How to judge whether the exposure of a photo is accurate?

Lead: We pay attention to a parameter problem when taking pictures, so what is the basis for us to judge whether a photo is exposed accurately? Everybody follow me!

How to judge whether the exposure of a photo is accurate? 1. What is a histogram?

Step by step judge the exposure. Speaking of exposure, I think we must first talk about histogram.

First, let's look at the histogram.

Some people may say: What? Histogram? I use it when I touch the camera. What is there to talk about? Well, it's not too late for you to say it when I'm finished. Take this picture as an example:

Let me introduce the most basic knowledge of histogram first. The horizontal axis of the histogram shows higher brightness from left to right, and the vertical axis shows more pixels from bottom to top. The brightness range is 0-255, with 0 representing black and 255 representing white. If the peak is higher somewhere, it means that there are more pixels at that brightness.

Taking this histogram as an example, its distribution is very uniform, which shows that the pixel distribution in each brightness interval is very uniform.

After understanding the above sentence, you will have a basic understanding of histogram, but there are still many things about histogram.

Second, how to read the parameters of the histogram

Let me ask a question: Are two pictures with the same histogram necessarily the same picture?

The answer is of course no, because the histogram records the brightness information of pixels. In other words, we just change the relative positions of all the pixels above, and the histogram will not change at all, but the content of the picture may be completely changed.

It is very important to understand the above point, which is very helpful for us to understand the essence of histogram. Well, all the above is known knowledge, and here is what I want to say. Let's go back to the previous picture. There are several parameters such as color code, quantity and percentage on the right side of this figure. What do they mean?

You open the histogram, put the mouse in a certain position of the histogram, and these three parameters will appear, representing:

Color scale: the brightness of the pointer, that is, the value from 0 to 255.

Quantity: the pixel value under this brightness, for example, the above figure shows that the color scale 138 has 19 15 pixels.

Percentage: the position of the current color scale in the whole color scale.

Well, this has advanced knowledge. When you hold down the left mouse button and pull it to the right, you will find that they have changed.

Color scale: the color scale range you choose. For example, the above one is the color scale range from 1 15 to 2 16.

Number: The total number of pixels in this range.

Percentage: The percentage here is not the percentage of the position, but the percentage of the pixels in your selected range to the whole pixel.

Wait, some people will say that the total number of pixels in this photo is only 207284. How can there be 227728 pixels in this range? Isn't this a contradiction?

Good observation, because I chose RGB channel, and the total number of pixels should be multiplied by three. I will talk about each channel in detail later.

Well, after the above study, you know more about histogram, but it's not enough. There are more complicated ones. Take this histogram as an example, there are several parameters on the left, such as average, standard deviation, median and pixel. What do these parameters mean?

Average value: the higher the average value, the brighter the photo as a whole, and 128 is the middle value.

Its algorithm is: the total brightness of the image? The total number of image pixels.

Take the above picture as an example. The average value is 1 17, which is close to 128, so the exposure is normal.

Standard deviation: Standard deviation is a statistical term. A standard for measuring the deviation of data distribution, which is used to measure the degree to which data values deviate from the arithmetic mean. The smaller the standard deviation, the smaller the deviation of these values from the average, and vice versa. The standard deviation can be measured by the multiplication of the standard deviation and the average value.

Standard deviation formula: sample standard deviation S=Sqrt[ (? (xi-x) 2)/(n- 1)], where? Stands for sum, x stands for average value of sample X, 2 stands for quadratic, and Sqrt stands for square root.

None of the above matters, just understand. What we need to know is the relationship between standard deviation and pictures. The greater the standard deviation, the more obvious the picture contrast, and vice versa.

Intermediate value: the intermediate value after arranging the brightness values of all pixels in the image from small to large. That is, the data is divided into two parts, one part is greater than this value and the other part is less than this value. (If there are even pixels and there are two numbers in the middle, take the first one. )

The meaning of the middle value is to reflect the overall brightness of the picture from another side, whether it is overexposed or underexposed. It is complementary to the average, but not as accurate as the average. The specific reason is my own experience.

Pixel: I won't say much about this. Everyone is familiar with it.

Third, what is the channel?

After reading the above, you should have a comprehensive understanding of histogram, but you need some knowledge to really understand histogram.

There are many channels: RGB, red, green and blue, lightness and color. First of all, it is necessary to understand that the number and pixels in the histogram are not the same concept.

When we choose RGB channel, the maximum value = pixel value? 3。 And when we choose other channels, the maximum value = pixel value. For example:

In the RGB channel, when the color scale is one hundred, the number is 3119; Under the R channel, when the color scale is 100, the number is 945; G channel, when the color code is one hundred, the number is1610; Under channel B, when the color scale is 100, the number is 564.

You will find that the magnitude under the R+G+B channel is R+G+B, that is, the RGB channel is actually the sum of the values of the R, G and B channels.

What is a pixel? We regard the final mixed color of RGB as a color, which is what we call a pixel. I believe you can understand why the maximum value in a single R, G and B channel = pixel value. Similarly, RGB channels and lightness channels are also different.

This is the brightness channel:

Brightness channel

Maybe you will wonder, isn't histogram a reflection of brightness information? Why are the histograms under RGB channel and lightness channel different? This is caused by the calculation method. Brightness statistics is the composite value of each pixel, and the calculation method of brightness value of each pixel is: 30%? R+59%？ G+ 1 1%？ B.

Here again corresponds to the previous pixel, which is a composite value.

I believe you are a little dizzy after reading so much in front? Wait, one last thing about the histogram. Let me use the histogram of the red channel to say it.

Red channel

What can you think of when you see this histogram?

It shows that the red information is mainly distributed in the middle and dark parts, but not much in the bright parts. The histogram information of a single channel plays an important role in color matching and color deviation correction.

Fourthly, the histogram of cache level.

Finally, talk about the cache level of histogram. What does this mean? Look at the picture first.

I won't talk about its calculation principle, just say:

The higher the cache level, the faster the histogram is generated, but the more inaccurate it is (simply put, the higher the cache level, it will not calculate the value of each pixel, but merge several pixels into one pixel).

If you need to change the cache level to 1, just click the triangle in the upper right corner.

The cache level is 3

The cache level is 1.

Histogram is very helpful for us to understand exposure! For example:

Different exposure levels

The first photo is obviously overexposed and the second photo is obviously underexposed. The third exposure is just right, and the high light and weak light are the correct exposure. Well, if you believe what I said above, you still don't understand the essence of histogram.

As I said before, the histogram records the brightness information of pixels. In other words, we just change the relative positions of all the pixels above, and the histogram will not change at all, but the picture content may be completely changed.

Be sure to remember this sentence: histogram records the brightness information of pixels, and nothing else. The accuracy of exposure is not necessarily related to the uniformity of brightness distribution. The pictures corresponding to the above three histograms are:

Samples corresponding to histograms

The above point is the limitation of histogram, which only reflects brightness information, and nothing else represents it, and it is not necessarily related to correct exposure.

Application of verb (Verb Abbreviation) Histogram

I mentioned the limitations of histogram earlier. The reason why we should talk about the limitations first is to let everyone not be too superstitious about histograms, and not to form what kind of histograms are exposed accurately and what kind of histograms are not accurate.

So how to use the histogram? The answer is to use it in combination with the shooting environment.

The function of histogram in photography is obvious, especially when the screen can't be seen clearly under strong light, it is difficult for you to judge whether the exposure is accurate or not. At this time, you can make a general judgment on the exposure by combining the histogram.

The specific point about using it in combination with the shooting environment is that, for example, when you shoot a snow scene, it is generally unrealistic to insist on a large number of pixels in low light. For another example, when you shoot dark clouds, it is generally unrealistic to insist that highlights have a large number of pixels.

You should have a rough estimate of the histogram according to your shooting environment, instead of blindly pursuing weak light. There are pixels in the middle and high lights, and of course you need to accumulate some photographic experience. A common way to improve quickly is to look at the histograms of photos in some typical environments.

Histogram knowledge, if mastered skillfully, is very helpful for color matching and image recognition (later recognition means).

In addition, completely digital exposure is not suitable, and there is no absolutely correct exposure value. It doesn't mean that the average value must be what, the percentage must be what, and the histogram must be what is the accurate exposure. It is just a reference and a tool.

The accuracy of exposure depends on your shooting intention. For example, if you want to shoot LOMO, you can't use the normal standard deviation to measure it, because the standard deviation of LOMO is generally high. For example, if you want to shoot Japanese people, you can't use the normal average to measure them, because the average of Japanese people is generally higher. Therefore, whether the exposure is accurate or not must be discussed in combination with your shooting intention.