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.