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The Image Quality Assessment or IQA for short is a library that implements the most popular algorithms in order to generate image / video quality metrics.
The following algorithms are implemented in the current release:
· MSE,
· PSNR,
· SSIM,
· MS-SSIM,
· MS-SSIM*.

 

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IQA is a technique used for evaluating the quality of images and video streams. Its objective is to evaluate the perceptual quality of an image or video, in a way that is not dependent on the reference images. This approach is different from the analysis and evaluation of the fidelity of the original image, for which the objective is to maximize the similarity with the reference image.
IQA typically involves the following measurements:
– MSE: the mean square error (MSE) is the mean of the squares of the differences between pixel values in the image, compared with the pixel values that would be found in an ideal image with perfect fidelity to the reference.
– PSNR: it is the ratio of the reference signal power to the MSE.
– SSIM: the structural similarity measure, or SSIM, is a similarity metric that reflects the structural similarity between two images. The SSIM also compares the image to a reference and uses the MSE as the objective measure.
– MS-SSIM: the multiscale structural similarity metric SSIM, or MS-SSIM, it provides a similarity measurement between two images. The MS-SSIM is particularly well-suited to assessing details with different spatial frequencies.
* In the case of MS-SSIM the images have the same dimensions as the input images.
You can use the IQA functions as follows:

Define a struct containing the values to be used as inputs and outputs:

/// /// Input struct: Contains the values that will be used in the operation./// They must be specified as struct./// /// /// See the documentation for more information about the inputs and outputs/// of these functions./// struct ImageQualityMetric { public float ImageSimilarity; /// /// Output values: The value of the metric, in float, with respect to the original image. /// /// /// See the documentation for more information about the inputs and outputs/// of these functions./// public float ImageMSE; /// /// Output values: The root mean square error between the original and /// transformed images./// /// /// See the documentation for more information about the inputs and outputs/// of these functions./// public float ImagePSNR; /// /// Output values: The mean square error between the two images./// ///

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The IQA is a statistical metric and is able to check the quality of a picture, a video or a set of pictures.
The IQA is based on a set of statistical hypothesis related to the content and the structure of the image, therefore it is able to classify this image in a certain quality class that typically corresponds to a certain minimum of distortion.
The IQA is based on a set of parameters that are typically related to the visual perception of the observer. The perception of the quality is directly dependent on those parameters and, if those parameters are properly defined, there is a relation between the parameter and the quality of the image/video.
The IQA typically refers to the optimal parameter value for each scenario, the optimal parameter value can usually be considered the one that maximizes the quality of the images.
The IQA can make a direct comparison between an image of a lower quality and an image of a higher quality.
To be able to do that, the IQA first calculates a target for a perfect quality image, once this target is calculated, the target can be compared to the quality of the image and this comparison will provide values of parameters that are typical of the scenario in which the image was acquired.
IQA can be used in several scenarios in which the quality of the image is defined.
The simple format in which the IQA presents the images allows for an easy understanding in terms of its applicability in the various situations.
The IQA can be implemented in a variety of operating systems, currently most of the IQA implementations are compatible with Windows.

The MSE is the most representative quality metric. The MSE will be used in this guide to refer to the MSE_s to be precise.
The MSE measures the mean square error between the reference and the distorted image.

Best compression qualityMSE = mean square error

The Mean Squared Error measures the difference between the reference and the distorted image.
The MSE is the most representative quality metric.
The MSE will be used in this guide to refer to the MSE_s to be precise.
The MSE measures the mean square error between the reference and the distorted image.

The main difference in the statistical function with respect to the other IQA is that the MSE is based on the complete square of the coefficient of variation and is therefore larger.
In a good way, MSE can be seen as the opposite of the PSNR, that measures the perceptual distortion and
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This library can be used to perform a range of image and video quality analysis tasks, but still only supports the generic image quality metrics listed above. Some of the work that has been done to make the library so simple to use has also been transferred to the usage examples and demos.
The algorithm comparison part of this library shows all the different criteria, and provides a nice view of all available methods.
* New with version 0.7.0
Image quality algorithms
PSNR
The Peak Signal-to-Noise Ratio, PSNR, was created by Robert N. Graham in 1975 and has been used in compressive sensing. The PSNR is a popular, common and fast way to measure an image’s quality. It consists of two components, the MSE, and the Numerical Cost (NC) which measures the cost of performing an operation.
MSE: The Mean Squared Error is a quantity used to evaluate the mean difference between the predicted value and the desired/expected value. In other words, it evaluates the average error between each pixel and the desired image. The MSE uses the formula:
MSE ⁢ ( x , y ) = 1 M ⁢ ∑ i = 0 N – 1 ⁢ (

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Before the image is fed to the algorithm as an input, its histogram is computed and its compactness is calculated.
After the execution of the algorithm, a summary report with the corresponding metrics is provided for the image in every scenario defined in the `repo1` directory (each scenario corresponds to a particular image quality metric, such as MSE).
This distribution makes it possible to compare several algorithms in a relative way, such as MSE_psnr or MSE_ssim.
The evaluation metrics that are implemented are described in the `repo2` directory.

Image Quality Assessment (IQA) is a library that implements popular algorithms for quality evaluation of digital images or videos. The evaluation is made in a common ground, that’s the quality of the image / video is estimated in terms of its visual quality (that is, how different the generated image / video is with respect to the original one).
MSE, PSNR, SSIM and MS-SSIM are well-known metrics widely used for this purpose.
See the README for further details.

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System Requirements For Image Quality Assessment:

NEXUZ EXILUS Ver. 2.8.1
Minimum:
CPU:
x86 (32bit)
RAM:
2GB
HDD:
Recommended:
x64 (64bit)
4GB
3GB
* RAM and HDD required for installation, not save data (officially, no save data)
Additional Requirements:

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