Image Processing Reference

In-Depth Information

Table 2

Average Objective Measurement Data of Upsampled Depth Maps (PSNR unit: dB)

Depth Map BLU BCU BU

JBU

VBU ABU DTBU

PSNR

35.85 35.71 35.64 34.15 35.64 33.16 34.86

E-PSNR

23.68 23.55 23.66 22.82 23.38 20.97 22.93

NE-PSNR

38.07 37.94 37.78 37.50 37.93 35.43 36.92

Sharpness

39.5

42.2

49.51 49.09 31.92 88.31 68.14

Blur

8.48

11.38 10.29 10.87 10.51 9.00

9.89

SSIM

0.976 0.955 0.975 0.956 0.971 0.962 0.972

VIF

0.518 0.539 0.424 0.422 0.478 0.398 0.438

BIQI

57.8

66.34 63.11 32.81 41.94 29.15 72

NIQE

15.95 13.11 13.94 11.82 12.47 13.41 13.82

The 3D perception grades of upsampling methods in
Table 1
are based on 3D visual discom-

fort.

Quality scores of upsampled depth maps obtained from each IQA metric are considered

as a group of seven samples. All values are normalized by scaling between 0 and 1 and the

similarity of samples distribution in each IQA group is compared with subjective evaluation

samples group using Pearson, Spearman, and Kendall correlation coefficients.
Table 3
shows

the correlation results.

Table 3

Pearson, Spearman, and Kendall Correlation Coefficients Between Subjective and Objective

Measurements

NE-

PSNR

Blur

Metric

PSNR

E-PSNR

Sharpness

SSIM

VIF

BIQI

NIQE

Pearson

0.528

0.608

0.554

− 0.522

0.273

0.505

0.019

− 0.34

0.132

Spearman

0.035

0.142

0.035

− 0.321

0.142

0.357

0.107

− 0.321

0.107

Kendall

0.047

0.142

0.047

− 0.142

0.047

0.142

0.142

− 0.238

0.142

Before evaluating the strength of correlation using different correlation coefficients, it is

worth mentioning that Pearson's correlation coefficient takes into account both the number

and degree of concordances and discordances, whereas Kendall's tau correlation coefficient

shows only the number of concordances and discordances. Spearman's correlation is in

between of the Pearson's and Kendall's, reflecting the degree of concordances and discord-

ances on the rank scale. The disadvantage of Pearson is the sensitivity to outliers (an observa-

tion that is numerically distant from the rest of the data). In this case, Spearman and Kendall

are less sensitive to outliers and preferable.

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