CNN Based No-Reference HDR Image Quality Assessment

Motivated by the problems of non-universality and over-reliance on the original reference image in High dynamic range(HDR) Image quality assessment(IQA), a convolutional neural network-based algorithm for no-reference HDR image quality assessment is proposed. The Salience detection by self-resemblance(SDSR) algorithm which extracts the salient regions of the HDR image, is used to simulate the human visual attention mechanism. Then a visual quality perception network for training quality prediction models is designed according to the visual characteristics of luminance and contrast sensitivity. And this network consists of an Error estimation network(Error-net), a Perceptual resistance network(PR-net) and a mixing function. The experimental results indicate that the method proposed has high consistency with subjective perception, and the value of assessment metrics Spearman rank-order correlation coefficient(SROCC), Pearson product-moment correlation coefficient(PLCC) and Root mean square error(RMSE)correspondingly reaches 0.941, 0.910 and 8.176 as well. It is comparable with classic full-reference HDR IQA methods.