1,637 research outputs found
Large-Scale Study of Perceptual Video Quality
The great variations of videographic skills, camera designs, compression and
processing protocols, and displays lead to an enormous variety of video
impairments. Current no-reference (NR) video quality models are unable to
handle this diversity of distortions. This is true in part because available
video quality assessment databases contain very limited content, fixed
resolutions, were captured using a small number of camera devices by a few
videographers and have been subjected to a modest number of distortions. As
such, these databases fail to adequately represent real world videos, which
contain very different kinds of content obtained under highly diverse imaging
conditions and are subject to authentic, often commingled distortions that are
impossible to simulate. As a result, NR video quality predictors tested on
real-world video data often perform poorly. Towards advancing NR video quality
prediction, we constructed a large-scale video quality assessment database
containing 585 videos of unique content, captured by a large number of users,
with wide ranges of levels of complex, authentic distortions. We collected a
large number of subjective video quality scores via crowdsourcing. A total of
4776 unique participants took part in the study, yielding more than 205000
opinion scores, resulting in an average of 240 recorded human opinions per
video. We demonstrate the value of the new resource, which we call the LIVE
Video Quality Challenge Database (LIVE-VQC), by conducting a comparison of
leading NR video quality predictors on it. This study is the largest video
quality assessment study ever conducted along several key dimensions: number of
unique contents, capture devices, distortion types and combinations of
distortions, study participants, and recorded subjective scores. The database
is available for download on this link:
http://live.ece.utexas.edu/research/LIVEVQC/index.html
Measuring objective and subjective well-being: dimensions and data sources
AbstractWell-being is an important value for people's lives, and it could be considered as an index of societal progress. Researchers have suggested two main approaches for the overall measurement of well-being, the objective and the subjective well-being. Both approaches, as well as their relevant dimensions, have been traditionally captured with surveys. During the last decades, new data sources have been suggested as an alternative or complement to traditional data. This paper aims to present the theoretical background of well-being, by distinguishing between objective and subjective approaches, their relevant dimensions, the new data sources used for their measurement and relevant studies. We also intend to shed light on still barely unexplored dimensions and data sources that could potentially contribute as a key for public policing and social development
Crowdsourcing Emotions in Music Domain
An important source of intelligence for music emotion recognition today comes from user-provided
community tags about songs or artists. Recent crowdsourcing approaches such as harvesting social tags,
design of collaborative games and web services or the use of Mechanical Turk, are becoming popular in
the literature. They provide a cheap, quick and efficient method, contrary to professional labeling of songs
which is expensive and does not scale for creating large datasets. In this paper we discuss the viability of
various crowdsourcing instruments providing examples from research works. We also share our own
experience, illustrating the steps we followed using tags collected from Last.fm for the creation of two
music mood datasets which are rendered public. While processing affect tags of Last.fm, we observed that
they tend to be biased towards positive emotions; the resulting dataset thus contain more positive songs
than negative ones
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