Understanding Histograms for Digital Photography August 2008 Number 22/4



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Conserve O Gram

Understanding Histograms for Digital Photography

 

August 2008

      Number 22/4

A histogram is a bar graph accessible through a 

SLR (single lens reflex) camera menu display-

ing the distribution of light, dark and color 

tonal values inside a digital image.  These tonal 

values are sometimes referred to as a camera’s 

dynamic range.  

The histogram displays all the available tonal 

values of a digital image along the horizontal 

axis (bottom) of the graph from left (darkest) 

to right (lightest).  The vertical axis represents 

how much of the image data is found at any 

specific brightness value.  The colors in the 

histogram reveal the tonal values of each color 

channel.  White represents the tonal data of all 

three color channels overlapped.

The histogram is one of the most valued tools 

in digital imaging capture.  It is used to:

Provide real-time information to    

immediately adapt and shoot better pic-



tures balance and light adjustment. 

Adjust the image during processing by pro-

viding a graphic display and target for edit-



ing actions 

 

Digital image editing relies on histograms to 



reveal the outcome of each editing and red, 

green and blue color alteration.  It enables the 

production of consistent quality digital images 

by providing ideal targets for tonal values.

Figure 1. Object Image

Figure 2. Histogram of the Object in Figure 1

Figure 3. Camera View of Figure 2 Histogram

Figure 4.  Editing Software Showing the Relationship 

Between the Image and the Histogram 



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Understanding Histograms for Digital Photography

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One of the main uses of histograms is to pre-

vent “clipping,” the cutting off or removal 

of tonal data in digital images.  Clipping is 

shown on a histogram when tonal data goes 

below 0, true black, or above 255, true white, 

and is eliminated from the edges of the histo-

gram.  A small amount of clipping is accept-

able.  Accented highlights of metal subjects or 

dark shadows may clip a little in a histogram 

but the overall image itself may still look good. 

Clipped data is not recoverable when process-

ing the image for use.  The histograms in Figs 

7-12 illustrate clipping at both ends of the 

spectrum by showing at either end of the scale 

beyond the 0 and 255.

The histogram is a guide.  The best way to 

judge exposure is looking at the image and 

color chart, not just the histogram.  Generally, 

a histogram representing a “good” exposure has 

tonal data gently sloping up from 0, shadows, 

peaking in the midtones and then gently slop-

ing down to 255 at the highlights. Fig. 2 shows 

a good standard distribution in the histogram 

for this object.  However, the tonal data doesn’t 

have to peak in the midtones or gently slope 

down to shadows and highlights for a “good” 

digital image.  A dark subject, like a cannon-

ball, will have most of its tonal data clustered 

near the shadows while a light subject, like a 

white china plate, will place most of its tonal 

data clustered near the highlights.  Figs 5 and 6 

show a good example of a histogram represent-

ing a light object with most of its tonal data 

near but not beyond 255.

Histogram Examples

Figures 5 and 6. Good Exposure 

This is a good exposure for this item.  Since this object 

is so light to begin with, the highlights bunch a little 

instead of ideally gently sloping down to 255.  But the 

shadows do ideally start at 0 and gently slope upward 

revealing details in darkest areas. 



Understanding Histograms for Digital Photography

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Understanding Histograms for Digital Photography

 National Park Service

Conserve O Gram 22/4

Figures 7 and 8. Overexposed

This histogram reveals too many tonal values that are 

close to 255 or more, indicating clipped highlights.  

Details in the highlights are lost and shadows are not 

dark enough.

Figures 9 and 10. Underexposed

This histogram reveals too many tonal values near or 

below 0, indicating clipped shadows.  There should 

only be values near 0 if there is pure black in the image.  

Details in the dark areas are lost and there is not enough 

information near 255 to reveal highlights.

Figures 11 and 12. Too Much Contrast 

This histogram shows clipped highlights and clipped 

shadows.  This indicates either the exposure and contrast 

settings are off or the dynamic range of the subject is too 

much for the camera.



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Understanding Histograms for Digital Photography

Conserve O Gram 22/4

National Park Service

lead to posterization (data loss that leads to artificial 

infill and distortion of the image when viewed).

References

Fraser, Bruce Real World Camera Raw with 



Adobe Photoshop CS2. Peachpit Press, Berkeley, 

CA, pp.109-110. 2005.

Grogh, Peter The DAM Book, Digital Asset 

Management for Photographers. O’Reilly Media, 

Sebastopol, CA, p. 179. 2006.

Hogan, Thom “Histogram,” By Thom, 2001. 

http://www.bythom.com/histogram.htm

Northeast Document Conservation Center. 

Digital Preservation Readiness Webliography 

April 2008. http://nedcc.org/resources/leaflets

/6Reformatting/08DigitalPreservationReadine

ss.php

James Carey



Museum Technician

Digital Imaging Project

Sponsored by the Park Museum Management Program 

National Park Service

Harpers Ferry Center, WV 25425

Figures 13 and 14. Too Little Contrast 

This histogram shows only midtones, lacking any high-

light or shadow tonal information.  This lack of contrast 

results in a hazy image.   

Figures 15 and 16. Over Manipulated Contrast

This histogram shows how contrast is improved when 

using curves or levels in Photoshop to “stretch” the above 

histogram see in Figs 13 & 14. As the same amount of 

tonal data is now stretched over a wider area, some data 

are missing.  This is represented by gaps in this “combed” 

histogram.  Too much combing or stretching of data may 




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