AccAnn: A New Subjective Assessment Methodology for Measuring Acceptability and Annoyance of Quality of Experience

User expectations have a crucial impact on the levels of quality of experience (QoE) that they consider acceptable or satisfying. Measuring acceptability and annoyance has mainly been performed in separate or multi-step experiments without any control over participants’ expectations. This paper introduces a simple methodology to obtain the information about both of the entities in a single step and compares several data processing strategies useful for results interpretation. A specifically designed subjective experiment, conducted on compressed videos, has shown that the multi-step procedures could be replaced by our proposed single-step approach, regardless of the viewing conditions, while the novel approach is significantly preferred by observers for its low time requirements and higher intuitiveness. The test has simultaneously proven that user expectations can be altered by the instructions and it is, therefore, possible to simulate different user profiles regardless of the participants’ real habits. The acceptability/annoyance experimental results are also used to benchmark the state-of-the-art objective video quality metrics in predicting acceptability/annoyance of QoE. A case study on the determination of the threshold of acceptability/annoyance for objective quality metrics is conducted, which can be served as a guideline for video streaming service providers.

[1]  G. Barnard,et al.  A New Test for 2 × 2 Tables , 1945, Nature.

[2]  M. Angela Sasse,et al.  Sharp or smooth?: comparing the effects of quantization vs. frame rate for streamed video , 2004, CHI '04.

[3]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[4]  R. Fisher On the Interpretation of χ 2 from Contingency Tables , and the Calculation of P Author , 2022 .

[5]  M. Angela Sasse,et al.  How low can you go? The effect of low resolutions on shot types in mobile TV , 2006, Multimedia Tools and Applications.

[6]  Stephen Wolf,et al.  Video Quality Model for Variable Frame Delay (VQM_VFD) , 2011 .

[7]  Dar'ya Khaustova,et al.  Objective assessment of stereoscopic video quality of 3DTV. (Évaluation objective de la qualité vidéo en TV 3D relief) , 2015 .

[8]  Manish Narwaria,et al.  Data Analysis in Multimedia Quality Assessment: Revisiting the Statistical Tests , 2017, IEEE Transactions on Multimedia.

[9]  Zhou Wang,et al.  Multiscale structural similarity for image quality assessment , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.

[10]  Marcus Barkowsky,et al.  Subjective assessment methodology for preference of experience in 3DTV , 2013, IVMSP 2013.

[11]  A. Parasuraman,et al.  Delivering quality service : balancing customer perceptions and expectations , 1990 .

[12]  Wei Song,et al.  Impact of automatic region-of-interest coding on perceived quality in mobile video , 2015, Multimedia Tools and Applications.

[13]  Lucjan Janowski,et al.  The Accuracy of Subjects in a Quality Experiment: A Theoretical Subject Model , 2015, IEEE Transactions on Multimedia.

[14]  Rik Van de Walle,et al.  Subjective Quality Assessment of Longer Duration Video Sequences Delivered Over HTTP Adaptive Streaming to Tablet Devices , 2014, IEEE Transactions on Broadcasting.

[15]  L. Thurstone,et al.  A low of comparative judgement , 1927 .

[16]  J. Astola,et al.  ON BETWEEN-COEFFICIENT CONTRAST MASKING OF DCT BASIS FUNCTIONS , 2007 .

[17]  M. Angela Sasse,et al.  The big picture on small screens delivering acceptable video quality in mobile TV , 2009, TOMCCAP.

[18]  Touradj Ebrahimi,et al.  A comprehensive database and subjective evaluation methodology for quality of experience in stereoscopic video , 2010, Electronic Imaging.

[19]  Wei Song,et al.  Saving bitrate vs. pleasing users: where is the break-even point in mobile video quality? , 2011, MM '11.

[20]  Prasant Mohapatra,et al.  Temporal quality assessment for mobile videos , 2012, Mobicom '12.

[21]  Q. Mcnemar Note on the sampling error of the difference between correlated proportions or percentages , 1947, Psychometrika.

[22]  Alan C. Bovik,et al.  Motion Tuned Spatio-Temporal Quality Assessment of Natural Videos , 2010, IEEE Transactions on Image Processing.

[23]  Kai Zeng,et al.  Display device-adapted video quality-of-experience assessment , 2015, Electronic Imaging.

[24]  Toon De Pessemier,et al.  Quantifying Subjective Quality Evaluations for Mobile Video Watching in a Semi-Living Lab Context , 2012, IEEE Transactions on Broadcasting.

[25]  Markus Rupp,et al.  Performance evaluation of mobile video quality estimators , 2007, 2007 15th European Signal Processing Conference.

[26]  Alexander Eichhorn,et al.  Pick Your Layers Wisely - A Quality Assessment of H.264 Scalable Video Coding for Mobile Devices , 2009, 2009 IEEE International Conference on Communications.

[27]  Miska M. Hannuksela,et al.  Does context matter in quality evaluation of mobile television? , 2008, Mobile HCI.

[28]  H. Knoche,et al.  Quality in Context-an ecological approach to assessing QoS for mobile TV , 2006 .

[29]  Zhi Li,et al.  Recover Subjective Quality Scores from Noisy Measurements , 2016, 2017 Data Compression Conference (DCC).

[30]  Margaret H. Pinson,et al.  A new standardized method for objectively measuring video quality , 2004, IEEE Transactions on Broadcasting.

[31]  Gustavo de Veciana,et al.  Video Quality Assessment on Mobile Devices: Subjective, Behavioral and Objective Studies , 2012, IEEE Journal of Selected Topics in Signal Processing.

[32]  John M. Carroll,et al.  Making Use: Scenario-Based Design of Human-Computer Interactions , 2000 .

[33]  Welch Bl THE GENERALIZATION OF ‘STUDENT'S’ PROBLEM WHEN SEVERAL DIFFERENT POPULATION VARLANCES ARE INVOLVED , 1947 .

[34]  R. Fisher On the Interpretation of χ2 from Contingency Tables, and the Calculation of P , 2018, Journal of the Royal Statistical Society Series A (Statistics in Society).

[35]  Wei Song,et al.  Exploration and Optimization of User Experience in Viewing Videos on a Mobile Phone , 2010, Int. J. Softw. Eng. Knowl. Eng..

[36]  Wei Song,et al.  Acceptability-Based QoE Models for Mobile Video , 2014, IEEE Transactions on Multimedia.

[37]  Daniele D. Giusto,et al.  Quality perception when streaming video on tablet devices , 2014, J. Vis. Commun. Image Represent..

[38]  Alan C. Bovik,et al.  Image information and visual quality , 2006, IEEE Trans. Image Process..

[39]  Andrew Catellier,et al.  Impact of mobile devices and usage location on perceived multimedia quality , 2012, 2012 Fourth International Workshop on Quality of Multimedia Experience.

[40]  Edward Cutrell,et al.  How bad is good enough?: exploring mobile video quality trade-offs for bandwidth-constrained consumers , 2012, NordiCHI.

[41]  Karel Fliegel,et al.  On the accuracy of objective image and video quality models: New methodology for performance evaluation , 2016, 2016 Eighth International Conference on Quality of Multimedia Experience (QoMEX).