Image Quality Assessment

Image Quality Assessment (IQA) is a crucial research field in computer vision and image processing, primarily used to evaluate visual fidelity after image compression, transmission, or enhancement. MATLAB, as a powerful mathematical computing tool, is commonly employed to implement various assessment algorithms.

Common IQA methods can be classified into two categories: subjective and objective evaluation:

Subjective Evaluation: Relies on human observers' intuitive perception of image quality, typically using scoring systems like MOS (Mean Opinion Score). While accurate, this method is time-consuming and difficult to scale for large applications.

Objective Evaluation: Quantifies image quality through mathematical models. Key algorithms include: PSNR (Peak Signal-to-Noise Ratio): Calculates the Mean Squared Error between original and distorted images. Higher values indicate better quality, but it has limited correlation with human visual perception. SSIM (Structural Similarity Index): Compares images based on luminance, contrast, and structure, better aligning with human visual system characteristics. VIF (Visual Information Fidelity): Measures information loss using information theory principles. MSE (Mean Squared Error): Simple and straightforward, but may not accurately reflect perceptual differences.

In MATLAB implementation, images typically need to be converted to grayscale or processed channel-wise, leveraging matrix operations for efficient metric computation. For instance, SSIM involves sliding window operations to compare local image patches, while PSNR calculates global pixel-level differences. Practical applications often combine multiple metrics for comprehensive evaluation to overcome limitations of single algorithms.

Advanced Considerations: Deep learning has recently been introduced to quality assessment, where trained neural networks learn human scoring patterns to handle complex distortion types (e.g., blur, noise, artifacts). However, this approach requires substantial annotated data for training.