Application of PQS for Image Quality Analysis at Visible Spectral Imaging

An image quality investigation in visible spectral imaging was performed. Spectral images were simulated using different number of imaging channels, wavelength steps, and noise levels based on practical spectral imaging systems. A mean opinion score (MOS) was determined from a subjective visual assessment scale experiment for image quality of spectral images rendered to a three-channel display. A set of partial image distortion measures, including color difference for color images, were defined based on classified and quantified actual distortions produced by spectral imaging systems. Principal components analysis was then carried out to quantify the correlation between distortion factors. Finally, a multiple regression analysis (MRA) was carried out between the principal component vectors and the measured MOS values to determine the picture quality scale (PQS). The obtained quality metric, PQS, had high correlation with the subjective measure, MOS. The importance of contribution of the distortion factors in the image quality metric was also evaluated.

[1]  Qun Sun,et al.  Image Quality for Visible Spectral Imaging , 2003, PICS.

[2]  Brian A. Wandell,et al.  A spatial extension of CIELAB for digital color‐image reproduction , 1997 .

[3]  Peter G. J. Barten,et al.  Evaluation of Subjective Image Quality with the Square Root Integral Method , 1990, Applied Vision.

[4]  Lindsay W. MacDonald,et al.  Colour Difference Metrics and Image Sharpness , 2000, Color Imaging Conference.

[5]  Ron Gershon,et al.  Measurement and Analysis of Object Reflectance Spectra , 1994 .

[6]  P.G.J. Barten The effects of picture size and definition on perceived image quality , 1989 .

[7]  Peter D. Burns,et al.  ANALYSIS OF IMAGE NOISE IN MULTISPECTRAL COLOR ACQUISITION , 1997 .

[8]  Mark D. Fairchild,et al.  A Revision of CIECAM97s for Practical Applications , 2001 .

[9]  Qun Sun,et al.  New procedure for capturing spectral images of human portraiture , 2002, Other Conferences.

[10]  Di-yuan Tzeng Spectral-based color separation algorithm development for multiple-ink color reproduction , 1999 .

[11]  Bernhard Hill Optimization of total multispectral imaging systems: best spectral match versus least observer metamerism , 2002, Other Conferences.

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

[13]  Mark D. Fairchild,et al.  Compare and Contrast: Perceived Contrast of Color Images , 2002, CIC.

[14]  V. Ralph Algazi,et al.  Objective picture quality scale (PQS) for image coding , 1998, IEEE Trans. Commun..