scholarly journals Reversible data embedding for vector quantization compressed images using search-order coding and index parity matching

2014 ◽  
Vol 8 (6) ◽  
pp. 899-906 ◽  
Author(s):  
Chuan Qin ◽  
Chin-Chen Chang ◽  
Gwoboa Horng ◽  
Ying-Hsuan Huang ◽  
Yen-Chang Chen
Keyword(s):  
Vector Quantization ◽  
Data Embedding ◽  
Compressed Images ◽  
Search Order ◽  
Reversible Data Embedding

scholarly journals Reversible Data Hiding Scheme with High Embedding Capacity Using Semi-Indicator-Free Strategy

2013 ◽  
Vol 2013 ◽  
pp. 1-11
Author(s):  
Jiann-Der Lee ◽  
Yaw-Hwang Chiou ◽  
Jing-Ming Guo
Keyword(s):  
Vector Quantization ◽  
Data Hiding ◽  
Reversible Data Hiding ◽  
Embedding Capacity ◽  
Bit Rate ◽  
Secret Data ◽  
Compressed Images ◽  
Search Order ◽  
High Embedding Capacity ◽  
Embedding Rate

A novel reversible data-hiding scheme is proposed to embed secret data into a side-matched-vector-quantization- (SMVQ-) compressed image and achieve lossless reconstruction of a vector-quantization- (VQ-) compressed image. The rather random distributed histogram of a VQ-compressed image can be relocated to locations close to zero by SMVQ prediction. With this strategy, fewer bits can be utilized to encode SMVQ indices with very small values. Moreover, no indicator is required to encode these indices, which yields extrahiding space to hide secret data. Hence, high embedding capacity and low bit rate scenarios are deposited. More specifically, in terms of the embedding rate, the bit rate, and the embedding capacity, experimental results show that the performance of the proposed scheme is superior to those of the former data hiding schemes for VQ-based, VQ/SMVQ-based, and search-order-coding- (SOC-) based compressed images.

A robust and high capacity data hiding method for JPEG compressed images with SVD-based block selection and advanced error correcting techniques

2021 ◽  
pp. 1-11
Author(s):  
Kusan Biswas
Keyword(s):  
Data Hiding ◽  
High Capacity ◽  
Error Correcting Codes ◽  
Human Vision ◽  
Data Embedding ◽  
Compressed Images ◽  
Matrix Encoding ◽  
Dct Coefficients ◽  
Capacity Data ◽  
Value Decomposition

In this paper, we propose a frequency domain data hiding method for the JPEG compressed images. The proposed method embeds data in the DCT coefficients of the selected 8 × 8 blocks. According to the theories of Human Visual Systems  (HVS), human vision is less sensitive to perturbation of pixel values in the uneven areas of the image. In this paper we propose a Singular Value Decomposition based image roughness measure (SVD-IRM) using which we select the coarse 8 × 8 blocks as data embedding destinations. Moreover, to make the embedded data more robust against re-compression attack and error due to transmission over noisy channels, we employ Turbo error correcting codes. The actual data embedding is done using a proposed variant of matrix encoding that is capable of embedding three bits by modifying only one bit in block of seven carrier features. We have carried out experiments to validate the performance and it is found that the proposed method achieves better payload capacity and visual quality and is more robust than some of the recent state-of-the-art methods proposed in the literature.

scholarly journals A Distortion-Free Data Hiding Scheme for Triangular Meshes Based on Recursive Subdivision

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Yuan-Yu Tsai
Keyword(s):  
Principal Component Analysis ◽  
Data Hiding ◽  
Similarity Transformation ◽  
Principal Component ◽  
Secret Message ◽  
Subdivision Scheme ◽  
Data Embedding ◽  
Embedding Capacity ◽  
Free Data ◽  
Reversible Data Embedding

This study adopts a triangle subdivision scheme to achieve reversible data embedding. The secret message is embedded into the newly added vertices. The topology of added vertex is constructed by connecting it with the vertices of located triangle. For further raising the total embedding capacity, a recursive subdivision mechanism, terminated by a given criterion, is employed. Finally, a principal component analysis can make the stego model against similarity transformation and vertex/triangle reordering attacks. Our proposed algorithm can provide a high and adjustable embedding capacity with reversibility. The experimental results demonstrate the feasibility of our proposed algorithm.

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