Perceptual Spatial Uniformity Assessment of Projection Displays with a Calibrated Camera

Spatial uniformity is one of the most important image quality attributes in visual experience of displays. In conventional researches, spatial uniformity was mostly measured with a radiometer and its quality was assessed with non-reference image quality metrics. Cameras are cheaper than radiometers and they can provide accurate relative measurements if they are carefully calibrated. In this paper, we propose and implement a work-flow to use a calibrated camera as a relative acquisition device of intensity to measure the spatial uniformity of projection displays. The camera intensity transfer functions for every projected pixels are recovered, so we can produce multiple levels of linearized non-uniformity on the screen in the purpose of image quality assessment. The experiment results suggest that our work-flow works well. Besides, none of the frequently referred uniformity metrics correlate well with the perceptual results for all types of test images. The spatial non-uniformity is largely masked by the high frequency components in the displayed image content, and we should simulate the human visual system to ignore the non-uniformity that cannot be discriminated by human observers. The simulation can be implemented using models based on contrast sensitivity functions, contrast masking, etc.

[1]  Aditi Majumder,et al.  Perceptual photometric seamlessness in projection-based tiled displays , 2005, TOGS.

[2]  Joni-Kristian Kämäräinen,et al.  Mottling Assessment of Solid Printed Areas and Its Correlation to Perceived Uniformity , 2005, SCIA.

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

[4]  Mark D. Fairchild,et al.  A top down description of S-CIELAB and CIEDE2000 , 2003 .

[5]  Peter Y. Y. Ngai.  The Relationship between Luminance Uniformity and Brightness Perception , 2000 .

[6]  Ian Ashdown,et al.  A Proposed Lighting Quality Metric Based on Spatial Frequency Analysis , 1999 .

[7]  Phil J. Green.  A smoothness metric for colour transforms , 2008, Electronic Imaging.

[8]  W. Brent Seales,et al.  Multi-projector displays using camera-based registration , 1999, Proceedings Visualization '99 (Cat. No.99CB37067).

[9]  Richard I. Hartley.  Self-Calibration from Multiple Views with a Rotating Camera , 1994, ECCV.

[10]  Peter G. Engeldrum,et al.  Psychometric Scaling: A Toolkit for Imaging Systems Development , 2000 .

[11]  Douglas A. Kerr.  Derivation of the "Cosine Fourth" Law for Falloff of Illuminance Across a Camera Image , 2007 .

[12]  Ruigang Yang,et al.  Camera-based calibration techniques for seamless multiprojector displays , 2005, IEEE Transactions on Visualization and Computer Graphics.

[13]  I. Ashdown,et al.  Luminance Gradients: Photometric Analysis and Perceptual Reproduction , 1996 .

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

[15]  Jean-Baptiste Thomas,et al.  A Colorimetric Study of Spatial Uniformity in Projection Displays , 2009, CCIW.

[16]  Jon Y. Hardeberg,et al.  Image registration for quality assessment of projection displays , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[17]  Jean-Baptiste Thomas.  Colorimetric characterization of displays and multi-display systems , 2009 .

[18]  Jon Y. Hardeberg,et al.  Correcting projection display nonuniformity using a webcam , 2005, IS&T/SPIE Electronic Imaging.

[19]  H. Pande,et al.  A new wavelet-based instrumental method for measuring print mottle , 2004 .

[21]  Didier Stricker,et al.  Spatially uniform colors for projectors and tiled displays , 2007 .