An HVS-based no-reference perceptual quality assessment of JPEG coded images using neural networks
In this paper, we present a novel no-reference (NR) metric to assess the quality of JPEG-coded images. The features for predicting the perceived image quality are extracted by considering the key human visual sensitivity factors such as, edge amplitude, edge length, background activity and background luminance. The extracted features with the subjective test results are used to train a multi-layer perceptron (MLP) neural network. Experimental results show that the prediction of the trained neural network is very close to the mean opinion score (MOS). The subjective test results of the proposed metric are compared with the Wang-Bovik's NR blockiness metric. Further, this metric can be extended to assess the quality of the MPLG/H.26x compressed videos.