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In both cases, the practice of ETTR does not use or depend on the camera's meter. Rather, it uses and depends on the exposure indicators, either the histograms and/or the highlight indictors (blinkies/zebras), which, ideally, have been set to reflect as well as possible the maximal values of the underlying raw data.
An image histogram is a type of histogram that acts as a graphical representation of the tonal distribution in a digital image. [1] It plots the number of pixels for each tonal value. By looking at the histogram for a specific image a viewer will be able to judge the entire tonal distribution at a glance.
In digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple image segments, also known as image regions or image objects (sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to ...
An example of histogram matching. In image processing, histogram matching or histogram specification is the transformation of an image so that its histogram matches a specified histogram. [1] The well-known histogram equalization method is a special case in which the specified histogram is uniformly distributed. [2]
Histogram differences (HD). Histogram differences is very similar to Sum of absolute differences. The difference is that HD computes the difference between the histograms of two consecutive frames; a histogram is a table that contains for each color within a frame the number of pixels that are shaded in that color.
In image processing and photography, a color histogram is a representation of the distribution of colors in an image.For digital images, a color histogram represents the number of pixels that have colors in each of a fixed list of color ranges, that span the image's color space, the set of all possible colors.
This reflects the way the camera works and how the data is stored in the computer, but it does not correspond to the way that people recognize color. Therefore, the HSL and HSV color models are more often used; note that since hue is a circular quantity it requires circular thresholding. It is also possible to use the CMYK color model. [12]
In more general fields of data processing, such as digital signal processing, it is referred to as dynamic range expansion. [ 1 ] The purpose of dynamic range expansion in the various applications is usually to bring the image, or other type of signal, into a range that is more familiar or normal to the senses, hence the term normalization.