+\paragraph{Theory:}
+
+For more on the mathematical aspect see here:
+
+{\small \url{https://thirdspacelearning.com/gcse-maths/statistics/histogram/}}
+
+For our discussion, it is enough to understand a few concepts. Each vertical line we see in the histogram is not a simple line, but a rectangle having a certain base. This base is given by the values of $x_i$ present at the edges of the rectangle \textit{i} (pixel range, $x_{max_i} - x_{min_i}$). Rectangles are called \textit{Bins} or Accumulators. The bin's number is of fixed and known size because it depends on the color depth. The bin height is our output $y$ and the bin area ($A_i = f(x_i)$) is known because it represents the \textit{number of occurrences} that are read in bin, also called \textit{frequency ($f_x$)}. The plugin scans the entire range of $x_i$, from 0 to 1.0, and records all the occurrences within each bin. The value of $f_x$ for each bin is the \textit{max} value. At this point knowing base and area, we can obtain the value of $y$ axis that is reported in the histogram.
+
+$width = b_i = x_{max_i} - x_{min_i}$
+
+Having established the depth color, the bin width is always the same (b) for every $bin_i$:
+
+$b_i = b = \dfrac{range(1.0-0)}{\# bins}$
+
+$A_i = f(x_i) = max_i = Base \times High = b \times y_i$
+
+Hence:
+
+$y_i = \dfrac{f(x_i)}{b}$
+
+Dim bins are on the left, bright bins on the right.
+You can have discordance of results, looking in the scopes, either by switching from Histogram to Histogram Bezier or after a conversion between color spaces (with associated change in depth color). The number of bins used depends on the color model bit depth:
+
+\begin{description}
+ \item[Histogram:] 256 for rgb8 and 65536 for all others
+ \item[Bezier:] 256 for rgb8/yuv8 and 65536 for all others
+ \item[Scopes:] always uses 65536
+\end{description}
+
+All of the bins are scanned when the graph is plotted. What is shown on the graph, in the Compositor window, and finally on the scopes, depends on which plugin is used:
+
+\begin{description}
+ \item[Histogram:] was max of the bins in the pixel range, now is the sum (to make it congruent with Bezier and the Scopes)
+ \item[Bezier:] is the max of the bins in the pixel range
+ \item[Scopes:] is the max of the bins in the pixel range
+\end{description}
+
+Another difference in behavior is regarding the type of curve, whether Linear or Log:
+
+\begin{description}
+ \item[Histogram:] the curve is Linear, but it is editable with the Linear/Log slider
+ \item[Bezier:] the curve is Log
+ \item[Scopes:] the curve is Log
+\end{description}
+
+This diversity also leads to different visual results from Histogram Bezier or Videoscope.
+
+When the color space and the bin size are the same, all of the values increment the indexed bins. But if we start from YUV type edits, the plugin will automatically do the conversion to RGB. When the color is the result of yuv $\rightarrow$ rgb float conversion, we go from 256 bins of YUV to 65536 bins of RGB Float and the results \textit{spread} if there are more bins than colors. This is the same effect you see when you turn on \textit{smoothing} in the vectorscope histogram.
+
+The \textit{total} pixels for each value is approximately the same, but the \textit{max} value depends on the color quantization. More colors increment more bins. Fewer colors increment fewer bins. In both cases, the image size has the same number of pixels. So the pixels will distribute into more bins if you go to a higher depth color; those bins will have a lower count. The fewer color case increments the used bins, and skips the unused bins. This sums all of the pixels into fewer bins, and the bins have higher values. That is the \textit{rgb} vs \textit{yuv} case, fewer vs more bins are used. To get more consistent visual feedback (and on scopes), the concept of \textit{sum} was used instead of the maximum number of occurrences (max).
+
+To report something more consistent, the reported value has been changed from the original code to be the \textit{sum} of the accumulated counts for the bins reporting a pixel bar on the
+
+\begin{center}
+ \begin{tabular}{clccr}
+ \hline
+ 1 & & & & \\
+ 1 & & & 1 & \\
+ 000100 & 3 pixels & vs & 0011000& 3 pixels \\
+ \hline
+ \end{tabular}
+\end{center}
+
+On the left, the course color model piles all 3 pixels into one bin, max
+value 3.
+On the right, the fine color model puts the counts into 2 bins, max 2, sum 3.
+
+So, by reporting the sum the shape of the results are more similar to Bezier.
+