Czenakowski distance

The Czenakowski distance (sometimes shortened as CZD) is a per-pixel quality metric that estimates quality or similarity by measuring differences between pixels. Because it compares vectors with strictly non-negative elements, it is often used to compare colored images, as color values cannot be negative. This different approach has a better correlation with subjective quality assessment than PSNR.[citation needed]

Definition

Androutsos et al. give the Czenakowski coefficient as follows:[1]

d z ( i , j ) = 1 2 k = 1 p min ( x i k ,   x j k ) k = 1 p ( x i k + x j k ) {\displaystyle d_{z}(i,j)=1-{\frac {2\sum _{k=1}^{p}{\text{min}}(x_{ik},\ x_{jk})}{\sum _{k=1}^{p}(x_{ik}+x_{jk})}}}

Where a pixel x i {\displaystyle x_{i}} is being compared to a pixel x j {\displaystyle x_{j}} on the k-th band of color – usually one for each of red, green and blue.

For a pixel matrix of size M × N {\displaystyle M\times N} , the Czenakowski coefficient can be used in an arithmetic mean spanning all pixels to calculate the Czenakowski distance as follows:[2][3]

1 M N i = 0 M 1 j = 0 N 1 ( 1 2 k = 1 3 min ( A k ( i , j ) ,   B k ( i , j ) ) k = 1 3 ( A k ( i , j ) + B k ( i , j ) ) ) {\displaystyle {\frac {1}{MN}}\sum _{i=0}^{M-1}\sum _{j=0}^{N-1}{\begin{pmatrix}1-{\frac {2\sum _{k=1}^{3}{\text{min}}(A_{k}(i,j),\ B_{k}(i,j))}{\sum _{k=1}^{3}(A_{k}(i,j)+B_{k}(i,j))}}\end{pmatrix}}}

Where A k ( i , j ) {\displaystyle A_{k}(i,j)} is the (i, j)-th pixel of the k-th band of a color image and, similarly, B k ( i , j ) {\displaystyle B_{k}(i,j)} is the pixel that it is being compared to.

Uses

In the context of image forensics – for example, detecting if an image has been manipulated –, Rocha et al. report the Czenakowski distance is a popular choice for Color Filter Array (CFA) identification.[2]

References

  1. ^ Androutsos, D.; Plataniotiss, K.N.; Venetsanopoulos, A.N. (1998). "Distance measures for color image retrieval". Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269). Vol. 2. pp. 770–774. doi:10.1109/ICIP.1998.723652. ISBN 0-8186-8821-1. S2CID 10134889. Closed access icon
  2. ^ a b Rocha, Anderson; Scheirer, Walter; Boult, Terrance; Goldenstein, Siome (October 2011). "Vision of the unseen". ACM Computing Surveys. 43 (4): 1–42. doi:10.1145/1978802.1978805. ISSN 0360-0300. S2CID 113533. Closed access icon
  3. ^ Kale, K. V.; Mehrotra, S. C.; R. R. Manza, R. R., eds. (2007). Advances in Computer Vision and Information Technology. New Delhi, India: I.K. International Pvt. Ltd. p. 91. ISBN 978-81-89866-74-7.
  • v
  • t
  • e