A Novel Data Embedding Method Using Random Pixels Selecting

2013 ◽  
Vol 12 (7) ◽  
pp. 1299-1308 ◽  
Author(s):  
Ling Liu ◽  
Tungshou Chen ◽  
Chen Cao ◽  
Xuan Wen ◽  
Rongsheng Xie
Keyword(s):  
Data Embedding ◽  
Embedding Method

scholarly journals Improved Coefficient Recovery and Its Application for Rewritable Data Embedding

2021 ◽  
Vol 7 (11) ◽  
pp. 244
Author(s):  
Alan Sii ◽  
Simying Ong ◽  
KokSheik Wong
Keyword(s):  
Image Coding ◽  
Rate Distortion ◽  
Divide And Conquer ◽  
Storage Device ◽  
Data Embedding ◽  
Discrete Cosine Transformation ◽  
Recovery Method ◽  
Embedding Method ◽  
Coefficient Recovery ◽  
Recovery Methods

JPEG is the most commonly utilized image coding standard for storage and transmission purposes. It achieves a good rate–distortion trade-off, and it has been adopted by many, if not all, handheld devices. However, often information loss occurs due to transmission error or damage to the storage device. To address this problem, various coefficient recovery methods have been proposed in the past, including a divide-and-conquer approach to speed up the recovery process. However, the segmentation technique considered in the existing method operates with the assumption of a bi-modal distribution for the pixel values, but most images do not satisfy this condition. Therefore, in this work, an adaptive method was employed to perform more accurate segmentation, so that the real potential of the previous coefficient recovery methods can be unleashed. In addition, an improved rewritable adaptive data embedding method is also proposed that exploits the recoverability of coefficients. Discrete cosine transformation (DCT) patches and blocks for data hiding are judiciously selected based on the predetermined precision to control the embedding capacity and image distortion. Our results suggest that the adaptive coefficient recovery method is able to improve on the conventional method up to 27% in terms of CPU time, and it also achieved better image quality with most considered images. Furthermore, the proposed rewritable data embedding method is able to embed 20,146 bits into an image of dimensions 512×512.

scholarly journals The data embedding method

1996 ◽  
Author(s):  
M.T. II Sandford ◽  
J.N. Bradley ◽  
T.G. Handel
Keyword(s):  
Data Embedding ◽  
Embedding Method

Data embedding method

1996 ◽  
Author(s):  
Maxwell T. Sandford II ◽  
Jonathan N. Bradley ◽  
Theodore G. Handel
Keyword(s):  
Data Embedding ◽  
Embedding Method

Improved Data Embedding Method for Digital Image with Human Visual Systems

2014 ◽  
Vol 97 (11) ◽  
pp. 19-27
Author(s):  
Atsuhiro Fujii ◽  
Masayoshi Nakamoto ◽  
Mitsuji Muneyasu ◽  
Shuichi Ohno
Keyword(s):  
Digital Image ◽  
Data Embedding ◽  
Embedding Method ◽  
Visual Systems ◽  
Human Visual Systems

Improved Data Embedding Method for Digital Image with Human Visual Systems

2013 ◽  
Vol 133 (1) ◽  
pp. 185-192
Author(s):  
Atsuhiro Fujii ◽  
Masayoshi Nakamoto ◽  
Mitsuji Muneyasu ◽  
Shuichi Ohno
Keyword(s):  
Digital Image ◽  
Data Embedding ◽  
Embedding Method ◽  
Visual Systems ◽  
Human Visual Systems
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