Industrial cameras come with numerous parameters, but the pixel format is undoubtedly the most critical factor affecting image quality and transmission efficiency. While many users are adept at adjusting exposure and gain, they often hesitate when it comes to choosing the Pixel Format. In reality, mastering this parameter will deepen your understanding of camera imaging.

In today's article, we'll thoroughly explain one of the most critical parameters in industrial cameras—the pixel format.

After watching this, you'll understand three key points: what a pixel format is; the differences between common pixel formats; and how to configure them in practice.

I. First, understand: What is a pixel format?

The so-called pixel format essentially refers to how each pixel point stores and organizes data during image acquisition by the camera.

Different pixel formats determine whether the image stores grayscale information or color information; they also affect the data volume, transmission efficiency, imaging quality, and even the final frame rate.

Common pixel formats used in industrial cameras primarily include the following categories:

Mono

Bayer

RGB

YUV

The name isn't complicated, but the underlying differences directly impact your project selection and debugging efficiency.

II.What are the common pixel formats, and for what scenarios are they suitable?

1) Mono: The most common format for black-and-white images

Mono, also known as the monochrome format, is typically used for black-and-white image acquisition.

In this format, each pixel contains only brightness information—specifically the grayscale value—and no color information. For example, Mono 10 means each pixel is stored using 10 bits.

Its features are straightforward: more concise data, and higher efficiency in transmission and storage.

For black-and-white cameras, the raw data is typically in the Mono format.

2) Bayer: The most common native format for color cameras

For color cameras, most raw image data is typically stored in the Bayer format.

The principle involves using a Color Filter Array (CFA) on the image sensor, where each pixel records only one color value—red, green, or blue—with other values interpolated from adjacent pixels.

In other words, each pixel does not contain complete RGB information; instead, it involves "first sampling a portion and then calculating the remaining portion."

The common arrangements of Bayer are as follows:

Common arrangement options include:

GR, GR (color channel for even-line scanning)

BG, BG (another common scanning sequence)

Bayer's advantages are evident: the relatively small data volume makes it suitable for raw acquisition of color images.

However, it has one drawback: since color information must be interpolated, the image quality is generally inferior to that achieved directly with RGB.

3) RGB: Complete color information for higher image quality

The RGB format contains three channels:

R： red

G： green

B： blue

In this format, each pixel contains complete three-channel color information. In other words, a pixel stores the values of R, G, and B simultaneously.

Its advantages include richer color gamut, making it more suitable for high-quality color image acquisition and precise color processing.

But the cost is clear: the volume of data is much larger.

Therefore, RGB is often more suitable for scenarios that prioritize visual effects.

4) YUV: A very common format in video processing

The YUV format is commonly used for video processing.

It breaks down the image information into two parts:

Y： monochrome information

U、V： chromatic information

Why is this design chosen? The human eye is more sensitive to brightness and less sensitive to chromaticity. Therefore, YUV reduces data volume while preserving visual quality as much as possible, facilitating compression, transmission, and storage.

Common YUV formats include:

Common types include:

YUV 422

YUV 444

YUV 420

These numbers represent different chrominance subsampling methods. Generally, the smaller the number, the less chrominance information is contained, and the smaller the image file size becomes.

III. Don't overlook this term: What is the relationship between Pixel Format and Packet?

When reviewing camera specifications, many users notice not only the Pixel Format but also a term: Packet.

Its core function can be summed up in one sentence: to save storage space.

Why do we need it?

In the absence of packets, pixel data is often stored in larger memory allocations. For example, data requiring only 10 bits may occupy 16 bits of storage space, resulting in unnecessary waste.

Just take an example to understand:

Mono 10: Each pixel occupies 10 bits of data; however, without packets, these 10 bits could be stored in a 16-bit space, resulting in a waste of the remaining 6 bits.

Mono 10 Packet: Stores 10 bits of data in a 12-bit space, with the remaining 2 bits filled with zeros to minimize storage waste.

In essence, Packet does one thing: it strives to make data storage as compact as possible.

This holds practical significance for bandwidth, storage capacity, and transmission efficiency.

IV. What exactly are the differences between various pixel formats?

To understand image formats, you shouldn't just memorize their names. The key point is that they differ in data content, file size, frame rate, and visual quality.

1) Each pixel records different information.

This is the most fundamental difference.

Mono: Each pixel has only a grayscale value

Bayer: Each pixel records only one color value (R, G, or B), while other colors are inferred from adjacent pixels.

RGB: Each pixel contains complete R, G, and B color values

YUV: Decomposes an image into two components: luminance Y and chrominance U and V

On the surface, it merely differs in data organization methods, but in reality, it impacts subsequent processing workflows.

2) The data size varies for each frame of the image

Different pixel formats result in different data volumes.

Mono: usually 8-bit or 10-bit

Bayer: The data volume is generally small, typically 8 bits.

RGB: occupies more space, typically 24 bits

YUV: The resolution depends on the sampling method, typically 16-bit or 12-bit.

In summary: The more complete the image information, the larger the data volume is typically.

3) Frame rate performance will also vary.

Different data volumes result in varying transmission loads. Consequently, the frame rate performance naturally differs accordingly.

generally speaking ：

Bayer: Easier to achieve higher frame rates

RGB: Due to the larger data volume, the frame rate is usually lower.

YUV: Generally somewhere in between

In many projects, the pixel format is not merely a 'image format choice'; it also directly impacts system efficiency.

4) There is a significant difference in imaging quality.

This is also the point of greatest concern to everyone.

For color cameras:

Bayer: Color information is determined by interpolation calculations, resulting in relatively low image color saturation.

RGB: Each pixel contains complete color information for more accurate and richer colors

YUV: Its color reproduction is nearly identical to RGB, but with separate handling of luminance and chrominance, it offers higher processing efficiency.

You'll find that speed, data volume, and image quality often cannot be optimized simultaneously. The choice depends entirely on your application requirements.

V. How to choose between a black-and-white camera and a color camera?

Summarizing the previous content again will make it easier to form a clear judgment.

Black-and-white camera: Typically uses Mono

The raw data from black-and-white cameras is typically in Mono format. Since it contains only grayscale information without color data, it requires less storage space and offers higher efficiency for storage and transmission.

Color camera: The raw data is typically Bayer.

Color cameras typically use the Bayer format for raw image acquisition. This format requires less data and is better suited for raw capture and high-frame-rate scenarios. However, since color information is reconstructed through interpolation, the image quality is generally inferior to that achieved with direct RGB encoding.

Pursue higher color quality: support RGB

For applications that prioritize color accuracy and image detail, RGB is more suitable. However, you must accept the increased data volume it entails.

In the video processing chain: YUV is very common.

The advantage of YUV lies in its separate processing of luminance and chrominance, balancing visual quality with compression efficiency. As a result, it is widely used in scenarios such as video transmission and storage.

VI. How to Set the Pixel Format? Actually, this is the only step required.

The actual setup is not complicated.

Before setting the pixel format, stop the camera's image capture stream first. Then, using the camera control software or the properties tree, select the desired pixel format under the Pixel Format settings.

The corresponding interface is usually as follows:

This step isn't difficult; the real challenge lies in whether you clearly understand why you chose this format.

VII. Final Summary: Pixel Format – More Than Just 'Setting It Up'

Many people treat the Pixel Format as a mere parameter. In reality, it encompasses critical aspects such as image data structure, storage methods, transmission efficiency, frame rate performance, and image quality.

You can remember it this way:

Mono: Black and white, simple and efficient

Bayer: Color raw data, small file size, high frame rate

RGB: Full color and high image quality, but large data volume

YUV: Suitable for video processing, balancing visual quality and compression efficiency

A true expert in industrial cameras doesn't merely know how to access the parameter menu; more importantly, they understand the meaning behind each parameter.

The pixel format is a very typical example.