Image Processing Reference
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d = 20, 40, 60, 80, 100, 200, 400, 800, 1000, and 2000 cm, in addition to test their objective and
subjective image quality by means of the PSNR and MSSIM metrics, respectively.
In Figures 8 and 9 , green functions denoted as F-pSQ are the quality metrics of perceptual
quantized images after applying α ( ν , r ), while blue functions denoted as I-pSQ are the quality
metrics of recovered images after applying
in CMU image data base. Thus, for either
gray-scale or color images, both PSNR and MSSIM estimations of the quantized image de-
crease regarding d , the longer the d the greater the image quality decline. When the image de-
coder recovers Q and it is perceptually inverse quantized, the quality barely varies and is close
to perceptually lossless, no mater the distance
FIGURE 8 PSNR and MSSIM assessments of compression of gray-scale images (Y Chan-
nel) of the CMU image database. Green functions denoted as F-pSQ are the quality metrics of
forward perceptual quantized images after applying α ( ν , r ), while blue functions denoted as I-
pSQ are the quality metrics of recovered images after applying
. (a) PSNR. (b)
MSSIM.
FIGURE 9 PSNR and MSSIM assessments of compression of Color Images of the CMU im-
age database. Green functions denoted as F-pSQ are the quality metrics of forward perceptu-
al quantized images after applying α ( ν , r ), while blue functions denoted as I-pSQ are the qual-
ity metrics of recovered images after applying
. (a) PSNR. (b) MSSIM
Results obtained both in the categorical subjective image quality image database [ 9 ] ( Figures
10 and 11 ) and image and video-communication image database [ 10 ] ( Figures 12 and 13 ) show
that for either gray-scale or color images, both PSNR and MSSIM estimations of the quantized
image Q decrease regarding d , the longer the d the greater the image quality decline. When the
image decoder recovers and it is perceptually inverse quantized, the quality barely varies
and is close to perceptually lossless, no mater the distance.
 
 
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