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In this paper, we propose a novel no reference quality assessment method by incorporating statistical luminance and texture features (NRLT) for screen content images (SCIs) with both local and global feature representation. The proposed method is designed inspired by the perceptual property of the human visual system (HVS) that the HVS is sensitive to luminance change and texture information for image perception. In the proposed method, we first calculate the luminance map through the local normalization, which is further used to extract the statistical luminance features in global scope. Second, inspired by existing studies from neuroscience that high-order derivatives can capture image texture, we adopt four filters with different directions to compute gradient maps from the luminance map. These gradient maps are then used to extract the second-order derivatives by local binary pattern. We further extract the texture feature by the histogram of high-order derivatives in global scope. Finally, support vector regression is applied to train the mapping function from quality-aware features to subjective ratings. Experimental results on the public large-scale SCI database show that the proposed NRLT can achieve better performance in predicting the visual quality of SCIs than relevant existing methods, even including some full reference visual quality assessment methods. [ABSTRACT FROM PUBLISHER]

It is very important to extract high quality texture features from images. This is, however, often laborious, because the randomness in the color distribution patterns for texture elements makes texture measurement very difficult, despite these elements having a very similar visual appearance. In this paper, we propose the use of multi-scale color histograms to measure the effect of color distribution patterns efficiently and without having to compute the actual patterns, which saves considerable effort. Meanwhile, the hue-saturation-intensity color model is mainly adopted to take the advantage of human visual experiences in texture recognition. We discuss and validate the effectiveness and efficiency of our method by applying to various benchmarks. The results show that we can extract quality dominant textures automatically in real time, and faster by several orders of magnitude than existing methods. [ABSTRACT FROM AUTHOR]