What Defines Good Image Quality? 

Human eye vs. Machine Perception 

Image quality definition depends on the application. For the human eye, sharpness, brightness, color accuracy, minimal noise, and low optical distortion define a “good” image. Such images also need to display well on screens and print reliably. Embedded machine vision, in contrast, prioritizes sensitivity and feature extraction over aesthetics. Embedded systems often operate in linear color space and are optimized for low-light performance rather than color accuracy. 

Raw Image Fundamentals 

The foundation of image quality is the sensor and optics. Quantum efficiency, pixel size, and dynamic range determine how well a sensor converts light into electrical signals. Consumer devices prioritize accurate color reproduction with fine-tuned color filter arrays, while industrial sensors trade some color fidelity for sensitivity and near-infrared response. Modern smartphones use quad-Bayer patterns and pixel binning to maintain high sensitivity in low light.  

From Raw to Final Image 

Processing a raw image involves corrections like black level, defective pixel removal, color correction, and white balance. Consumer devices go further with gamma correction, noise reduction, sharpening, HDR, and AI-driven enhancements to create visually appealing results. Industrial systems often stop at basic corrections to balance performance, cost, and computational efficiency. 

Consumer vs. Industrial Performance 

A comparison between the iPhone 15 and the FRAMOS FSM:GO-IMX678 module showed that raw image quality results are very similar. Differences in final image appearance are mostly due to smartphone enhancements such as HDR, noise reduction, and AI processing. Industrial sensors focus on low-light sensitivity and reliability, delivering strong performance even with less computational processing.  

Conclusion 

Good image quality depends on the application. Consumer devices rely on advanced processing to meet human expectations, while industrial systems optimize for sensitivity, low-light performance, and cost efficiency. Understanding these differences helps in selecting the right imaging system for each use case.