In order to display a high dynamic range (HDR) image on a standard monitor, tone-mapping operators (TMOs) aim to compress HDR images into low dynamic range tone-mapped (TM) images. To accurately evaluate the performance of different TMOs, this paper proposes a no-reference image quality assessment (IQA) method for TM images. Firstly, the image is divided into dark area, middle area and bright area by using clustering algorithm. The entropy and area ratio features are extracted from three areas mentioned above and the saliency area that is detected by the proposed method. Then the natural scene statistics features of the luminance channel and RGB color channels of TMI are used to assess the luminance naturalness and chrominance naturalness, respectively. Finally the support vector regression module is utilized to yield a quality score of the TM images. The experimental results on the tone-mapped image database (TMID) show the effectiveness of the proposed algorithm. Compared with the existing representative IQA methods, the proposed method has better performance.
Light field has richer scene information than traditional images, including not only spatial information but also directional information. Aiming at multiple distortion problem of dense light field, combining with spatial and angular domain information, a light field image quality assessment method based on dense distortion curve analysis and scene information statistics is proposed in this paper. Firstly, the mean difference between all multi-view images in the angular domain of dense light field is extracted, and a corresponding distortion curve is drawn. Three statistical features are obtained by fitting the curve, which are slope, median and peak, respectively represent the distortion deviation, interpolation period and the maximum distortion. Then, the mean information entropy and mean gradient magnitude of the light field are extracted as the global and local features of the spatial domain. Finally, the extracted features are trained and tested by the Support Vector Regression. The experiment is conducted on the public MPI dense light field database. Experimental results show that the PLCC of the proposed method is 0.89, better than the existing methods, especially for different types of distorted contents.