No-reference image quality assessment using extreme learning machines

In this paper, a No-Reference Image Quality Assessment (NR-IQA) algorithm is implemented with the help of Extreme Learning Machine (ELM) using spatial and spectral features. ELMs are single hidden layer feed-forward neural networks that provides optimum solution in a single iteration, hence ELMs can be used for performing classification and regression at high speeds. Proposed NR-IQA algorithm can quantify the amount of distortion for images caused by JPEG compression, JPEG2000 compression, Additive White Gaussian Noise, Gaussian Blurring effect and Rayleigh's Fast Fading effects. The proposed algorithm is evaluated using LIVE IQA database via Spearman's Ranked Ordered Correlation Coefficient (SROCC) and Root Mean Square Error (RMSE). The proposed algorithm outperforms existing NR-IQA methods.