7,678 research outputs found
Crowdsourcing evaluation of high dynamic range compression
Crowdsourcing is becoming a popular cost effective alternative to lab-based evaluations for subjective quality assessment. However, crowd-based evaluations are constrained by the limited availability of display devices used by typical online workers, which makes the evaluation of high dynamic range (HDR) content a challenging task. In this paper, we investigate the feasibility of using low dynamic range versions of original HDR content obtained with tone mapping operators (TMOs) in crowdsourcing evaluations. We conducted two crowdsourcing experiments by employing workers from Microworkers platform. In the first experiment, we evaluate five HDR images encoded at different bit rates with the upcoming JPEG XT coding standard. To find best suitable TMO, we create eleven tone-mapped versions of these five HDR images by using eleven different TMOs. The crowdsourcing results are compared to a reference ground truth obtained via a subjective assessment of the same HDR images on a Dolby `Pulsar' HDR monitor in a laboratory environment. The second crowdsourcing evaluation uses semantic differentiators to better understand the characteristics of eleven different TMOs. The crowdsourcing evaluations show that some TMOs are more suitable for evaluation of HDR image compression
Analysis of Crowdsourced Sampling Strategies for HodgeRank with Sparse Random Graphs
Crowdsourcing platforms are now extensively used for conducting subjective
pairwise comparison studies. In this setting, a pairwise comparison dataset is
typically gathered via random sampling, either \emph{with} or \emph{without}
replacement. In this paper, we use tools from random graph theory to analyze
these two random sampling methods for the HodgeRank estimator. Using the
Fiedler value of the graph as a measurement for estimator stability
(informativeness), we provide a new estimate of the Fiedler value for these two
random graph models. In the asymptotic limit as the number of vertices tends to
infinity, we prove the validity of the estimate. Based on our findings, for a
small number of items to be compared, we recommend a two-stage sampling
strategy where a greedy sampling method is used initially and random sampling
\emph{without} replacement is used in the second stage. When a large number of
items is to be compared, we recommend random sampling with replacement as this
is computationally inexpensive and trivially parallelizable. Experiments on
synthetic and real-world datasets support our analysis
Exploring Outliers in Crowdsourced Ranking for QoE
Outlier detection is a crucial part of robust evaluation for crowdsourceable
assessment of Quality of Experience (QoE) and has attracted much attention in
recent years. In this paper, we propose some simple and fast algorithms for
outlier detection and robust QoE evaluation based on the nonconvex optimization
principle. Several iterative procedures are designed with or without knowing
the number of outliers in samples. Theoretical analysis is given to show that
such procedures can reach statistically good estimates under mild conditions.
Finally, experimental results with simulated and real-world crowdsourcing
datasets show that the proposed algorithms could produce similar performance to
Huber-LASSO approach in robust ranking, yet with nearly 8 or 90 times speed-up,
without or with a prior knowledge on the sparsity size of outliers,
respectively. Therefore the proposed methodology provides us a set of helpful
tools for robust QoE evaluation with crowdsourcing data.Comment: accepted by ACM Multimedia 2017 (Oral presentation). arXiv admin
note: text overlap with arXiv:1407.763
Beautiful and damned. Combined effect of content quality and social ties on user engagement
User participation in online communities is driven by the intertwinement of
the social network structure with the crowd-generated content that flows along
its links. These aspects are rarely explored jointly and at scale. By looking
at how users generate and access pictures of varying beauty on Flickr, we
investigate how the production of quality impacts the dynamics of online social
systems. We develop a deep learning computer vision model to score images
according to their aesthetic value and we validate its output through
crowdsourcing. By applying it to over 15B Flickr photos, we study for the first
time how image beauty is distributed over a large-scale social system.
Beautiful images are evenly distributed in the network, although only a small
core of people get social recognition for them. To study the impact of exposure
to quality on user engagement, we set up matching experiments aimed at
detecting causality from observational data. Exposure to beauty is
double-edged: following people who produce high-quality content increases one's
probability of uploading better photos; however, an excessive imbalance between
the quality generated by a user and the user's neighbors leads to a decline in
engagement. Our analysis has practical implications for improving link
recommender systems.Comment: 13 pages, 12 figures, final version published in IEEE Transactions on
Knowledge and Data Engineering (Volume: PP, Issue: 99
- …