1.2.6 Display utility functions

Utility functions for images refer to mathematical functions or algorithms that are designed to evaluate or measure various aspects of an image’s quality, content, or characteristics. These functions are often used in image processing and computer vision applications to perform tasks such as image analysis, enhancement, compression, or evaluation. Here are some common utility functions for images:

1. Image Quality Metrics: These functions are used to assess the quality of an image, often in the context of image compression or transmission. Metrics like Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and others are used to compare the original image with a compressed or processed version to quantify the quality loss.

2. Histogram Analysis: Histogram-related utility functions help in understanding the distribution of pixel values in an image. Common functions include histogram equalization, histogram matching, and histogram-based thresholding.

3. Edge Detection: Utility functions like the Canny edge detector or the Sobel operator are used to identify edges and transitions in an image. Edge detection is crucial for tasks like object recognition and image segmentation.

4. Feature Extraction: These functions extract specific features or patterns from an image. Examples include the Histogram of Oriented Gradients (HOG) for object detection, Scale-Invariant Feature Transform (SIFT) for feature matching, and various deep learning-based feature extractors.

5. Image Transformation: Functions for geometric transformations like rotation, scaling, and perspective correction are used to modify or preprocess images for specific applications.

6. Color Space Conversion: Converting images from one color space to another, such as RGB to grayscale, HSV, or LAB, is a common utility for image processing tasks like image segmentation or color correction.

7. Noise Reduction: Functions like Gaussian blur, median filtering, and Wiener filtering are used to reduce noise in images, enhancing their quality.

8. Morphological Operations: Morphological operations like dilation, erosion, opening, and closing are used in image processing for tasks like shape analysis, object detection, and image enhancement.

9. Region of Interest (ROI) Extraction: Utility functions that help identify and extract specific regions or objects within an image, facilitating further analysis or processing.

10. Object Detection: Object detection algorithms, such as the Viola-Jones algorithm or deep learning-based methods like YOLO and Faster R-CNN, are used to locate and identify objects or specific patterns in an image.

11. Image Compression: Functions and algorithms used for image compression, like JPEG or PNG, to reduce the file size while preserving acceptable image quality.

12. Semantic Segmentation: Utility functions for image segmentation tasks that assign each pixel in an image to a specific object or class, enabling applications like autonomous driving and medical image analysis.

These utility functions are essential tools for image processing and computer vision tasks, enabling the analysis and manipulation of images for a wide range of applications, from medical imaging and facial recognition to image-based quality control and augmented reality.