Comprehensive model for predicting perceptual image quality of smart mobile devices.

An image quality model for smart mobile devices was proposed based on visual assessments of several image quality attributes. A series of psychophysical experiments were carried out on two kinds of smart mobile devices, i.e., smart phones and tablet computers, in which naturalness, colorfulness, brightness, contrast, sharpness, clearness, and overall image quality were visually evaluated under three lighting environments via categorical judgment method for various application types of test images. On the basis of Pearson correlation coefficients and factor analysis, the overall image quality could first be predicted by its two constituent attributes with multiple linear regression functions for different types of images, respectively, and then the mathematical expressions were built to link the constituent image quality attributes with the physical parameters of smart mobile devices and image appearance factors. The procedure and algorithms were applicable to various smart mobile devices, different lighting conditions, and multiple types of images, and performance was verified by the visual data.

[1]  Jeonghun Kwak,et al.  Improvement of electron injection in inverted bottom-emission blue phosphorescent organic light emitting diodes using zinc oxide nanoparticles , 2010 .

[2]  Ming Ronnier Luo,et al.  Image quality evaluation for smart-phone displays at lighting levels of indoor and outdoor conditions , 2012 .

[3]  Guihua Cui,et al.  Measurement of the relationship between perceived and computed color differences. , 2007, Journal of the Optical Society of America. A, Optics, image science, and vision.

[4]  Andrei Khrennikov,et al.  Front Matter: Volume 7421 , 2009 .

[5]  Michael R. Pointer,et al.  Investigation of Large Display Color Image Appearance I: Important Factors Affecting Perceived Quality , 2008 .

[6]  Sn Yendrikhovskij,et al.  Color reproduction and the naturalness constraint , 1999 .

[7]  H. Ridder,et al.  Chroma variations and perceived quality of color images of natural scenes , 1997 .

[8]  M. Luo,et al.  Uniform colour spaces based on CIECAM02 colour appearance model , 2006 .

[9]  Mark E. Gorzynski,et al.  CRT colorimetry. part I: Theory and practice , 1993 .

[10]  Achintya K. Bhowmik,et al.  Mobile Displays: Technology and Applications , 2008 .

[11]  L. Thurstone.  A law of comparative judgment. , 1994 .

[12]  Mark D. Fairchild,et al.  Perceived Image Contrast and Observer Preference I. The Effects of Lightness, Chroma, and Sharpness Manipulations on Contrast Perception , 2003, Journal of Imaging Science and Technology.

[13]  Haifeng Li,et al.  Investigation of perceptual attributes for mobile display image quality , 2013 .

[14]  R.W.G. Hunt,et al.  The Reproduction of Colour: Hunt/Reproduction of Colour , 2005 .

[15]  K. Plataniotis,et al.  Color Image Processing : Methods and Applications , 2006 .

[16]  Youn Jin Kim,et al.  Factors affecting the psychophysical image quality evaluation of mobile phone displays: the case of transmissive liquid-crystal displays. , 2008, Journal of the Optical Society of America. A, Optics, image science, and vision.

[17]  Guihua Cui,et al.  Colour naturalness metric for evaluating image quality of mobile displays , 2009, Electronic Imaging.

[18]  Changjun Li,et al.  Applying CIECAM02 for Mobile Display Viewing Conditions , 2007, Color Imaging Conference.

[19]  Anthony Calabria,et al.  Perceived image contrast and observer preference , 2000 .

[20]  P. G. Engeldrum.  A framework for image quality models , 1995 .

[21]  Haisong Xu,et al.  Impacts of appearance parameters on perceived image quality for mobile-phone displays , 2014 .