No-Reference Hdr Image Quality Assessment Method Based on Tensor Space

Abstract

The full-reference image quality assessment (IQA) method are limited in practical applications. Here we propose a no-reference quality assessment method for high dynamic range (HDR) images based on tensor space. First, the tensor decomposition is used to generate three feature maps of an HDR image, considering color and structure information of the HDR image. Second, for a given HDR image, the corresponding multi -scale manifold structure features are extracted from the first feature map. For the second and third feature maps of the HDR image, multi-scale contrast features are extracted. Finally, the extracted features are aggregated by support vector regression to obtain the objective quality score of the HDR image. Experimental results show that the proposed method is superior to some representative full and no-reference methods, and even superior to the full-reference HDR IQA method, HDR-VDP-2.2, on the Nantes database. The proposed method has a higher consistency with human visual perception.