Scalable Image Self-Embedding Based on Dual-Rate SPIHT-LDPC Reference Generation Scheme

Abstract

Image Self-Embedding is a method of embedding two sets of data into the original image, authentication data for tamper detection and reference data for image recovery. In this paper, a scalable self-embedding method is proposed based on dual-rate source-channel coding for reference data generation. The proposed method uses Set Partitioning in Hierarchical Tree (SPIHT) algorithm for source coding and Low-Density Parity Check (LDPC) for channel coding. Accordingly, the proposed recovery system provides higher reconstruction quality at low tampering rates, while it can handle higher tampering rates with less reconstruction quality. Therefore, the proposed method has the ability of both preserving the image quality and recovering higher tampering rates. Simulation results show noticeable improvements compared with the related self-embedding methods in the literature. His research interests include content-based image retrieval, pattern recognition and medical image processing.

Region‐based scalable self‐recovery for salient‐object images

“…The MTL value is determined according to the amount of generated redundancy data with respect to the reference data. In [24,25], we proposed a solution for the MTL problem (termed scalable self‐recovery). In the present study, this method is improved for region‐based recovery.…”