Why do you need to know histograms to learn photography?

Many people encounter this situation when taking daily photos:

The photos on the camera seem to be exposed normally, but after exporting the photos, they find that their true form is far from the camera's rendering, and there is obviously a problem with the exposure.

Affected by environmental light, screen brightness and other factors, this situation is relatively normal, but it also brings us a revelation: when looking at photos and analyzing photos, you must learn to read the histogram Picture .

So, what is a histogram?

To simply understand, the histogram is a display form of the brightness distribution of photo pixels:

Horizontally, the histogram can be roughly divided into 3 parts, the left side is the shadow area, and the middle is the midtones part, the right side is the highlight area; the far left is the dead black area in the shadows, and the far right is the overflow area in the highlight.

The vertical axis represents the specific distribution of pixels. For example, if the "hills" on the histogram are concentrated on the left, it means that a large number of pixels in the photo are concentrated in the shadow area, and the overall picture is dark, and there may be underexposure; if the "hills" are If it is concentrated on the right side, it means that the photo is overall bright and may be overexposed.

Thus, when we view the photo, we can call up the histogram on the camera, and more accurately determine whether the photo is properly exposed based on the pixel distribution on the histogram.

Of course, there are different standards for normal exposure according to different themes and creativity, and the histogram only provides us with a reference.

However, we can pay special attention to the dead black and highlight overflow parts, which are the far left and right sides of the histogram. If there are a large number of pixels distributed in this part, then the photo is likely to be obviously underexposed or overexposed. , we need to adjust the shooting in time.

In addition to reflecting the exposure, the histogram can also give us a reference for the light and dark contrast of the photo:

We still regard the histogram as three parts: shadow, midtones, and highlights. If the pixels are in the three Each part is distributed:

Then it means that there are three forms of shadow, mid-tone and highlight in the photo at the same time, and the whole has a certain contrast, just like the photo below:

The blue area means dead black

And if the pixels are mainly concentrated in the shadow and highlight areas:

, it means that the contrast of the photo is very high, and the contrast between light and dark is great, just like the following effect:

On the contrary, if the span of pixels in the photo is very small and only concentrated in a certain area, it means that the contrast between light and dark in the photo is very small and the pixel distribution is very even:

It can be seen from here that the histogram can bring great convenience and reference to us in the later stage. When we adjust some parameters, the histogram also changes accordingly.

For example, when we increase the contrast, the light and dark contrast of the photo is enhanced, and the pixels on the histogram will move closer to both sides; when we increase the shadows, the pixels on the left side of the histogram will move closer to the right; and when we increase the exposure, All pixels on the histogram will move to the right as a whole.

Based on this property of the histogram, we can easily analyze the light and shadow characteristics of a photo.

When we encounter a photo we like, we can first call up its histogram, and then imitate its light and shadow effects based on the reference of the histogram. This way we don’t have to worry about parameters and filters, because the “understanding” of the photo has already been Completely exposed by a small histogram.

色彩方面也是如此，一些後期工具如 Lightroom，不僅可以顯示光影直方圖，還可以以三原色的形式反映色彩特徵，其原理與光影是一樣的。

由此可以看出，只要會看直方圖，我們幾乎能複製模擬出大多數照片的風格，我們對拍攝、後期的理解也會有質的突破。

今天的內容就到這裡了~

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