4,853 research outputs found
Survey of Web-based Crowdsourcing Frameworks for Subjective Quality Assessment
The popularity of the crowdsourcing for performing various tasks online increased significantly in the past few years. The low cost and flexibility of crowdsourcing, in particular, attracted researchers in the field of subjective multimedia evaluations and Quality of Experience (QoE). Since online assessment of multimedia content is challenging, several dedicated frameworks were created to aid in the designing of the tests, including the support of the testing methodologies like ACR, DCR, and PC, setting up the tasks, training sessions, screening of the subjects, and storage of the resulted data. In this paper, we focus on the web-based frameworks for multimedia quality assessments that support commonly used crowdsourcing platforms such as Amazon Mechanical Turk and Microworkers. We provide a detailed overview of the crowdsourcing frameworks and evaluate them to aid researchers in the field of QoE assessment in the selection of frameworks and crowdsourcing platforms that are adequate for their experiments
Towards speech quality assessment using a crowdsourcing approach: evaluation of standardized methods
Subjective speech quality assessment has traditionally been carried out in laboratory environments under controlled conditions. With the advent of crowdsourcing platforms tasks, which need human intelligence, can be resolved by crowd workers over the Internet. Crowdsourcing also offers a new paradigm for speech quality assessment, promising higher ecological validity of the quality judgments at the expense of potentially lower reliability. This paper compares laboratory-based and crowdsourcing-based speech quality assessments in terms of comparability of results and efficiency. For this purpose, three pairs of listening-only tests have been carried out using three different crowdsourcing platforms and following the ITU-T Recommendation P.808. In each test, listeners judge the overall quality of the speech sample following the Absolute Category Rating procedure. We compare the results of the crowdsourcing approach with the results of standard laboratory tests performed according to the ITU-T Recommendation P.800. Results show that in most cases, both paradigms lead to comparable results. Notable differences are discussed with respect to their sources, and conclusions are drawn that establish practical guidelines for crowdsourcing-based speech quality assessment
Crowdsourcing in Computer Vision
Computer vision systems require large amounts of manually annotated data to
properly learn challenging visual concepts. Crowdsourcing platforms offer an
inexpensive method to capture human knowledge and understanding, for a vast
number of visual perception tasks. In this survey, we describe the types of
annotations computer vision researchers have collected using crowdsourcing, and
how they have ensured that this data is of high quality while annotation effort
is minimized. We begin by discussing data collection on both classic (e.g.,
object recognition) and recent (e.g., visual story-telling) vision tasks. We
then summarize key design decisions for creating effective data collection
interfaces and workflows, and present strategies for intelligently selecting
the most important data instances to annotate. Finally, we conclude with some
thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in
Computer Graphics and Vision, 201
- …