2 research outputs found
Image Quality Assessment and Color Difference
An average healthy person does not perceive the world in just black and
white. Moreover, the perceived world is not composed of pixels and through
vision humans perceive structures. However, the acquisition and display systems
discretize the world. Therefore, we need to consider pixels, structures and
colors to model the quality of experience. Quality assessment methods use the
pixel-wise and structural metrics whereas color science approaches use the
patch-based color differences. In this work, we combine these approaches by
extending CIEDE2000 formula with perceptual color difference to assess image
quality. We examine how perceptual color difference-based metric (PCDM)
performs compared to PSNR, CIEDE2000, SSIM, MS-SSIM and CW-SSIM on the LIVE
database. In terms of linear correlation, PCDM obtains compatible results under
white noise (97.9%), Jpeg (95.9%) and Jp2k (95.6%) with an overall correlation
of 92.7%. We also show that PCDM captures color-based artifacts that can not be
captured by structure-based metrics.Comment: Paper: 5 pages, 5 figures, 2 tables, and Presentation [Ancillary
files
CSV: Image Quality Assessment Based on Color, Structure, and Visual System
This paper presents a full-reference image quality estimator based on color,
structure, and visual system characteristics denoted as CSV. In contrast to the
majority of existing methods, we quantify perceptual color degradations rather
than absolute pixel-wise changes. We use the CIEDE2000 color difference
formulation to quantify low-level color degradations and the Earth Mover's
Distance between color name descriptors to measure significant color
degradations. In addition to the perceptual color difference, CSV also contains
structural and perceptual differences. Structural feature maps are obtained by
mean subtraction and divisive normalization, and perceptual feature maps are
obtained from contrast sensitivity formulations of retinal ganglion cells. The
proposed quality estimator CSV is tested on the LIVE, the Multiply Distorted
LIVE, and the TID 2013 databases, and it is always among the top two performing
quality estimators in terms of at least ranking, monotonic behavior or
linearity.Comment: 31 pages, 9 figures, 7 table