Understand Exposure Compensation:what is and how does it work?

In embedded vision, light is the lifeblood of an image, and its control is the soul of the imaging system. Whether it's an industrial camera or an in-vehicle vision system, the stability and reliability of its performance are closely linked to the chosen exposure mode. Auto exposure has gained widespread adoption due to its flexibility, but its underlying principles and limitations are often overlooked.

As a consultant specializing in camera modules, this article will provide an in-depth analysis of the advantages, disadvantages, and applicable scenarios of manual and automatic exposure, as well as their respective compensation modes. We will explore the art of adjusting key parameters such as gain exposure, helping engineers understand the true secrets of camera exposure and make the most informed decisions for their machine vision applications.

What is Manual Exposure Mode?

Manual exposure mode, as the name suggests, involves manually setting exposure parameters by the engineer or the system. These parameters typically include exposure time (shutter speed) and gain. Exposure time determines the length of time the image sensor collects light, while gain amplifies the electrical signal after it is formed. In manual mode, these two parameters are fixed.

The core of camera exposure lies in its controllability and predictability. Because exposure parameters are preset, each shot under the same lighting conditions produces identical image data. This provides stable input for machine vision algorithms and is preferred for many high-precision applications. However, its drawback is that it cannot adapt to changes in ambient lighting; if the lighting changes, the image will be over- or underexposed.

Use Cases of Manual Exposure: When to Choose Fixed Gain Exposure?

The determinism and stability of manual exposure are irreplaceable in many embedded vision applications.

1. Controlled Lighting Environments: On industrial production lines, lighting is often constant. Manual exposure ensures highly consistent image data for every product, which is crucial for industrial quality inspection and dimensional measurement. Fixed gain exposure eliminates the variation introduced by fluctuating exposure parameters, ensuring algorithm robustness.
2. High-Speed Motion Capture: To capture fast-moving objects without motion blur, extremely short exposure times are required. Manual mode ensures that the shutter speed is locked to a fixed value, which is particularly critical for high-frame-rate and high-speed camera applications.
3. Quantitative Analysis: In scientific research and medical imaging, engineers need to quantify certain physical or chemical properties by analyzing image brightness values. Manual exposure ensures that image brightness values are linearly proportional to actual light intensity, thereby ensuring accurate data analysis.
4. Texture and Color Analysis: When machine vision algorithms need to accurately analyze an object's color or texture, fixed exposure parameters ensure that image color and brightness do not drift due to exposure adjustments, ensuring the reliability of the analysis results.

What is Auto Exposure Mode? Core Technology and What is Auto Exposure Lock?

Auto Exposure Mode (AE) involves the camera's internal algorithm automatically adjusting exposure parameters based on image content to achieve a preset brightness target. AE algorithms typically measure the average brightness of an image or the brightness of a specific area and then dynamically adjust exposure time and gain.

What is Auto Exposure Lock (AEL)? An important supplementary feature to AE mode. It allows users to lock the current exposure settings at a specific moment, maintaining them for subsequent shots. This prevents the AE algorithm from making inappropriate adjustments due to scene changes. AE's strengths lie in its flexibility and convenience, but its challenge lies in the fact that the algorithm can be tricked, leading to incorrect judgments in unusual scenarios (such as backlighting).

Use Cases of Auto Exposure: When should you let the camera exposure run wild?

Despite its limitations, auto exposure's flexibility is indispensable in many applications.

1. Dynamic Lighting Environments: In outdoor surveillance, automotive vision, and security monitoring, lighting conditions fluctuate drastically, from day to night and from sunny to cloudy. Auto exposure ensures that the camera consistently delivers a usable, appropriately bright image without requiring manual intervention.
2. Unstructured Scenes: In general robotics or consumer applications, the scene and content being captured are unpredictable. Auto exposure can serve as a plug-and-play solution, ensuring a generally usable image under a variety of conditions.
3. User Experience: For embedded vision products that don't require rigorous quantitative analysis, auto exposure provides the best user experience, freeing users from having to worry about complex exposure parameters.

How Exposure Compensation Works?

Exposure compensation is an advanced feature of auto exposure mode. It allows users to fine-tune camera exposure based on automatic exposure. The user sets a positive value (e.g., +1EV) or a negative value (e.g., -1EV). The AE algorithm adjusts its brightness target accordingly, resulting in a brighter or darker final image than the default setting.

The core idea behind how exposure compensation works is that it provides a fine-grained control method that strikes a balance between fully automatic and fully manual exposure. It allows engineers to adjust exposure bias in specific scenes while maintaining the adaptability of automatic exposure, thereby achieving a more targeted image.

Limitations of Exposure Compensation

While exposure compensation is very useful, it's not a panacea and has its limitations and challenges.

1. Non-absolute control: Exposure compensation is a "suggestion" to the automatic exposure algorithm, not a mandatory command. It still relies on the AE algorithm's own judgment, so its effects can be unpredictable in complex scenes and cannot completely replace the certainty of manual exposure.
2. Limited adjustment range: Most camera modules have a limited exposure compensation range (e.g., ±2 or ±3 EV), which may not be sufficient in extreme lighting conditions.
3. Consistency Challenges: If the lighting conditions of a scene change dramatically, the AE algorithm's default brightness target will change accordingly. This can cause the same exposure compensation value to produce different results at different times, resulting in inconsistent images.

Use Cases of Exposure Compensation: How Exposure Compensation Works

After understanding how exposure compensation works, we can apply it to specific scenarios to address auto-exposure pain points.

1. Backlit Scenes: When a subject is against a bright background, the AE algorithm may be misled by the background's brightness, resulting in underexposure. In this case, setting a positive exposure compensation (such as +1 or +2 EV) can correct the subject's exposure.
2. Dark or Bright Scenes: When shooting pure black or pure white objects, the AE algorithm may attempt to average exposure to a medium gray. For example, when shooting snowy scenes, AE may cause the snow to appear grayish. In this case, setting a positive exposure compensation can restore the snow's pure whiteness.
3. Fine Adjustments: In some semi-controlled environments, if the AE default settings are consistently too bright or too dark, engineers can use exposure compensation to make a one-time correction without switching to full manual mode.

Summary

Automatic exposure and manual exposure are two core exposure strategies in embedded vision, each with its own unique advantages and disadvantages. Manual exposure offers stability and predictability, making it the preferred choice for controlled environments and quantitative analysis. Automatic exposure offers flexibility and convenience, making it the preferred choice for dynamic environments and general-purpose applications. Exposure compensation, on the other hand, offers a middle ground of refined control.

Understanding the relationship between these three options and making the right choice based on the application scenario is essential for every embedded vision engineer. This not only impacts image quality, but also the success of the machine vision algorithm.

Muchvision's Exposure Control Solutions

Is your project facing exposure control challenges? Contact our expert team today to provide professional exposure strategy consultation and camera module selection solutions, helping you build a high-performance embedded vision system!