A fast no reference image quality assessment using laws texture moments

The development of robust No-Reference Image Quality Assessment (NR-IQA) techniques continues to be a challenging problem. NR-IQA techniques are critical In numerous multimedia applications. Most existing techniques are distortion-specific, as they are only efficient when the type of distortion is known. In this work, we introduce a computationally efficient NR-IQA algorithm that uses basic filtering operations in spatial domain. The features are calculated using Laws' filters proven to be efficient in texture analysis. The image quality score is predicted using a simple Generalized Regression Neural Network. The proposed algorithm has low computational complexity, making it suitable for real-time applications. The performance of the proposed technique is confirmed, using the LIVE 2 image quality assessment dataset. The proposed approach is shown to provide excellent results that are robust across different distortions, and is computationally less expensive than most existing techniques.