Neural Network-based Prediction of Visual Quality for Noisy Images
Andrii Rubel, Oleksii S. Rubel, Владимир Васильевич Лукин · 2019 IEEE 15th International Conference on the Experience of Designing and Application of CAD Systems (CADSM) · 2019
Noise is one of the main factors that degrade image visual quality. Assessment of perceptual quality of noisy images is of critical importance for imaging systems and image processing application. In this paper, we propose a framework that employs multi-layer neural networks to predict visual quality for noisy images. In particular, the proposed method utilizes quite simple features extracted in discrete cosine transform (DCT) domain for predicting values of full-reference visual quality metrics for noisy images. The neural networks are trained and tested on noisy images corrupted by additive white Gaussian noise (AWGN). The experimental results show that prediction is quite accurate and fast. Source codes of the proposed method and datasets will be available at https://github.com/ViA-RiVaL/Blind-Noisy-Image-Visual-Quality-Prediction.