# Color Space Transformations - Image data is encoded into 3 stacked matrices, each of size height×width. These matrices represent pixel values for an individual RGB color value - Lighting biases are amongst the most frequently occurring challenges to image recognition problems - A quick fix to overly bright or dark images is to loop through the images and decrease or increase the pixel values by a constant value. - Another quick color space manipulation is to splice out individual RGB color matrices. - Another transformation consists of restricting pixel values to a certain min or max value. - Similar to [[Geometric Transformations]], a disadvantage of color space transformations is increased memory, transformation costs, and training time. - Additionally, color transformations may discard important color information and thus are not always a label-preserving transformation. - For example, when decreasing the pixel values of an image to simulate a darker environment, it may become impossible to see the objects in the image. - Digital image data is usually encoded as a tensor of the dimension (height × width × color channels) - Performing augmentations in the color channels space is another strategy that is very practical to implement. - Very simple color augmentations include isolating a single color channel such as R, G, or B. - An image can be quickly converted into its representation in one color channel by isolating that matrix and adding 2 zero matrices from the other color channels. Additionally, the RGB values can be easily manipulated with simple matrix operations to increase or decrease the brightness of the image. - More advanced color augmentations come from deriving a color histogram describing the image