+\paragraph{Theory:}
+
+A histogram is a bunch of \textit{bins} (accumulators) that count the number of times a particular pixel channel intensity occurs in an image. Dim are on the left, bright on the right.
+
+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
+depends on which plugin is used:
+
+\begin{description}
+ \item[Histogram:] was max of the bins in the pixel range, now is the sum
+ \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}
+
+When the color space and the bin size are the same, all of the values
+increment the indexed bins. When the color is the result of yuv $\rightarrow$ rgb conversion, 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. 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 report something more consistent, has been changed the reported value to
+the \textit{sum} of the accumulated counts for the bins reporting a pixel bar on the
+graph. The effect of this is to do this:
+
+\begin{center}
+ \begin{tabular}{ l l c r r }
+ \hline
+ 1 & & & & \\
+ 1 & & & 1 & \\
+ 000100 & 3 pixels & vs & 001000& 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.
+
+