At present, image privacy-preserving is mainly applied to the field of cloud computing, and since the recognition tasks for images or videos generally need to be visually visible, the privacy-preserving problem is often ignored. To solve this kind of problem, inspired by sparse representation for classification based on compressed sensing (CS) which is robust at the images with occlusions and disguises, an extended model of single-layer CS sampling is proposed to ensure the image is degraded and the content is gradually indiscernible after multilayer CS sampling and encoding, which can still be used for image recognition and achieve the purpose of privacy-preserving. To be able to effectively evaluate image content privacy-preserving for multilayer CS sampling and coding, a content privacy-preserving evaluation (MCS-CPPE) model for multilayer CS images based on human visual system (HVS) is proposed, which by measuring image contrast and extracting local binary pattern (LBP) feature because of degradation of contrast and image visual structure. Experiments with human visual correlation on the three constructed databases show that the proposed model has a better prediction performance and effect.

multilayer compressed sensing; image content privacy-preserving evaluation; contrast; LBP feature; human visual system