In this study, the authors proposed a new scheme which performs both lossless compression and encryption of images. Lossless compression is done by arithmetic coding (AC) while encryption is based on a chaos-based pse...
详细信息
In this study, the authors proposed a new scheme which performs both lossless compression and encryption of images. Lossless compression is done by arithmetic coding (AC) while encryption is based on a chaos-based pseudorandom bit generator. Hence, they proposed to incorporate recent results of chaos theory into AC in order to shuffle the cumulative frequency vector of input symbols chaotically to make AC secure and the decoding process completely key-dependent. Many other techniques based on varying the statistical model used by AC have been proposed in literature, however, these techniques suffer from losses in compression efficiency that result from changes in entropy model statistics and are weak against known attacks. The proposed compression-encryption techniques were developed and discussed. The numerical simulation analysis indicates that the proposed scheme is highly satisfactory for image encryption without any AC compression efficiency loss. In addition, it can be incorporated into any image compression standard or algorithm employing AC as entropy coding stage, including static, adaptive and context-based adaptive models, and at any level, including bit, pixel and predictive error pixel levels.
Recently, arithmetic coding has attracted the attention of many scholars because of its high compression capability. Accordingly, in this paper, a method that adds secrecy to this well-known source code is proposed. F...
详细信息
Recently, arithmetic coding has attracted the attention of many scholars because of its high compression capability. Accordingly, in this paper, a method that adds secrecy to this well-known source code is proposed. Finite state arithmetic code is used as source code to add security. Its finite state machine characteristic is exploited to insert some random jumps during source coding process. In addition, a Huffman code is designed for each state to make decoding possible even in jumps. Being prefix-free, Huffman codes are useful in tracking correct states for an authorized user when he/she decodes with correct symmetric pseudo-random key. The robustness of our proposed scheme is further reinforced by adding another extra uncertainty by swapping outputs of Huffman codes in each state. Several test images are used for inspecting the validity of the proposed Huffman finite state arithmetic coding (HFSAC). The results of several experimental key space analyses, statistical analyses, key and plaintext sensitivity tests show that HFSAC with a little effect on compression efficiency provides an efficient and secure method for real-time image encryption and transmission.
In this paper, a field-programmable gate array (FPGA) based enhanced architecture of the arithmetic coder is proposed, which processes two symbols per clock cycle as compared to the conventional architecture that proc...
详细信息
ISBN:
(纸本)9781424442959
In this paper, a field-programmable gate array (FPGA) based enhanced architecture of the arithmetic coder is proposed, which processes two symbols per clock cycle as compared to the conventional architecture that processes only one symbol per clock. The input to the arithmetic coder is from the bit-plane coder, which generates more than two context-decision pairs per clock cycle. But due to the slow processing speed of the arithmetic coder, the overall encoding becomes slow. Hence, to overcome this bottleneck and speed up the process, a two-symbol architecture is proposed which not only doubles the throughput, but also can be operated at frequencies greater than 100 MHz. This architecture achieves a throughput of 210 Msymbols/sec and the critical path is at 9.457 ns.
Distributed arithmetic coding (DAC) is an effective implementation of Slepian–Wolf coding (SWC), especially for short data blocks. However, currently, most of the DAC research methods focus on the lossy compression o...
详细信息
Distributed arithmetic coding (DAC) is an effective implementation of Slepian–Wolf coding (SWC), especially for short data blocks. However, currently, most of the DAC research methods focus on the lossy compression on the condition that the prior knowledge is known in advance. In order to realize lossless and adaptive compression, in this paper, we propose lossless adaptivedistributed arithmetic coding (LADAC) using the method of EOF (end of the file) and the adaptive encoding. In our proposed LADAC, encoder and decoder can work simultaneously instead of alternatively, and encoder-driven method is also achieved without a feedback channel instead of decoder-driven. Experimental results show that LADAC performs the better compression performance and the lower complexity than conventional lossless distributed arithmetic coding (LDAC).
Reconfigurable embedded systems can take advantage of programmable devices, such as microprocessors and field-programmable gate arrays (FPGAs), to achieve high performance and flexibility. Support to flexibility often...
详细信息
Reconfigurable embedded systems can take advantage of programmable devices, such as microprocessors and field-programmable gate arrays (FPGAs), to achieve high performance and flexibility. Support to flexibility often comes at the expense of large amounts of nonvolatile memories. Unfortunately, nonvolatile memories, such as multilevel-cell (MLC) NAND flash, exhibit a high raw bit error rate that is mitigated by employing different techniques, including error correcting codes. Recent results show that low-density-parity-check (LDPC) codes are good candidates to improve the reliability of MLC NAND flash memories especially when page size increases. This letter proposes to use a joint source/ channel approach, based on a modified arithmetic code and LDPC codes, to achieve both data compression and improved system reliability. The proposed technique is then applied to the configuration data of FPGAs and experimental results show the superior performance of the proposed system with respect to state of the art. Indeed, the proposed system can achieve bit-error-rates as low as about for cell-to-cell coupling strength factors well higher than 1.0.
Embedded distributed systems are becoming increasingly complex and interconnected. Some of the challenges in building such systems are safety, i.e., the ability to operate correctly even in the face of arbitrary hardw...
详细信息
ISBN:
(纸本)9781479955848
Embedded distributed systems are becoming increasingly complex and interconnected. Some of the challenges in building such systems are safety, i.e., the ability to operate correctly even in the face of arbitrary hardware errors, and security, i.e., the ability to withstand hacker attacks. In this paper, an approach to improve both safety and security for embedded distributed systems with low performance overhead is proposed. Preliminary results indicate that applications hardened using the proposed technique have less than 2x performance overhead and fault coverage of 99.9% (assuming no control flow faults).
This paper presents a novel data hiding using Integer Wavelet Transform (IWT) through lifting scheme that aims to achieve high quality of stego image. This method transforms a spatial domain cover image into a frequen...
详细信息
ISBN:
(纸本)9781479938346
This paper presents a novel data hiding using Integer Wavelet Transform (IWT) through lifting scheme that aims to achieve high quality of stego image. This method transforms a spatial domain cover image into a frequency domain cover image. It hides the secret message into detail coefficients (CH, CV, CD) of IWT by construct a binary image in any of selected bit in CH, CV, CD separately and compresses that bit by using arithmetic coding. This method is very simple and effective.
Nowadays, most software and hardware applications are committed to reduce the footprint and resource usage of data. In this general context, lossless data compression is a beneficial technique that encodes information...
详细信息
ISBN:
(纸本)9781479928163
Nowadays, most software and hardware applications are committed to reduce the footprint and resource usage of data. In this general context, lossless data compression is a beneficial technique that encodes information using fewer (or at most equal number of) bits as compared to the original representation. A traditional compression flow consists of two phases: data decorrelation and entropy encoding. Data decorrelation, also called entropy reduction, aims at reducing the autocorrelation of the input data stream to be compressed in order to enhance the efficiency of entropy encoding. Entropy encoding reduces the size of the previously decorrelated data by using techniques such as Huffman coding, arithmetic coding, and others. When the data decorrelation is optimal, entropy encoding produces the strongest lossless compression possible. While efficient solutions for entropy encoding exist, data decorrelation is still a challenging problem limiting ultimate lossless compression opportunities. In this paper, we use logic synthesis to remove redundancy in binary data aiming to unlock the full potential of lossless compression. Embedded in a complete lossless compression flow, our logic synthesis based methodology is capable to identify the underlying function correlating a data set. Experimental results on data sets deriving from different causal processes show that the proposed approach achieves the highest compression ratio compared to state-of-art compression tools such as ZIP, bzip2 and 7zip.
In order to perform source coding (data compression), we treat messages emitted by independent and identically distributed sources as imprecise measurements (symbolic sequence) of a chaotic, ergodic, Lebesgue measure ...
详细信息
In order to perform source coding (data compression), we treat messages emitted by independent and identically distributed sources as imprecise measurements (symbolic sequence) of a chaotic, ergodic, Lebesgue measure preserving, non-linear dynamical system known as Generalized Luroth Series (GLS). GLS achieves Shannon's entropy bound and turns out to be a generalization of arithmetic coding, a popular source coding algorithm, used in international compression standards such as JPEG2000 and H.264. We further generalize GLS to piecewise non-linear maps (Skewed-nGLS). We motivate the use of Skewed-nGLS as a framework for joint source coding and encryption. (c) 2007 Elsevier B.V. All rights reserved.
In this paper, an improved soft in soft out (SISO) iterative decoding scheme for joint source-channel coding is presented. It is realized as the iterative soft decoding of arithmetic code based on sequential decoding ...
详细信息
ISBN:
(纸本)9781479934324
In this paper, an improved soft in soft out (SISO) iterative decoding scheme for joint source-channel coding is presented. It is realized as the iterative soft decoding of arithmetic code based on sequential decoding to successively prune the decoding tree. Making use of the forecasted forbidden symbols, an error-resistant arithmetic code with an improved a posteriori probability (APP) metric is adopted to further enhance the error correction performance. Simulation results have validated the superiority of our scheme in terms of packet error rate for the AWGN channel.
暂无评论