Quality Assessment for Retargeted Images: A Review

Transmission, saving and many processing methods cause different damage in images. Image Quality Assessment (IQA) is necessary to benchmark processing algorithms, to optimize them, and to monitor the quality of images in quality control systems. Traditional quality metrics have low correlations with subjective perception. The key problem is to evaluate the distorted images as human do. Subjective quality assessment is more reliable but is cumbersome and time-consuming, so it is impossible to embed it in online applications. Therefore, many objective perceptual IQA models have been developed until now. Content-aware retargeting methods aim to adapt source images to target display devices with different sizes and aspect ratios so that salient areas will be less distorted. The size mismatch and the completely different distortions caused by retargeting have made common IQA methods useless in this area. Therefore, retargeted Image Quality Assessment (RIQA) methods are designed for this purpose. The quality of retargeted images is different depending to image content and retargeting algorithm. This paper provides a literature review and a new categorization of the current subjective and objective retargeted image quality measures. Also, we intend to compare and analyze the performance of these measures. It is demonstrated that the performance of RIQA methods can be further improved by using high-level descriptors in addition to low-level ones.