A platform for subjective image quality evaluation on mobile devices

From content creators, to content distributors and consumer electronics industry, image quality is an important consideration both from an engineering and artistic point of view. In recent years, with the shift towards mobile multimedia, it is becoming increasingly important to be able to assess the quality of images displayed on mobile devices. In this paper we present an iOS app that makes it easy to perform subjective image quality evaluation on mobile devices. The app implements two-alternative forced choice (2AFC) test methodology, and further reduces the cognitive load of subjects performing the test by providing an easy-to-use, natural interface using the mobile device's touch screen. A simple subjective experiment comparing two tone mapping operators for High Dynamic Range (HDR) images is performed to illustrate the effectiveness of the app.

[1]  Jari Takatalo,et al.  Evaluation of stereoscopic image quality for mobile devices using interpretation based quality methodology , 2009, Electronic Imaging.

[2]  Rafal Mantiuk,et al.  Display adaptive tone mapping , 2008, ACM Trans. Graph..

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

[4]  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.

[5]  Weisi Lin,et al.  Perceptual visual quality metrics: A survey , 2011, J. Vis. Commun. Image Represent..

[6]  Grey Giddins Statistics , 2016, The Journal of hand surgery, European volume.

[7]  Panos Nasiopoulos,et al.  3D video quality metric for mobile applications , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[8]  Daniele D. Giusto,et al.  QoE assessment of multimedia video consumption on tablet devices , 2012, 2012 IEEE Globecom Workshops.

[9]  Ivan V. Bajic,et al.  Saliency-Aware Video Compression , 2014, IEEE Transactions on Image Processing.

[10]  James J. Filliben,et al.  NIST/SEMATECH e-Handbook of Statistical Methods; Chapter 1: Exploratory Data Analysis , 2003 .

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

[12]  Weisi Lin,et al.  Gradient-weighted structural similarity for image quality assessments , 2015, 2015 IEEE International Symposium on Circuits and Systems (ISCAS).

[13]  Parvaneh Saeedi,et al.  Good-looking green images , 2011, 2011 18th IEEE International Conference on Image Processing.

[14]  Erik Reinhard,et al.  Photographic tone reproduction for digital images , 2002, ACM Trans. Graph..