While Photoshop was able to improve this image, one thing that should be noted is that using Photoshop to correct poor quality images is not a good practice. Images should be shot properly in the first place. Repairing poor images in the computer usually results in an image that is less than high quality. For instance, this image will have increased noise in the darker tones.
Figure 8 shows a sunset over a mountain. This image is composed primarily of very dark tones and midtones. Light tones are mostly missing as would be expected of this type of shot.
The Histogram in Figure 9 shows the predominance of the dark and midtones. The Histogram verifies that the lighter tones are almost completely missing. Despite the fact that this histogram is missing the lighter tones, this image does not need any tonal editing as the histogram of this image shows the tones that would be expected based on the image content.
The Leaf and Mountain Sunset images again show the importance of comparing a Histogram to its image. The Histograms in both Figures 2 and 9 are lacking lighter tones. However, comparing Figure 2 to its image indicates an underexposed image while comparing Figure 9 to its image indicates a properly exposed image that is composed of mostly dark and midtones.
The image's Histogram in Figure 11 is shifted to the right, thus, verifying the overexposure. The Histogram also contains no dark tones. These problems can be improved with a tonal adjustment. Of far greater concern is the right hand side of the Histogram. It shows that the highlights were clipped (i.e., cut off). If the image is a JPEG (which this image is), the clipped tones are completely lost and can not be recovered. If the image was from a raw file, the clipped tones may be recoverable, but there is no guarantee that they can be recovered.
The loss of the highlights is shown in the close-up in Figure 12.
The grayscale Histogram does not tell the entire story. It essentially takes the information from the three color channels (i.e., the red, green, and blue channels) and calculates a grayscale tonality. This might be thought of as an average tonality of the three color channels. However, the three channels can be significantly different from each other. To understand the full tonality of the image, a photographer needs to look at each of the three channels.
However, a look at the channel Histograms tells a different story. Figure 30 shows the channel Histograms before the tonal adjustment. The important point for this tonal adjustment is that the red Histogram extends almost all of the way to the right side of the chart. In other words, setting the highlight values too far to the left will clip this channel and cause a loss of detail. In fact, if one looks at the channel Histograms after the tonal adjustment (see Figure 31), this is exactly what happened when the Levels adjustment was made. The red Histogram in Figure 31 shows that the red channel was clipped. This can be seen by the red spike at the right side of the red Histogram.
Analyzing the individual channel Histograms is similar to analyzing the grayscale Histogram. The main difference is that all three channel histograms need to be considered before making any editing choices. Subsequently, no further examples of channel Histograms will be presented.
Histograms are a critical tool for analyzing images and determining the results of edits. The ability to read histograms is a necessary skill for anyone who desires to perform digital image editing.