No-Reference Image Quality Assessment (NR-IQA)
- Login to Download
- 1 Credits
Resource Overview
Detailed Documentation
This article discusses the concept of no-reference image quality assessment (NR-IQA). NR-IQA refers to evaluating image quality by processing an input image and generating a quality metric score, typically scaled between 0 and 100. This assessment method enables rapid and accurate quality judgment without relying on any reference images. Through implementing NR-IQA algorithms—which commonly involve feature extraction techniques (such as natural scene statistics analysis or deep learning features) and regression models to map features to quality scores—we can better understand visual perception characteristics and take appropriate measures to enhance image quality. Common implementations include using Python/Matlab with libraries like TensorFlow or PyTorch for deep learning-based approaches, or traditional methods using handcrafted features with SVM/random forest regressors.
- Login to Download
- 1 Credits