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Hence, the target of IQA is to develop automatic methods that can predict image quality consistently with human subjective evaluation.
The algorithm measures uncertainty present in the input output feature space to predict image quality accurately as close to human observations.
Especially, it is desirable and meaningful to design effective opinion-free BIQA (OF-BIQA) model to predict image quality without depending on human subjective score.
Scharstein and Szeliski [27] described the procedures to define the Middlebury Database [28], which offers the most complete stereoscopic algorithms benchmark to date, and software to evaluate new algorithms to predict image parallax.
Computer simulations were used to improve insight and predict image quality of different electrode configurations.
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The prediction error (PE) is obtained as the difference between the actual and the predicted image pixel values.
The spatial target image (STI) is made by subtracting the predicted image from the original image.
Apply the LIH procedure to an input halftone image, and generate a corresponding predicted image.
Step 2. Perform the inverse halftoning using Gaussian filtering for image, and generate a predicted image.
Fig. 8 Scatter plots of predicted image quality indices on the LIVE database.
can be regarded as the quality improvement of the predicted image by replacing Gaussian filtering with the LUT method.
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