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For that, we present some criteria commonly used for measuring image quality reconstruction.
Other approaches for measuring image quality can be found in [6, 7].
This paper presents a new algorithm for measuring image quality of a BDCT coded images or videos.
The SSIM index is a full reference metric for measuring image quality using an initial noise-free-image as reference.
(A) Measuring image quality of the grid dataset with SSIM.
(B) Measuring image quality of the gradient grid dataset with SSIM.
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Therefore, No-Reference Image Quality Assessment (NR-IQA) technique [9, 10] must be used to measure image quality.
Although most studies employ the peak signal-to-noise ratio (PSNR) metric to measure image quality performance, it is well known that this metric does not always capture the distortion perceived by the human being.
In order to measure image quality, Sheikh and Bovik [4] propose an image information fidelity measure that quantifies the information that is present in the reference image and how much of this reference information can be extracted from the distorted image.
Established methods in performing observer and diagnostic performance tests make it possible to measure image quality by the evaluation of anatomical structures seen on the CT images against a set of criteria that have to be fulfilled [21, 22, 23, 24].
Other device and image attributes such as binary similarity measures, image quality measures, and higher order wavelet statistics have also been adopted for identifying source imaging devices [19 22].
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