A tone-mapped image (TMI) obtained from the corresponding high dynamic range (HDR) image induces artifacts and distortion, which might result in the loss of structure information and impaired color. By analyzing the visual characteristics of TMIs, this work proposes a robust blind visual quality evaluation method for TMIs by using gradient and chromatic statistics (VQGC). First, motivated by the perceptual mechanism that the human visual system (HVS) is sensitive to image structure variation, we employ the gradient features to measure structure degradation in TMIs. To predict structure distortion accurately, we compute the gradient magnitude and orientation to measure image structure variation, and the relative gradient magnitude and orientation are also computed to capture microstructure change. Second, the color invariance descriptors are utilized to capture the visual degradation of colorfulness by local binary pattern (LBP) on four chromatic feature maps. Finally, the gradient and chromatic features are combined together as the final quality-aware feature vector, which is applied to assess the perceptual quality of TMIs by support vector regression (SVR). Comparison experiments show that the performance of the proposed method is better than other existing blind quality assessment methods on public databases.

In this paper, we propose an effective blind quality assessment method for screen content images (SCIs), called perceptual quality measure by spatial continuity (PQSC). With the center-surround mechanism in the human visual system (HVS), the proposed method extracts the statistical features on chromatic and textural variations in SCIs to measure the visual distortion. First, by considering the chromatic continuity between spatially adjacent pixels, photo-metric invariant chromatic descriptors are extracted as zero-order and first-order features. Second, motivated by the perceptual mechanism that the HVS is sensitive to image texture variation, we employ local ternary pattern operator to effectively depict the spatial continuity of texture. With these extracted chromatic and textural features, we further adopt histogram to compute the statistical chromatic and textural features. Support vector regression (SVR) is used to train the quality prediction model from visual features to human ratings. Experimental results on three public benchmark databases demonstrate that the performance of our method is superior to the current blind image quality assessment methods, even better than some full reference image quality assessment counterparts.

Depth image based rendering (DIBR) has been widely used to generate different virtual viewpoints of the same scene from the new perspective. However, DIBR tends to introduce annoying artifacts including blurring, discontinuity, blocking, and stretching, etc.. Thus, to improve DIBR performance, it is important to accurately measure the visual quality of synthesized views. In this paper, we propose a novel and effective no reference (NR) quality assessment method for 3D synthesized views by local variation and global change (LVGC). More specifically, we firstly compute the Gaussian derivatives for the input image to extract structure and chromatic features. Then, we use the local binary pattern (LBP) operator to encode the structure and chromatic feature maps, which are used to calculate quality-aware features to measure the local structural and chromatic distortion. Besides, we extract luminance features by global change to evaluate the naturalness of 3D synthesized views. With these extracted features, we utilize random forest regression (RFR) to train the quality prediction model from visual features to human ratings. Experimental results on three public benchmark databases demonstrate the effectiveness of our method on estimating visual quality of 3D synthesized views.