Crowdsourcing has the potential to become a preferred tool to study image aesthetic appeal preferences of users. Nevertheless, some reliability issues still exist, partially due to the sometimes doubtful commitment of paid workers to perform the rating task properly. In this paper we compare the reliability in scoring image aesthetic appeal of both a paid and a volunteer crowd. We recruit our volunteers through Facebook and our paid users via Microworkers. We conclude that, whereas volunteer participants are more likely to leave the rating task unfinished, when they complete it they do so more reliably than paid users.
Research on Quality of Experience (QoE) heavily relies on subjective evaluations of media. An important aspect of QoE concerns modeling and quantifying the subjective notions of 'beauty' (aesthetic appeal) and 'something well-known' (content recognizability), which are both subject to cultural and social effects. Crowdsourcing, which allows employing people worldwide to perform short and simple tasks via online platforms, can be a great tool for performing subjective studies in a time and cost-effective way. On the other hand, the crowdsourcing environment does not allow for the degree of experimental control which is necessary to guarantee reliable subjective data. To validate the use of crowdsourcing for QoE assessments, in this paper, we evaluate aesthetic appeal and recognizability of images using the Microworkers crowdsourcing platform and compare the outcomes with more conventional evaluations conducted in a controlled lab environment. We find high correlation between crowdsourcing and lab scores for recognizability but not for aesthetic appeal, indicating that crowdsourcing can be used for QoE subjective assessments as long as the workers' tasks are designed with extreme care to avoid misinterpretations.
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