Depth Information Estimation-Based DIBR 3D Image Hashing Using SIFT Feature Points
Image hashing has been widely used for traditional 2D image authentication, content-based identification, and retrieval. Being different from the traditional 2D image system, virtual image pair is generated from the center image according to the corresponding depth image in the DIBR process. In one of the communication models for DIBR 3D image system, the content consumer side only receives the virtual images without performing DIBR operation. By this way, only a variety of copies for virtual image pairs could be distributed. This paper designs a novel DIBR 3D image hashing scheme based on depth information estimation using local feature points, by detecting the matched feature points in virtual image pair and dividing these feature points into different groups according to the corresponding depth information estimated to generate the hash vector. As the experiments shown, the proposed DIBR 3D image hashing is robust against most of the content-preserving operations.
KeywordsDepth image-based rendering (DIBR) DIBR 3D image hashing DIBR 3D image identification Depth information estimation
This work is supported by the National Natural Science Foundation of China (Grant Number: 61702224).
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