Benchmarking handheld graphical user interface: Smoothness quality of experience

Abstract With the rapid growth of smartphones in the market, the smoothness of smartphones, how quickly and well smartphones react to a user’s input, becomes a crucial factor consumers consider when making buying decisions. However, there is no benchmark for comparing the smoothness of one phone against another. In this paper, a handheld smoothness evaluation over regression (HSER) model was developed to make a fair evaluation. A video was first made and the several key indexes were extracted to represent behavior-based smoothness quality of services (BQoS). We built up a relationship between BQoS and behavior-based smoothness quality of experience (BQoE), and converted BQoE to handheld smoothness quality of experience. Our experiment results show that maximal frame interval and number of frame intervals are the two most critical indexes that indicate smoothness. The proposed HSER model is able to fairly evaluate the smoothness of smartphones because the error rate of the HSER model is less than 9% for a single behavior.

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

[2]  Alexandre Fleury,et al.  Evaluating user preferences for video transfer methods from a mobile device to a TV screen , 2013, Pervasive Mob. Comput..

[3]  Sankar K. Pal,et al.  A review on image segmentation techniques , 1993, Pattern Recognit..

[4]  Jakob Nielsen,et al.  Usability engineering , 1997, The Computer Science and Engineering Handbook.

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

[6]  Hannu Verkasalo.  Analysis of Smartphone User Behavior , 2010, 2010 Ninth International Conference on Mobile Business and 2010 Ninth Global Mobility Roundtable (ICMB-GMR).

[7]  Lansun Shen,et al.  Motion activity based wireless video quality perceptual metric , 2001, Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing. ISIMP 2001 (IEEE Cat. No.01EX489).

[8]  Silvio Savarese,et al.  MEVBench: A mobile computer vision benchmarking suite , 2011, 2011 IEEE International Symposium on Workload Characterization (IISWC).

[9]  Sangkyum Kim,et al.  ADP: automated diagnosis of performance pathologies using hardware events , 2012, SIGMETRICS '12.

[10]  Hyeon-Ju Yoon.  A Study on the Performance of Android Platform , 2012 .

[11]  Kuan-Ta Chen,et al.  Effect of Network Quality on Player Departure Behavior in Online Games , 2009, IEEE Transactions on Parallel and Distributed Systems.

[12]  Guillermo Sapiro,et al.  Navier-stokes, fluid dynamics, and image and video inpainting , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[13]  Chin-Laung Lei,et al.  Online game QoE evaluation using paired comparisons , 2010, 2010 IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR 2010).

[14]  Yuan-Cheng Lai,et al.  Improving the Accuracy of Automated GUI Testing for Embedded Systems , 2014, IEEE Software.

[15]  Abdul Hameed,et al.  A Decision-Tree-Based Perceptual Video Quality Prediction Model and Its Application in FEC for Wireless Multimedia Communications , 2016, IEEE Transactions on Multimedia.

[16]  Serge J. Belongie,et al.  SD-VBS: The San Diego Vision Benchmark Suite , 2009, 2009 IEEE International Symposium on Workload Characterization (IISWC).

[17]  Ahmad Rahmati,et al.  Pervasive and Mobile Computing , 2009 .

[18]  Chun-Ying Huang,et al.  Quantifying Skype user satisfaction , 2006, SIGCOMM.

[19]  Tei-Wei Kuo,et al.  Resource Reservation and Enforcement for Framebuffer-Based Devices , 2003, RTCSA.

[20]  M. Ibrahim,et al.  Performance measurement of VoIP over WiMAX 4G network , 2012, 2012 IEEE 8th International Colloquium on Signal Processing and its Applications.

[21]  Robert B. Miller,et al.  Response time in man-computer conversational transactions , 1899, AFIPS Fall Joint Computing Conference.

[22]  Lingfen Sun,et al.  QoE Prediction Model and its Application in Video Quality Adaptation Over UMTS Networks , 2012, IEEE Transactions on Multimedia.