1. Spectrum and Illumination

Select illumination sources to deliver sufficient information for target computer‑vision tasks. For instance, skin diagnostic instruments frequently capture data under visible‑light, polarized‑light and UV‑light sources; plant‑analysis applications commonly deploy multispectral cameras. (For more details on multispectral cameras, refer to the imaging article Multispectral vs Hyperspectral Imaging). Accordingly, choosing appropriate light sources and specific spectral bands is the primary consideration for any machine‑vision project.

As illustrated in the reference figure, distinct light sources and spectral bands yield image data with different feature sets.

2. Brightness, Motion Blur and Noise

These three metrics are closely interrelated and often trade off against one another, requiring careful balancing according to real‑world requirements.

Brightness impacts algorithm inference accuracy, and interacts with algorithm models and training datasets. As shown in the referenced chart, when MobileNet‑SSD and RFCN‑ResNet101 are trained with varying EV values, peak mAP for each algorithm occurs at different EV points.

Another example: IQ images generated by Algolux automated training tools may exhibit poor subjective visual quality (e.g., over‑bright vehicle bodies), yet remain correctly interpretable by target algorithms.

When adjusting brightness: longer exposure time may introduce motion blur and degrade recognition performance; shorter exposure paired with higher gain amplifies noise and increases hot pixels.

The sample reference demonstrates six test images where minor brightness changes to individual pixels alter algorithm outputs. In the top‑left example, adding one bright spot causes the VGG network to classify the vessel as “Car” instead of “Ship”.

Therefore for computer‑vision projects, engineers must evaluate:

• Sensor QE (quantum efficiency) and dynamic range;
• Lens aperture (light‑gathering capacity, depth of field, etc.);
• Image tuning strategies: balance exposure time and gain, configure HDR parameters, and suppress hot pixels.

3. Contrast

Reduced contrast degrades subjective visual quality and lowers machine‑vision detection accuracy.

Although contrast degradation affects human observers and AI algorithms in broadly similar ways, optimal ISP tuning for human viewing does not always match algorithm‑oriented tuning. The night‑scene and rainy‑scene comparison illustrates this point: the “Atlas‑optimized” tuning profile delivers superior detection success rates for machine‑vision workloads.

Stray light, surface contamination and ISP module tuning all exert influence on image contrast.

4. Image Distortion

Geometric distortion modifies raw scene representation. Neural‑network perception differs between original images and distorted variants, and shifts according to distortion magnitude.

5. Resolution / Sharpness

Sharpness directly affects both human viewing and machine‑vision performance. Multiple hardware factors determine camera sharpness. Its impact on computer vision differs from conventional subjective image‑quality evaluation. MTF / SFR performance and angular resolution are both critical for machine‑vision tasks.

6. Color Accuracy

Color inaccuracies introduce misclassification errors for both classification and segmentation algorithms.