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The images are distorted using different distortion types and levels.
The images are distorted using different distortion types: JPEG2000, JPEG, Gaussian blur, white noise, and bit errors.
An extensive experiment was conducted to obtain scores from human subjects for a number of images distorted with different distortion types.
As shown in Fig. 1, panel a is a reference image while panels d and g are its two distorted versions (the distortion types are additive gaussian noise and non-eccentricity pattern noise, respectively).
a is a reference image while d and g are its two distorted versions (the distortion types are additive gaussian noise and non-eccentricity pattern noise, respectively); b, e, and h are the edge component of a, d, and g, respectively; c, f, and i are the texture component of a, d, and g, respectively.
It summarizes their main characteristics: number of image contents, total number of distorted images involved in the subjective experiment, number of distortion types evaluated, and format of the subjective scores.
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Thus, an objective NR metric will typically be designed for measuring a particular distortion type [32].
Distortion type had significant effect on response time, error rate, Critical flicker fusion (CFF), and NASA-TLX score.
In the first stage, various distortions are classified into a few types, and individual quality scorers targeting at a specific distortion type are developed.
This method can be divided into two stages: classification of distortion type and evaluation utilizing distortion-specific methods based on the most suitable color channel.
For each distortion type, correlations between the measured solution error and the different mesh metrics are quantified, identifying the most appropriate overall mesh metric.
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