial PEAs (i.e. blurring, blocking, ringing and color bleeding) and 2 types of temporal PEAs (i.e. flickering and floating). Each containing at least 60,000 image or video patches with positive and negative labels. To objectively identify these PEAs, we train Convolutional Neural Networks (CNNs) using the PEA265 database. It appears that state-of-theart ResNeXt is capable of identifying each type of PEAs with high accuracy. Furthermore, we define PEA pattern and PEA intensity measures to quantify PEA levels of compressed video sequence. We believe that the PEA265 database and our findings will benefit the future development of video quality assessment methods and perceptually motivated video encoders.Comment: 10 pages,15 figures,4 table