Information Entropy: Image Quality Assessment Metrics

In this context, we require systematic evaluation of image quality. Assessing image quality encompasses multiple computational dimensions: 1. Resolution: Refers to the total number of pixels in an image. Higher resolution generally corresponds to clearer visual representation. In code implementation, this can be measured using MATLAB's size() function or Python's PIL.Image.size attribute to retrieve image dimensions in pixels. 2. Pixel Density: Denotes the number of pixels per inch (PPI). Higher pixel density typically enhances image clarity. This metric is crucial for display quality assessment and can be calculated using device specifications and image dimensions. 3. Color Fidelity: Image color quality depends on color depth (bits per pixel) and gamut. Higher color depth (e.g., 24-bit vs 8-bit) provides more accurate color representation. Color histogram analysis using OpenCV's calcHist() function or MATLAB's imhist() can quantify color distribution characteristics. 4. Contrast Ratio: Measures the luminance difference between brightest and darkest regions. Enhanced contrast improves image discernibility. Implementation involves calculating dynamic range through histogram analysis or using specialized functions like MATLAB's contrast(). 5. Noise Analysis: Refers to unwanted signal interference degrading image quality. Excessive noise reduces visual fidelity. Noise assessment algorithms include standard deviation calculation in uniform regions or frequency domain analysis using Fourier transforms. 6. Sharpness: Quantifies image clarity and edge definition. Higher sharpness yields clearer details. Computational methods involve edge detection algorithms (Sobel, Canny) or modulation transfer function (MTF) analysis to measure acutance. Therefore, comprehensive image quality evaluation requires integrated assessment of these parameters using multi-metric algorithms. Practical implementation might combine these metrics through weighted scoring systems or machine learning models for holistic quality prediction.