Biologically Inspired Blind Quality Assessment of Tone-Mapped Images

Currently, many tone mapping operators (TMOs) have been provided to compress high dynamic range images to low dynamic range (LDR) images for visualizing them on the common displays. Since quality degradation is inevitably induced by compression, how to evaluate the obtained LDR images is indeed a headache problem. Until now, only a few full reference (FR) image quality assessment metrics have been proposed. However, they highly depend on reference image and neglect human visual system characteristics, hindering the practical applications. In this paper, we propose an effective blind quality assessment method of tone-mapped image without access to reference image. Inspired by that the performance of existing TMOs largely depend on the brightness and chromatic and structural properties of a scene, we evaluate the perceptual quality from the perspective of color information processing in the brain. Specifically, motivated by the physiological and psychological evidence, we simulate the responses of single-opponent (SO) and double-opponent (DO) cells, which play an important role in the processing of the color information. To represent the textural information, we extract three features from gray-level co-occurrence matrix (GLCM) calculated from SO responses. Meanwhile, both GLCM and local binary pattern descriptor are employed to extract texture and structure in the responses of DO cells. All these extracted features and associated subjective ratings are learned to reveal the connection between feature space and human opinion score. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art blind quality assessment methods and is comparable with the popular FR methods on two recently published tone-mapped image databases.