Saliency-Guided Quality Assessment of Screen Content Images
With the widespread adoption of multidevice communication, such as telecommuting, screen content images (SCIs) have become more closely and frequently related to our daily lives. For SCIs, the tasks of accurate visual quality assessment, high-efficiency compression, and suitable contrast enhancement have thus currently attracted increased attention. In particular, the quality evaluation of SCIs is important due to its good ability for instruction and optimization in various processing systems. Hence, in this paper, we develop a new objective metric for research on perceptual quality assessment of distorted SCIs. Compared to the classical MSE, our method, which mainly relies on simple convolution operators, first highlights the degradations in structures caused by different types of distortions and then detects salient areas where the distortions usually attract more attention. A comparison of our algorithm with the most popular and state-of-the-art quality measures is performed on two new SCI databases (SIQAD and SCD). Extensive results are provided to verify the superiority and efficiency of the proposed IQA technique.