Welcome to vIQA’s documentation!

vIQA (volumetric Image Quality Assessment) provides an extensive assessment suite for image quality of 2D-images or 3D-volumes as a python package. Image Quality Assessment (IQA) is a field of research that aims to quantify the quality of an image. This is usually done by comparing the image to a reference image (full-reference metrics), but can also be done by evaluating the image without a reference (no-reference metrics). The reference image is usually the original image, but can also be another image that is considered to be of high quality. The comparison is done by calculating a metric that quantifies the difference between the two images or for the image itself. These quality metrics are used in various fields, such as medical imaging, computer vision, and image processing. For example the efficiency of image compression algorithms can be evaluated by comparing the compressed image to the original image. This package implements several metrics to compare two images or volumes using different IQA metrics. In addition, some metrics are implemented that can be used to evaluate a single image or volume.

The following metrics are implemented:

Implemented metrics

Metric	Name	Type	Dimensional behaviour	Colour Behaviour	Range (different/worst - identical/best)	Tested	Validated	Reference
PSNR	Peak Signal to Noise Ratio	FR	3D native	\(\checkmark\)	\([0, \infty)\)	\(\checkmark\)	\(\checkmark\)
RMSE	Root Mean Square Error	FR	3D native	\(\checkmark\)	\((\infty, 0]\)	\(\checkmark\)	\(\checkmark\)
UQI [*]	Universal Quality Index	FR	3D native	(\(\checkmark\)) [†]	\([-1, 1]\)	\(\times\)	(\(\checkmark\)) [‡]	[1]
SSIM	Structured Similarity	FR	3D native	(\(\checkmark\)) [§]	\([-1, 1]\) [¶]	\(\checkmark\)	\(\checkmark\)	[2]
MS-SSIM	Multi-Scale Structural Similarity	FR	3D slicing	Unknown	\([0, 1]\)	\(\times\)	\(\checkmark\)	[3]
FSIM	Feature Similarity	FR	3D slicing	\(\checkmark\)	\([0, 1]\)	\(\checkmark\)	\(\checkmark\)	[4]
VIFp	Visual Information Fidelity in pixel domain	FR	3D slicing	Unknown	\([0, \infty)\) [#]	\(\times\)	\(\times\) [♠]	[5]
VSI	Visual Saliency-based Index	FR	3D slicing	\(\checkmark\) [♥]	\([0, 1]\)	\(\times\)	\(\times\)	[6]
MAD	Most Apparent Distortion	FR	3D slicing		\([0, \infty)\)	\(\checkmark\)	\(\times\)	[7]
GSM	Gradient Similarity	FR	3D native or slicing		\([0, 1]\)	\(\times\)	\(\times\)	[8]
CNR	Contrast to Noise Ratio	NR	3D native		\([0, \infty)\)	\(\checkmark\)	\(\times\)	[9]
SNR	Signal to Noise Ratio	NR	3D native	\(\checkmark\)	\([0, \infty)\)	\(\checkmark\)	\(\times\)
Q-Measure	Q-Measure	NR	3D only [♦]	\(\times\)	\([0, \infty)\)	\(\times\)	\(\times\)	[10]

[*]

UQI is a special case of SSIM. Also see [2].

[†]

The metric is calculated channel-wise for color images. The values are then averaged after weighting.

[‡]

As UQI is a special case of SSIM, the validation of SSIM is also valid for UQI.

[§]

The metric is calculated channel-wise for color images. The values are then averaged after weighting.

[¶]

The range for SSIM is given as \([-1, 1]\), but is usually \([0, 1]\) in practice.

[#]

Normally \([0, 1]\), but can be higher than 1 for modified images with higher contrast than reference images.

[♠]

The calculated values for VIFp are probably not correct in this implementation. Those values should be treated with caution as further testing is required.

[♥]

The original metric supports RGB images only. This implementation can work with grayscale images by copying the luminance channel 3 times.

[♦]

The Q-Measure is a special metric designed for CT images. Therefore it only works with 3D volumes.

If you want to use the package, please have a look at the Getting started page. If you find bugs, please head to the Github issue tracker and open an issue.

Contact

If you have any questions, please contact the author at: .

Indices and tables

• Index

• Module Index

• Search Page

References

[1]

Wang, Z., & Bovik, A. C. (2002). A Universal Image Quality Index. IEEE SIGNAL PROCESSING LETTERS, 9(3). https://doi.org/10.1109/97.995823

[2] (1,2)

Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612. https://doi.org/10.1109/TIP.2003.819861

[3]

Wang, Z., Simoncelli, E. P., & Bovik, A. C. (2003). Multi-scale structural similarity for image quality assessment. The Thirty-Seventh Asilomar Conference on Signals, Systems & Computers, 1298–1402. https://doi.org/10.1109/ACSSC.2003.1292216

[4]

Zhang, L., Zhang, L., Mou, X., & Zhang, D. (2011). FSIM: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing, 20(8). https://doi.org/10.1109/TIP.2011.2109730

[5]

Sheikh, H. R., & Bovik, A. C. (2006). Image information and visual quality. IEEE Transactions on Image Processing, 15(2), 430–444. https://doi.org/10.1109/TIP.2005.859378

[6]

Zhang, L., Shen, Y., & Li, H. (2014). VSI: A visual saliency-induced index for perceptual image quality assessment. IEEE Transactions on Image Processing, 23(10), 4270–4281. https://doi.org/10.1109/TIP.2014.2346028

[7]

Larson, E. C., & Chandler, D. M. (2010). Most apparent distortion: full-reference image quality assessment and the role of strategy. Journal of Electronic Imaging, 19 (1), 011006. https://doi.org/10.1117/1.3267105

[8]

Liu, A., Lin, W., & Narwaria, M. (2012). Image quality assessment based on gradient similarity. IEEE Transactions on Image Processing, 21(4), 1500–1512. https://doi.org/10.1109/TIP.2011.2175935

[9]

Desai, N., Singh, A., & Valentino, D. J. (2010). Practical evaluation of image quality in computed radiographic (CR) imaging systems. Medical Imaging 2010: Physics of Medical Imaging, 7622, 76224Q. https://doi.org/10.1117/12.844640

[10]

Reiter, M., Weiß, D., Gusenbauer, C., Erler, M., Kuhn, C., Kasperl, S., & Kastner, J. (2014). Evaluation of a Histogram-based Image Quality Measure for X-ray computed Tomography. 5th Conference on Industrial Computed Tomography (iCT) 2014, 25-28 February 2014, Wels, Austria. e-Journal of Nondestructive Testing Vol. 19(6). https://www.ndt.net/?id=15715