Quality Evaluation for Image Retargeting With Instance Semantics

To meet the ever-increasing demand for devices with diversified displays, image retargeting has become a prevalent technique for adaptive image resizing. In practice, the retargeting operation inevitably causes impairments in the images; thus, image retargeting quality assessment (IRQA) is urgently needed and, can be used to guide algorithm optimization, selection and design. Unlike traditional image quality assessment, image retargeting introduces geometric distortions, which typically affect high-level image semantics. With this motivation, this paper presents a quality evaluation model for image retargeting based on INstance SEMantics (INSEM). Considering that the human visual system (HVS) perceives images highly dependent on apprehensible areas and that impairments in image retargeting mainly degrade the salient instances, an image instance is utilized as the basic semantic unit, and a top-down method is devised to extract instance-level semantic features for IRQA. In addition, taking into account the influence of semantic categories on the perception of retargeting quality, we further propose Semantic-based self-adaptive pooling (SSAP) to integrate instance-based semantic features. Finally, global features are incorporated to generate quality scores that are more consistent with people's perceptions. Extensive experiments and comparisons of three public databases, in terms of both intradatabase and cross-database settings, demonstrate the superiority of the proposed metric over state-of-the-art methods.