Documentation.md

# Documentation

# 1: Introduction on Image Blur:

Image Quality Check is a process of evaluating and assessing the visual quality of an image. It involves analyzing various factors that contribute to the image's perceived quality, such as sharpness, clarity, color accuracy, noise level, and overall visual appeal.

The goal of image quality check is to determine if an image meets certain quality standards or criteria, which can vary depending on the application or industry. For example, in photography, image quality is crucial for producing high-resolution and sharp images. In medical imaging, image quality is essential for accurate diagnosis and analysis. In computer vision and machine learning, image quality affects the performance of algorithms and models.

### To perform image quality checks, various metrics and algorithms can be used. These may include:
- Sharpness Metrics: Measures the level of sharpness or blurriness in an image. Common metrics include the Variance of Laplacian, Edge Contrast, or Frequency-Based Metrics.
- Noise Estimation: Quantifies the level of noise present in an image. Different noise models and estimation techniques can be used, such as Gaussian noise estimation or wavelet-based methods.
- Color Accuracy: Evaluates the accuracy and consistency of colors in an image. Color metrics like Delta E or Color Histograms can be used to compare the image's colors against reference standards.
- Compression Artifacts: Checks for artifacts introduced during image compression, such as blockiness or banding.
- Subjective Evaluation: In addition to objective metrics, subjective evaluation by human observers can provide valuable insights into the image quality perception.

Image quality check is essential in various domains, including photography, healthcare, manufacturing, and digital media. By ensuring high-quality images, it helps to enhance user experience, enable accurate analysis, and maintain data integrity.

Automating image quality checks through software applications, like the one we discussed earlier, streamlines the evaluation process, improves efficiency, and provides consistent and reliable results.