Why 16 bit depth

With bits, you can use up to 4GB of physical memory. Bit depth matters in post-production. When we make large prints, create HDR photography, or care to preserve great range of tonal values, it is wise to work on our images in a 16 bit mode, which gives us greater tonality and color values.

Try to work in bit mode for as long as possible. Open your image in Photoshop 2. The image editing experts of our Image Studio are happy to help. At the bottom, we have a graph of the quantization error or rounding noise, which is calculated by subtracting the quantized signal from the input signal.

Quantization noise increases the smaller the bit depth is, through rounding errors. Increasing the bit depth clearly makes the quantized signal a better match for the input signal.

Additive Synthesis tells us that a signal can be reproduced by the sum of any other two signals, including out of phase signals that act as subtraction. So these rounding errors are introducing a new noise signal.

Click here for a zoomed-in graphic. Very small changes in the input signal produce big changes in the quantized version. This is the rounding error in action, which has the effect of amplifying small-signal noise.

So once again, noise becomes louder as bit-depth decreases. Small signal changes have to jump up to the nearest quantization level. Larger bit depths have smaller quantization steps and thus smaller levels of noise amplification.

Most importantly though, note that the amplitude of quantization noise remains consistent, regardless of the amplitude of the input signals.

Larger bit depths produce less noise. We should, therefore, think of the differences between 16 and 24 bit depths not as the accuracy in the shape of a waveform, but as the available limit before digital noise interferes with our signal. Kelly Sikkema We require a bit-depth with enough SNR to accommodate for our background noise to capture our audio as perfectly as it sounds in the real world. Note how the 8-bit example looks like an almost perfect match for our noisy input signal.

This is because its 8-bit resolution is actually sufficient to capture the level of the background noise. In other words: the quantization step size is smaller than the amplitude of the noise, or the signal-to-noise ratio SNR is better than the background noise level. The equation 20log 2 n , where n is the bit-depth, gives us the SNR. This is important because we now know that we only need a bit depth with enough SNR to accommodate the dynamic range between our background noise and the loudest signal we want to capture to reproduce audio as perfectly as it appears in the real world.

Your ear has a sensitivity ranging from 0dB silence to about dB painfully loud sound , and the theoretical ability depending on a few factors to discern volumes is just 1dB apart. So the dynamic range of your ear is about dB, or close to 20 bits. If you were to put it on a graph, this is what it would look like:.

Bits per channel are pretty easy to understand, it is the number of bit used to represent one of the color channels Red, Green, Blue. Meaning that each pixel can have values ranging from 0 to 16,,, representing about 16 million colors. As the human eye can only discern about 10 million different colors, this sounds like a lot.

But if you consider that a neutral single color gradient can only have different values, you will quickly understand why similar tones in an 8-bit image can cause artifacts. Those artifacts are called posterization. More than 16 million times more numerical values then the 8-bit setting. Note: Photoshop will often show a color value between 0 to per channel regardless of what bit depth you edit in.

This is purely to simplify things for the user. Behind the scenes it utilizes the full value range. To get a smooth graduation between to tones, you need the space in between those tones to have enough width to hide the graduation. Like so:. The lower the bit depth, and the closer the start and end tonal values are to each other, the bigger risk of getting banding. If we take this to extreme, imagine that if you only had a bit depth of one bit the gradient you have at your disposal is really limited: either black or white.

If you want to go between tonal value 50 and , there are only 50 possible steps. If you stretch that out over a larger distance you are definitely going to see banding. This is what would happen if we were working in 8 bit BPC setting — just 50 steps.

When you look at a histogram of an image you are looking at its tonal range. At the far left the tonal value is 0 and at the far right the tonal value is , giving you a range of 8 bits. As I explained earlier, this histogram actually represents a larger range in bit mode; 0 to The risk of editing in 8-bit is that you could lose information if you were to push and pull on your edits.

Which in turn could lead to banding and unwanted color variations. Unfortunately most typical desktop displays only support 8 bits of color data per channel. This means that even if you chose to edit in bit, the tonal values you see, are going to be limited by your computer and display. There are a number of steps involved to convert photons of signal from your sample to the image you see on your computer monitor, each step has variables and factors that can change the ways in which images are generated.

Bit depth is one of these variables, by understanding how it can affect your images and improve your experiments, you can perform more efficient and informed imaging research.

The journey from light to an image is displayed in Fig. This process is the same for all camera technologies, but changes in each of these steps can optimize the end result.

Some important camera factors to consider before discussing bit depth are full well capacity and dynamic range. In different camera models or different modes in the same camera, these pixels have a different full well capacity , which is the maximum number of electrons they can store and still display as an image.

Some cameras offer full wells up to 80, electrons, meaning extremely bright samples can still be displayed, whereas some are much lower , meaning they are suited to lower signal levels. Full well should be considered if your sample level is very high brightfield imaging , if your sample can get significantly brighter and change over time, or if you are attempting to image bright and dim objects in one image.

However, most fluorescent applications have low signal levels and are suited to a lower full well capacity. The dynamic range of a camera is related to the full well capacity, and it describes the ratio between the highest and lowest signals that can be displayed. This is calculated by simply dividing the full well capacity by the read noise, as these represent the maximum and minimum readable signals respectively.

Dynamic range helps to analyze the change in your sample, how do you know your signal has doubled if the sensor cannot capture it? This article describes another factor of scientific cameras that affects both the full well capacity and the dynamic range, namely the camera sensor bit depth, as well as how to best match your application and signal level to a suitable bit depth.

The majority of scientific cameras are monochrome, meaning that the digital signal comes in the form of a grey level , ranging from pure black to pure white. The more intense the analog signal, the whiter the grey level, meaning that fluorescent images are typically displayed as a grey-white signal on a dark black background.

The signal is spread across the available range of grey levels, the greater the amount of signal, the more grey levels needed to fully display the image. If a signal had a peak of electrons, but the camera could only display different grey levels, the signal would be compressed and every 50 electrons would be converted to one gray level, meaning that a signal would have to increase by over 50 electrons before a change could be seen in the image. This would make the camera insensitive to small changes in the sample.

In order to produce the correct number of grey levels to display the range of signal, cameras can operate at different bit depths. If a camera pixel was 1 bit, it would either be pure black or pure white and would not be useful for quantitative imaging. Each bit can display 2 x grey levels, so 1 bit is 2 2 1 grey levels and 2 bits is 4 2 2 grey levels, as seen in Table 1. As this is also a visual concept it is described visually in Fig.

This is shown by Fig. As seen in Figs. This is due to a number of limiting factors, including the computer monitor you are using to view this article, as well as the eyes you are using to look at the monitor.

Computer monitors are limited to 8 bit per color channel, typically red, green, and blue RGB. While these monitors can display over 16 million RGB colors x x , for monochrome viewing there are only grey levels available, meaning your monochrome microscope images are being viewed as 8 bit through the monitor. This is similar to the human eye, which can see over 10 million colors, but only around shades of grey.