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.

[1]  Phuoc Tran-Gia,et al.  Quantification of YouTube QoE via Crowdsourcing , 2011, 2011 IEEE International Symposium on Multimedia.

[2]  Sung-Hee Kim,et al.  How to filter out random clickers in a crowdsourcing-based study? , 2012, BELIV '12.

[3]  Christian Timmerer,et al.  A web based subjective evaluation platform , 2013, 2013 Fifth International Workshop on Quality of Multimedia Experience (QoMEX).

[4]  Sebastian Kraft,et al.  BeaqleJS : HTML 5 and JavaScript based Framework for the Subjective Evaluation of Audio Quality , 2014 .

[5]  Hari Kalva,et al.  Assessing internet video quality using crowdsourcing , 2013, CrowdMM '13.

[6]  Phuoc Tran-Gia,et al.  Best Practices for QoE Crowdtesting: QoE Assessment With Crowdsourcing , 2014, IEEE Transactions on Multimedia.

[7]  Truong Q. Nguyen,et al.  Tally: A web-based subjective testing tool , 2013, 2013 Fifth International Workshop on Quality of Multimedia Experience (QoMEX).

[8]  Chin-Laung Lei,et al.  A crowdsourceable QoE evaluation framework for multimedia content , 2009, ACM Multimedia.

[9]  Qingming Huang,et al.  Online crowdsourcing subjective image quality assessment , 2012, ACM Multimedia.

[10]  Pavel Korshunov,et al.  Crowdsourcing-based multimedia subjective evaluations: a case study on image recognizability and aesthetic appeal , 2013, CrowdMM '13.

[11]  Xi Liu,et al.  Smart phone based online QoE assessment for end-to-end multimedia services on 3G mobile Internet , 2012, 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet).

[12]  Touradj Ebrahimi,et al.  Crowdsourcing-based evaluation of privacy in HDR images , 2014, Photonics Europe.

[13]  Michael Seufert,et al.  Crowdsourcing 2.0: Enhancing execution speed and reliability of web-based QoE testing , 2014, 2014 IEEE International Conference on Communications (ICC).

[14]  Christian Keimel,et al.  QualityCrowd — A framework for crowd-based quality evaluation , 2012, 2012 Picture Coding Symposium.

[15]  Dinei A. F. Florêncio,et al.  Crowdsourcing subjective image quality evaluation , 2011, 2011 18th IEEE International Conference on Image Processing.

[16]  Chin-Laung Lei,et al.  Crowdsourcing Multimedia QoE Evaluation: A Trusted Framework , 2013, IEEE Transactions on Multimedia.

[17]  Qingming Huang,et al.  HodgeRank on Random Graphs for Subjective Video Quality Assessment , 2012, IEEE Transactions on Multimedia.

[18]  Cha Zhang,et al.  CROWDMOS: An approach for crowdsourcing mean opinion score studies , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[19]  Chin-Laung Lei,et al.  Quadrant of euphoria: a crowdsourcing platform for QoE assessment , 2010, IEEE Network.

[20]  Sugato Chakravarty,et al.  Methodology for the subjective assessment of the quality of television pictures , 1995 .

[21]  Touradj Ebrahimi,et al.  Crowd-based quality assessment of multiview video plus depth coding , 2014, 2014 IEEE International Conference on Image Processing (ICIP).