A Brief Discussion on Factors Affecting Image Quality

Source：Shenzhen Kai Mo Rui Electronic Technology Co. LTD2026-03-30

1. Factors Related to Image Sensors

1.1 Aliasing (Moiré Effect)

Aliasing occurs when an image undergoes discrete sampling and the sampling frequency is lower than the spatial frequency of the target. Striped or banded patterns known as the moiré effect can be observed, similar to the visual phenomenon generated when two layers of fabric overlap.

Aliasing exerts the most significant impact on the photoelectric conversion process of image sensors for the following reasons:(a) Moiré fringes caused by post-shooting image processing can be eliminated through proper optimization, while aliasing generated during image sensor sampling cannot be removed fundamentally.(b) For conventional monochrome image sensors equipped with color filter arrays, aliasing usually appears in the form of color distortion. Since the color sampling frequency is lower than the luminance sampling frequency, it becomes a critical factor degrading image quality.

Color moiré effect is commonly adopted as a benchmark for evaluating image quality, which frequently emerges when the target contains high-frequency components. It manifests as false colored edges, namely incorrect color fringes appearing on the boundaries of black-and-white patterns.

When capturing high-frequency black-and-white targets, the severity of the color moiré effect is evaluated by detecting false color artifacts. The CZP (Circular Zone Plate) chart is the most widely used test pattern—a two-dimensional frequency scanning chart serving as an essential tool for designing and evaluating optical low-pass filters.

1.2 Smear and Image Lag

Smear refers to the phenomenon where the sensor output remains unchanged for a short duration; if the phenomenon persists for an extended period or permanently, it is defined as image residual lag.

Solid-state image sensors are generally free from permanent image lag. Nevertheless, smear may occur when the charge transfer efficiency is insufficient or the pixel reset charge is inadequate.

1.3 Dark Current

An image sensor is a semiconductor device. Even without incident light, thermal effects induce leakage charge inside the sensor, which is referred to as dark current. Spatially uniform dark current can be offset by subtracting the output value of optically shielded black pixels. However, dark current varies across individual pixels and introduces random noise into images. Additionally, charge accumulation within the charge transfer channels also generates dark current. Dark current becomes a prominent noise source when the charge transfer time in pixel channels is unstable, such as when charge transfer is paused or forcibly halted.

The noise level is proportional to two parameters:

Accumulation time of noise generation within pixels
Waiting time for noise generation in charge transfer channels

In practical applications, dark current is strongly temperature-dependent, and this correlation must be taken into consideration.

1.4 Pixel Defects

During the manufacturing of image sensors, impurity contamination and other process flaws result in defective pixels characterized by excessive dark current, readout signal failure, or low photosensitivity. These faulty pixels output fixed pure white or pure black signals, known as pixel defects.

Pixel defects are typically corrected by interpolating data from adjacent normal pixels to avoid visible anomalies in camera outputs. Severe concentrations of pixel defects, however, will inevitably compromise overall image quality.

1.5 Blooming and Smear Leakage

Intense incident light on the sensor surface triggers abnormal signal output. Blooming happens when pixel charges reach saturation and overflow into neighboring pixels. For CCD image sensors, smear leakage occurs when long-wavelength light penetrates the silicon substrate of the sensor chip.

1.6 White Clipping, Color Deficiency and Solid Black Areas

White level clipping, color missing and uniform dark regions are common artifacts. Two primary causes include insufficient dynamic range of the image sensor and poorly controlled sensor operating parameters.

White clipping arises when the sensor captures bright areas with illuminance exceeding its saturation threshold. Saturated signal values are uniformly recorded, forming completely featureless solid white regions in the image.

Color deficiency occurs when only one or two color channels deliver valid output from saturated sensor areas, severely disrupting color balance and deteriorating visual fidelity.

Solid black areas lose detailed information due to dynamic range limitations; during image restoration, the grayscale brightness drops to an indistinguishable low level visually. The core cause of solid black artifacts is the loss of grayscale information across both the image sensor and display playback systems.

1.7 Spatial Random Noise & Fixed Pattern Noise

Random noise distributes irregularly across the entire frame, exerting a minor and generally acceptable influence on image quality.

Fixed pattern noise, such as white stripe noise and ingress noise, is far more perceptible and severely degrades imaging performance.

1.8 Thermal Noise & Flicker Noise

Thermal noise is introduced by sensor amplifier circuits and preprocessing modules responsible for receiving sensor output signals. It can be reduced by cooling measures but cannot be eliminated entirely.

As pixel dimensions shrink continuously, quantum effects become non-negligible, causing random fluctuations in sensor signal amplitude. Even with a uniformly illuminated static target, output signals still fluctuate randomly over time and space. The amplitude of flicker noise is proportional to the root mean square of photon quantity, and the signal-to-flicker-noise ratio is proportional to the root mean square of the signal value.

2. Factors Related to Lenses

2.1 Lens Flare & Ghosting

Lens flare and ghosting are image interferences induced by stray light reaching the sensor surface, mainly resulting from internal reflections on lens surfaces and within the lens barrel structure.

2.2 Distortion

All practical lenses produce optical distortion to varying degrees, undermining the geometric similarity between the real object and the captured image. Prime lenses feature fixed distortion characteristics, while zoom lenses exhibit variable distortion at different focal lengths. In general imaging scenarios, lower distortion delivers superior image performance.

2.3 Chromatic Aberration

Lenses feature varying refractive indices for light of different wavelengths, leading to color fringing artifacts. Multi-element lens designs are adopted to correct chromatic aberration and other optical aberrations.

2.4 Depth of Field

Photography converts three-dimensional real-world scenes into two-dimensional planar images. Points precisely at the lens focal plane are captured with optimal resolution, while off-focus areas appear blurred—blur intensity increases with the distance from the focal point. Depth of field defines the effective distance range where blurriness remains within an acceptable visual threshold.

Depth of field values can be accurately calculated using fundamental optical parameters of the lens.

3. Factors Related to Signal Processing

3.1 Quantization Noise

Digital cameras convert analog illuminance values into digital data for recording. During analog-to-digital conversion (ADC), continuous analog light signals are discretized into multi-bit digital values, inevitably generating bit errors and quantization errors. High-precision systems suffer obvious image degradation from quantization noise, whereas such noise can be ignored in low-precision imaging systems.

3.2 Compression Noise

JPEG, a common standard format for interchangeable image files, employs irreversible lossy compression. This algorithm discards partial image details to achieve a high compression ratio. Compression noise refers to extra noise artifacts introduced when increasing the JPEG compression ratio.

3.3 Power Line Noise & Clock Noise

Power line noise and clock noise fall into the category of ingress noise. Switch control circuits for power supply and clock pulse signals interfere with the analog front-end of cameras before ADC triggering, producing bright spots or bright line artifacts in images. Evaluation methodology involves capturing a pure dark field at maximum sensor sensitivity, then observing noise distribution and measuring peak noise values. Generated by circuit operation, this noise varies with ambient temperature and self-heating of the camera hardware.

4. Factors Related to System Control

4.1 Focus Error

Even with optically ideal lenses, inaccurate focusing fails to deliver high-quality images. Targets outside the focal plane appear blurred, and blurring aggravates as defocus offset increases. In high-frequency detail regions, lens response declines sharply, resulting in severe losses of resolution and sharpness. Conversely, reduced overall resolution can mitigate aliasing side effects to a certain extent.

4.2 Exposure Error

Severe exposure errors cause signal overflow beyond the sensor dynamic range, leading to irreversible loss of image details and unsatisfactory imaging results. Moreover, improper exposure elevates various types of noise throughout the system, including quantization noise.

4.3 White Balance Error

White balance inaccuracies prevent accurate color reproduction in photography and directly impair overall image color fidelity.

4.4 Influence of Lighting Flicker

Alternating current power supplies induce periodic flicker in artificial lighting sources (especially fluorescent lamps), interfering with camera control systems and corrupting captured image frames.

5. Other Influencing Factors

5.1 Visual Adaptation

Human visual light adaptation and color adaptation characteristics must be considered during image processing and visual evaluation of captured scenes.

5.2 Camera Shake

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