![\"_A1P9136_combined\"](\"http:\/\/ralphnordstromphotography.com\/wordpress\/wp-content\/uploads\/2010\/11\/A1P9136_combined.jpg\" "\\"_A1P9136_combined\\"")
<\/a><\/p>\nThis photograph is a silhouette with a large dark foreground with the evening sky in the background.\u00a0 This histogram is a luminance histogram; that is, it ignores what little color there is in the image and just reports on shades of gray.\u00a0 (In many digital cameras RGB histograms are also available.\u00a0 They show each of the three color channels \u2013 red, green and blue \u2013 from the darkest shade of each color to its lightest.\u00a0 This can be very useful in many shooting situations that I\u2019ll discuss later.)<\/em><\/p>\n The histogram above shows a spike toward the left of the graph and a second broader area in the middle.\u00a0 The spike on the left side, the shadow side, is the dark silhouette in the foreground.\u00a0 The broader area toward the center is the sky.\u00a0\u00a0 That fact that this area is in the center tells us that the\u00a0 sky is neither dark nor light, something we can clearly see with our eyes.\u00a0 Tonalities that fall in the middle of the histogram like this are referred to as \u201cmid-tones.\u201d\u00a0 In the black and white world they\u00a0 would be called \u201cneutral gray,’\u201d a shade of gray that is perceived as being neither dark nor light.\u00a0 Understanding mid-tones is important to understanding how our\u00a0 camera\u2019s light meter works but is not particularly relevant to this discussion.<\/p>\n In a normal exposure the histogram fits between the left and right sides.\u00a0 It doesn\u2019t have to fill the entire range (we\u2019ll talk more about this in a subsequent post) but it\u2019s OK if it does.\u00a0 Here\u2019s an example of just such a photograph.\u00a0 The subject is not particularly interesting and the light is horrible but the image illustrates the point.\u00a0 The histogram extends from the left side to the right.<\/p>\n Notice there is a little bit of space or breathing room between the left end of the histogram and the left side of the graph.\u00a0 The same is true for the right side.\u00a0 This is a well exposed image.<\/p>\n But not all images will have histograms that extend to both sides.\u00a0 Why?\u00a0 Because the dynamic range of the scene may not be as great as the dynamic range of the camera\u2019s sensor.\u00a0 You see, what\u2019s really going on here is the camera\u2019s sensor has a maximum dynamic range that it is capable of capturing.\u00a0 The sensor\u2019s dynamic range is the difference between the darkest and brightest parts of the scene that it can capture without loosing detail in the both the shadows and the highlights.\u00a0 The dynamic range is measured in f\/stops and a typical digital camera may have a dynamic range of five or six f\/stops.<\/p>\n So if you\u2019re photographing a scene such as the one above where the dynamic range of the scene matches the dynamic range of the sensor, you get a histogram that nicely fits into the entire graph.\u00a0 But some of the scenes will have a dynamic range that is smaller than that of the camera\u2019s sensor.\u00a0 And here\u2019s what you get then.\u00a0 Again, the subject is blah, the light is horrible but the histogram illustrated the point.<\/p>\n This scene lacks the foreground shadows and the bright sky of the previous example.\u00a0 It\u2019s dynamic range is dramatically reduced and the histogram reflects that.\u00a0 Also notice that the camera\u2019s light meter centered the histogram, placing it in the mid-tone range.<\/p>\n These first two examples are ones of correctly exposed images.\u00a0 Next let\u2019s take a look at what happens when an image is over- or underexposed.<\/p>\n This next example shows the first image but now it is intentionally overexposed.\u00a0 You can see that clearly enough just from looking at it.\u00a0 It appears bright and washed out.<\/p>\n There are many areas in the image that have no detail whatsoever; these areas are pure white.\u00a0 The histogram reflects this situation; it is pushed up against the right (highlight) side \u2013 climbing the wall.\u00a0 This is highlight clipping<\/strong>.\u00a0 And since a large area of the image is\u00a0 overexposed, a proportionately large area of the histogram is crammed up against the right wall.<\/p>\n The opposite situation occurs when the image is underexposed.<\/p>\n The entire image appears dark and this is reflected in the histogram being well to the left of the mid-tone center.\u00a0 Areas of the image are pure black with no detail although the areas are not that large.\u00a0 That\u2019s also reflected in the histogram which has shifted to the left (shadow) side.\u00a0 A small portion of the histogram has climbed the left wall but not as dramatically as the overexposed example.\u00a0 Still, there is some shadow clipping <\/strong>in this image.<\/p>\n Let\u2019s look at the numbers.\u00a0 Digital cameras are computers and computers store all data as numbers.\u00a0 Images are captured by the sensor as points of light or pixels.\u00a0 So the luminance (lightness and darkness) of a pixel is stored as a number.\u00a0 Well, actually, it\u2019s stored as three numbers \u2013 red, green and blue.\u00a0 When all three numbers are 0 you have pure black.\u00a0 The numbers at the left wall are (0,0,0).\u00a0 The numbers at the right wall depend on whether your images are captured at 8 or 16 bits per channel.\u00a0 Let\u2019s go with 8 for this illustration.\u00a0 The smallest number that can be represented with 8-bit color is 0 (of\u00a0 course).\u00a0 The\u00a0 largest number is 255 (ask a computer geek to explain this if it doesn\u2019t make sense to you).\u00a0 So if (0,0,0) is pure black and, in 8-bit color, (255,255,255) is pure white.<\/p>\n When you capture an image in your digital camera the intensity of the light at each pixel is translated into three numbers like (100,105,90) which happens to be the color of the leaves of a California live oak tree.<\/p>\n Clipping occurs when the pixels contain the extremes of these numbers.\u00a0 When a pixel\u2019s values are (255,255,255) you have highlight clipping.\u00a0 When the values are (0,0,0) you have shadow clipping.\u00a0 It\u2019s as simple as that.<\/p>\n Both highlight clipping and shadow clipping are something to be avoided but are they equally \u2018bad\u2019 or is one worse than the other?\u00a0 This question has a very cut and dried answer.\u00a0 While we prefer avoiding both forms of clipping, highlight clipping is by far the worst.\u00a0 That\u2019s because you can often recover at least some detail from shadow clipping.\u00a0 You may have some noise but that can often be handled with noise reduction plugins.\u00a0 But all too often there\u2019s no recovering highlight clipping.\u00a0 There\u2019s no detail, there\u2019s no color, nothing.\u00a0 So, when I need to choose between highlight clipping and shadow clipping I\u2019ll underexpose to preserve the highlights and accept whatever shadow clipping happens.\u00a0 This produces silhouettes which in many instances look really nice.<\/p>\n There are some misconceptions about clipping.\u00a0 One is that the histogram below shows clipping.<\/p>\n There are some that think that if a histogram peak touches the top like this one you have clipping.\u00a0 But that\u2019s not the case at all.\u00a0 The large spike in the middle of the histogram is caused by the stucco wall that occupies a large area of the image.\u00a0 The RGB numbers for the stucco wall (in 8-bit color) are (130,130,122), nowhere near the clipping numbers of (0,0,0) or (255,255,255). The fact is these numbers are near the mid-tone numbers of (128,128,128).\u00a0\u00a0 So the big spike in the middle of the graph just means that a large portion of the image is at or near the mid-tone.\u00a0 It\u2019s NOT clipping.<\/p>\n At times during a photography workshop a student will point to a histogram with a huge spike like this one and ask if they have a clipping problem.\u00a0 Or they\u2019ll ask what the best shape of a histogram is.\u00a0 I always respond with the question, \u201cWhat\u2019s happening on the right side of your histogram?\u201d\u00a0 I want them to be aware of highlight clipping because I\u2019ve lost more potentially exciting images due to highlight clipping than any\u00a0 other cause.\u00a0 The shape of the histogram doesn\u2019t matter as long as it stays away from the right side.\u00a0 In fact, you have no control over the shape of the histogram.\u00a0 The only thing you have control over is where you place the histogram and that is controlled by the exposure.\u00a0 As we saw above, overexposure shifts the histogram to the right and underexposure shifts it to the left.\u00a0 You want to choose an exposure that avoids highlight clipping.<\/p>\n The shape of the histogram can affect the decisions you make regarding exposure and post processing and I\u2019ll cover these in a later post.\u00a0 But for now, the most important rule in exposure, the first critical step in creating a compelling photograph is<\/p>\n AVOID HIGHLIGHT CLIPPING<\/strong>.<\/span><\/p>\n Join me on an upcoming workshop.\u00a0 Click here for more details.<\/u><\/span><\/a><\/p>\n To see more of my photographs click here.<\/u><\/span><\/a><\/p>\n (2700)<\/p>\n","protected":false},"excerpt":{"rendered":" Alas, the histogram is misunderstood\u2026, or not understood at all.\u00a0 I often run into students on photography workshops who say they\u2019ve noticed the histogram but never knew what it was nor paid much attention to it.\u00a0 But the histogram is one of our most effective tools we have for getting the correct exposure.\u00a0 And a … Continue reading “Mastering Exposure–Histograms Part 1”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"jetpack_post_was_ever_published":false},"categories":[281,4],"tags":[279,267,199],"class_list":["post-1427","post","type-post","status-publish","format-standard","hentry","category-expoure","category-how-to-articles","tag-histogram","tag-mastering-exposure","tag-photography-workshops"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p9Nl7-n1","jetpack_likes_enabled":true,"_links":{"self":[{"href":"https:\/\/ralphnordstromphotography.com\/wordpress\/wp-json\/wp\/v2\/posts\/1427","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ralphnordstromphotography.com\/wordpress\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ralphnordstromphotography.com\/wordpress\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ralphnordstromphotography.com\/wordpress\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ralphnordstromphotography.com\/wordpress\/wp-json\/wp\/v2\/comments?post=1427"}],"version-history":[{"count":3,"href":"https:\/\/ralphnordstromphotography.com\/wordpress\/wp-json\/wp\/v2\/posts\/1427\/revisions"}],"predecessor-version":[{"id":4609,"href":"https:\/\/ralphnordstromphotography.com\/wordpress\/wp-json\/wp\/v2\/posts\/1427\/revisions\/4609"}],"wp:attachment":[{"href":"https:\/\/ralphnordstromphotography.com\/wordpress\/wp-json\/wp\/v2\/media?parent=1427"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ralphnordstromphotography.com\/wordpress\/wp-json\/wp\/v2\/categories?post=1427"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ralphnordstromphotography.com\/wordpress\/wp-json\/wp\/v2\/tags?post=1427"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Normal Exposure<\/h3>\n
<\/a><\/p>\n<\/h3>\n
<\/a><\/p>\nClipping<\/h3>\n
<\/a><\/p>\n<\/a><\/p>\nThe Worst Clipping<\/h3>\n
Clipping Is Not\u2026<\/h3>\n
<\/a><\/p>\n