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In digital image processing, the sum of absolute differences (SAD) is a measure of the similarity between image blocks.It is calculated by taking the absolute difference between each pixel in the original block and the corresponding pixel in the block being used for comparison.
The sum of absolute transformed differences (SATD) is a block matching criterion widely used in fractional motion estimation for video compression.It works by taking a frequency transform, usually a Hadamard transform, of the differences between the pixels in the original block and the corresponding pixels in the block being used for comparison.
Taxicab geometry or Manhattan geometry is geometry where the familiar Euclidean distance is ignored, and the distance between two points is instead defined to be the sum of the absolute differences of their respective Cartesian coordinates, a distance function (or metric) called the taxicab distance, Manhattan distance, or city block distance.
The absolute difference of two real numbers and is given by | |, the absolute value of their difference. It describes the distance on the real line between the points corresponding to x {\displaystyle x} and y {\displaystyle y} .
The MAE is conceptually simpler and also easier to interpret than RMSE: it is simply the average absolute vertical or horizontal distance between each point in a scatter plot and the Y=X line. In other words, MAE is the average absolute difference between X and Y.
Each color difference is the sum of absolute differences of the intensities of the color channels Red, Green, and Blue of the corresponding adjacent pixels. If this sum is greater than a given threshold, then the sign of the color difference is set equal to the sign of the difference of the green intensities.
The mean absolute difference (univariate) is a measure of statistical dispersion equal to the average absolute difference of two independent values drawn from a probability distribution. A related statistic is the relative mean absolute difference , which is the mean absolute difference divided by the arithmetic mean , and equal to twice the ...
Least absolute deviations (LAD), also known as least absolute errors (LAE), least absolute residuals (LAR), or least absolute values (LAV), is a statistical optimality criterion and a statistical optimization technique based on minimizing the sum of absolute deviations (also sum of absolute residuals or sum of absolute errors) or the L 1 norm of such values.