scholarly journals An Embedding Strategy Using Q-Ary Convolutional Codes for Large and Small Payloads

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1577
Author(s):  
Jyun-Jie Wang ◽  
Chi-Yuan Lin ◽  
Sheng-Chih Yang ◽  
Hsi-Yuan Chang ◽  
Yin-Chen Lin
Keyword(s):  
Convolutional Code ◽  
Convolutional Codes ◽  
Block Codes ◽  
Maximum Likelihood Decoding ◽  
Linear Block Codes ◽  
Embedding Efficiency ◽  
Matrix Embedding ◽  
Main Disadvantage ◽  
Embedding Strategy ◽  
Embedding Rate

Matrix embedding (ME) code is a commonly used steganography technique, which uses linear block codes to improve embedding efficiency. However, its main disadvantage is the inability to perform maximum likelihood decoding due to the high complexity of decoding large ME codes. As such, it is difficult to improve the embedding efficiency. The proposed q-ary embedding code can provide excellent embedding efficiency and is suitable for various embedding rates (large and small payloads). This article discusses that by using perforation technology, a convolutional code with a high embedding rate can be easily converted into a convolutional code with a low embedding rate. By keeping the embedding rate of the (2, 1) convolutional code unchanged, convolutional codes with different embedding rates can be designed through puncturing.

scholarly journals FPGA implementation of trellis decoders for linear block codes

2014 ◽  
Vol 12 ◽  
pp. 61-67
Author(s):  
S. Scholl ◽  
E. Leonardi ◽  
N. Wehn
Keyword(s):  
Forward Error Correction ◽  
Convolutional Codes ◽  
Block Codes ◽  
Point Of View ◽  
Linear Block Codes ◽  
Banyan Network ◽  
Hardware Architectures ◽  
Trellis Decoding ◽  
Forward Error ◽  
First Time

Abstract. Forward error correction based on trellises has been widely adopted for convolutional codes. Because of their efficiency, they have also gained a lot of interest from a theoretic and algorithm point of view for the decoding of block codes. In this paper we present for the first time hardware architectures and implementations for trellis decoding of block codes. A key feature is the use of a sophisticated permutation network, the Banyan network, to implement the time varying structure of the trellis. We have implemented the Viterbi and the max-log-MAP algorithm in different folded versions on a Xilinx Virtex 6 FPGA.

scholarly journals An Improved Product Code-Based Data Hiding Scheme

2018 ◽  
Vol 8 (11) ◽  
pp. 2119
Author(s):  
Wen-Rong Zhang ◽  
Yuh-Ming Huang
Keyword(s):  
Data Hiding ◽  
The Other ◽  
Two Dimensional ◽  
Optimal Solutions ◽  
Approximation Approach ◽  
One Dimensional ◽  
Product Code ◽  
Embedding Efficiency ◽  
Matrix Embedding ◽  
Embedding Rate

This paper explores the data hiding schemes which are based on the principle of matrix embedding. Under the same embedding rate, the efficiency of each data hiding scheme is evaluated by the metric of average embedding efficiency. In the literature, both the row-column embedding and the weight approximation embedding algorithms are sub-optimal solutions for the product code-based data hiding problem. For the former, it is still based on the concept of one-dimensional (1-D) toggle syndrome, and the concept of two-dimensional (2-D) toggle syndrome is directly adopted for the latter one. Data hiding with multiple embedding channels is the practice of hiding messages into hidden media many times. Here, two multi-channel embedding-based data hiding techniques—one is the 1-D toggle syndrome-based embedding scheme (1DTS-1), and the other is the improved weight approximation-based embedding scheme (2DTS-1), are presented. In the former, the proposed one-off decision technique is used to determine the locations of the required modification bits, and the amount of modification will be reduced through utilizing the characteristics of the linear code. With the technique of the former, in the latter, the amount of modification bits can be further reduced because that a toggle array with better structure is generated, which is more suitable for being assigned as the initial toggle array while applying the weight approximation approach. The experimental results show our proposed hybrid 1-D/2-D toggle syndrome-based embedding scheme (2DTS-1) has increased the embedding efficiency by 0.1149 when compared to the weight approximation embedding algorithm. Further, the embedding efficiency of the latter one can be further and significantly enhanced through the Hamming+1 technique.

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