Image Retargeting Using Blur Based Depth Saliency Descriptor

Image retargeting technique becomes research hot with the widely used digital images on different display screens,such as TV,iPhone,iPad and other wearable equipment.The objectives of image retargeting are important information preservation,less edge distortion during increasing/decreasing image size.The major existed content-aware methods perform well.However,there are two problems should be improved:the slight distortion appeared at the object edges and the structure distortion in the non-salient area.In order to solve the problems above,we propose a novel algorithm to calculate depth-based saliency based on the edge blur.According to psychological theories,people evaluate image quality based on multi-level judgments and comparison between different areas,both image content and structure.The paper proposes a new standard:the structure preserving in non-salient area.After observation and image analysis,blur(slight blur)is generally existed at the edge of objects.The blur feature is used to estimate the depth cue,named blur depth descriptor.It can be used in the process of saliency computation for balanced image retargeting results.In order to keep the structure information in non-salient area,the salient edge map is presented in Seam Carving process,instead of field-based saliency computation.The derivative blur-based depth from x-and y-direction can avoid the redundant energy seam around salient objects causing structure distortion.There are three comparison experiments between the proposed algorithm and selected classic algorithms:salient edge preservation experiment,large scale retargeting on high-resolution images experiment and average energy comparison.The promising results prove the feasibility of the proposed algorithm.