![]() ![]() In the case of RGB pixels, that common unit of measure is the relative luminosity. To be able to sum those amounts we need first to convert them to a common currency or unit of measure. What we are saying here is like we would have three amounts of money -each one with a different currency- and we wanted the total amount. After all, the pixel values are encoding red, green and blue (RGB) luminosity, right? Once the RGB pixel values are converted to their luminosity equivalent value, we will be ready to compute whatever we want for the whole image data and get statistics values representative of it. Looking for the whole image average, one would expect that value to be perceptually correlated with the image luminosity. ![]() However, if we average green pixel values together with the red and blue pixel values, what would that value represent? It sounds like comparing apples and oranges. It is easy to understand the average of the green pixel values is the image average green value, which means the average intensity of the green color component in the image. In each color channel we can add the pixel values and compute the Average and the Standard Deviation without any problem. One with the red value of each pixel in the color image pixel (the red channel), another with the green (the green channel) and another one with the blue values (the blue channel). We can think about that RGB image, like composed by three monochromatic images with the same dimensions as the color image. We have a color image represented in the RGB color model, this means we have an image composed by color pixels, where each pixel has three numeric attributes representing the intensity (or luminosity) of the red, green and blue primaries, whose mix renders the corresponding pixel color. What follows is a better context description, then we will explore some solutions. #Gen weighted standard deviation how to#The question is how can we get a noise measurements representative of the whole image? This means to solve questions as: how can we get the average and the standard deviation and the resulting SNR representative of the entire image? In other words, we want to know how to merge the three channels averages, standard deviations and SNRs to get only one set of those values representative of the entire image. For each of the three image RGB channels we can easily compute the average and noise to get the SNR, but this way we end up with three averages, standard deviation, and SNR values: One set of those values for each channel. #Gen weighted standard deviation Patch#We know that a key indicator of the noise in a image is its SNR (signal to noise ratio), which is computed as the average color value of the pixels in a plain patch target divided by the standard deviation (noise) of the color values of those pixels. We want to measure the noise in a RGB image. Photoshop Average and Standard Deviation Formulas. ![]()
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