Blind Quality Assessment of Tone-Mapped Images Based on Visual-Processing Features

Albeit high dynamic range (HDR) images contain way richer information than low dynamic range (LDR) images, they need to be tone mapped for the visualization on traditional display devices. The community has developed diverse tone-mapping operators (TMOs), and they produce images with varying qualities. Therefore, developing effective evaluation methods consistent with human visual perception is an urgent necessity for determining the best TMO in specific scenarios, or accordingly optimizing the parameters of a certain TMO. Towards this end, we propose a new blind quality assessment model by simulating the mechanism of human visual processing (HVS), which can adaptively adjust the sensitivity according to the chromatic properties of scenes at the beginning and well represent the perception process. Specifically, we first obtain all the retinal response maps of four visual sensitivities from three opponent color channels. After that, gradient similarities in each color channel are calculated as global features to represent the procedure of visual experience, and local mean values and standard deviations are computed in the brightest and darkest regions of the maps to measure the distortions caused by over- and under-exposure. Meanwhile, the maps’ natural scene statistics (NSS) are utilized as the third set of features. Finally, all these features are blended for quality assessment by support vector regression (SVR). Extensive experiments on two public benchmark databases show our method correlates highly with subjective perception and outperforms other state-of-the-art quality assessment methods.