No reference image quality assessment with shape adaptive discrete wavelet features using neuro-wavelet model

No reference method for image quality assessment using shape adaptive wavelet features by applying neuro-wavelet model is proposed in this paper. Images usually consist of visual objects. Degradation of an image ultimately causes distortions to the objects present in the image. Distortions can change the shape of these objects. Quality assessment of an image cannot be said to be complete without assessing the quality of individual objects present in the image. Therefore, deviation in shape has to be quantified along with the quality assessment of an image. Shape Adaptive Discrete Wavelet Transform offers a solution to shape identification problem. The variations in magnitude of feature values are found not proportional to the amount of degradation due to the presence of other artifacts. Wavelet decomposition is applied to capture the small variations observed in extracted features. Separate back propagation neural network models are trained for quality assessment of all kind of images ranging from pristine to bad. Results show improvement in accuracy independent of image databases. It has been observed that the predicted score correlates well with the mean opinion score with 90% accuracy for LIVE dataset, 93% and 95% for TID2008 and TID2013 respectively.