No-Reference Image Quality Assessment Based on HVS

With the rapid development of the usage of digital imaging and communication technologies, there appears a great demand for fast and practical approaches for image quality assessment algorithms which can match human judgments accurately. In this paper, we draw the human visual characteristics into no-reference image quality assessment field, and propose a no-reference image quality assessment method based on the PSO-epsilon-SVR for JPEG compressed images. Firstly, we analyse the 5x5 filter operator and propose a 3x3 filter operator for 5x5's shortcomings. Then we use the 3x3 filter operator to extract the texture features of the distorted image, and extract the contrast and edge structure features, After that, we get the MTSC value which comes from these characteristics fusion. Moreover, we use the MTSC as the input object to establish prediction model of no-reference image quality assessment based on PSO-epsilon-SVR. In order to achieve the best effect, we use particle swarm optimization algorithm to find the optimal parameters of epsilon-SVR. Finally, the image quality is predicted by this method. The experimental results show that this method has good predictive ability, and its predictive value is high consistent with the DMOS value. The results can better simulate the human visual characteristics. And the method proposed in this paper is superior to the traditional evaluation algorithm in some performances.