An Adaptive Joint Source/Channel Coding Using Error Correcting Arithmetic Codes

An approximately optimal adaptive arithmetic coding (AC) system using a forbidden symbol (FS) over noisy channels was proposed which allows one to jointly and adaptively design the source decoding and channel correcting in a single process, with superior performance compared with traditional separated techniques. The concept of adaptiveness is applied not only to the source model but also to the amount of coding redundancy. In addition, an improved branch metric computing algorithm and a faster sequential searching algorithm compared with the system proposed by Grangetto were proposed. The proposed system is tested in the case of image transmission over the AWGN channel, and compared with traditional separated system in terms of packet error rate and complexity. Both hard and soft decoding were taken into account.

Cryptanalysis of a chaos-based cryptosystem with an embedded adaptive arithmetic coder

In this paper, we analyse a new chaos-based cryptosystem with an embedded adaptive arithmetic coder, which was proposed by Li Heng-Jian and Zhang J S （Li H J and Zhang J S 2010 Chin. Phys. B 19 050508）. Although this new method has a better compression performance than its original version, it is found that there are some problems with its security and decryption processes. In this paper, it is shown how to obtain a great deal of plain text from the cipher text without prior knowledge of the secret key. After discussing the security and decryption problems of the Li Heng-Jian et al. algorithm, we propose an improved chaos-based cryptosystem with an embedded adaptive arithmetic coder that is more secure.

Reversible Natural Language Watermarking Using Synonym Substitution and Arithmetic Coding

For protecting the copyright of a text and recovering its original content harmlessly,this paper proposes a novel reversible natural language watermarking method that combines arithmetic coding and synonym substitution operations.By analyzing relative frequencies of synonymous words,synonyms employed for carrying payload are quantized into an unbalanced and redundant binary sequence.The quantized binary sequence is compressed by adaptive binary arithmetic coding losslessly to provide a spare for accommodating additional data.Then,the compressed data appended with the watermark are embedded into the cover text via synonym substitutions in an invertible manner.On the receiver side,the watermark and compressed data can be extracted by decoding the values of synonyms in the watermarked text,as a result of which the original context can be perfectly recovered by decompressing the extracted compressed data and substituting the replaced synonyms with their original synonyms.Experimental results demonstrate that the proposed method can extract the watermark successfully and achieve a lossless recovery of the original text.Additionally,it achieves a high embedding capacity.

A New Arithmetic Coding System Combining Source Channel Coding and MAP Decoding

A new arithmetic coding system combining source channel coding and maximum a posteriori decoding were proposed. It combines source coding and error correction tasks into one unified process by introducing an adaptive forbidden symbol. The proposed system achieves fixed length code words by adaptively adjusting the probability of the forbidden symbol and adding tail digits of variable length. The corresponding improved MAP decoding metric was derived. The proposed system can improve the performance. Simulations were performed on AWGN channels with various noise levels by using both hard and soft decision with BPSK modulation.The results show its performance is slightly better than that of our adaptive arithmetic error correcting coding system using a forbidden symbol.

Embedding adaptive arithmetic coder in chaos-based cryptography

In this study an adaptive arithmetic coder is embedded in the Baptista-type chaotic cryptosystem for implementing secure data compression. To build the multiple lookup tables of secure data compression, the phase space of chaos map with a uniform distribution in the search mode is divided non-uniformly according to the dynamic probability estimation of plaintext symbols. As a result, more probable symbols are selected according to the local statistical characters of plaintext and the required number of iterations is small since the more probable symbols have a higher chance to be visited by the chaotic search trajectory. By exploiting non-uniformity in the probabilities under which a number of iteration to be coded takes on its possible values, the compression capability is achieved by adaptive arithmetic code. Therefore, the system offers both compression and security. Compared with original arithmetic coding, simulation results on Calgary Corpus files show that the proposed scheme suffers from a reduction in compression performance less than 12% and is not susceptible to previously carried out attacks on arithmetic coding algorithms.

An efficient adaptive arithmetic coding image compression technology

This paper proposes an efficient lossless image compression scheme for still images based on an adaptive arithmetic coding compression algorithm. The algorithm increases the image coding compression rate and ensures the quality of the decoded image combined with the adaptive probability model and predictive coding. The use of adaptive models for each encoded image block dynamically estimates the probability of the relevant image block. The decoded image block can accurately recover the encoded image according to the code book information. We adopt an adaptive arithmetic coding algorithm for image compression that greatly improves the image compression rate. The results show that it is an effective compression technology.

An Improved Arithmetic Coding Algorithm

Arithmetic coding is the most powerful technique for statiscal lossless encoding that has attracted much attention in recent years. In this paper, we presents a new implementation of bit-level arithmetic coding by use of integer additions and shifts. The new algorithm has less computation complexity and is more flexible to use, and thus is very suitable for software and hardware design. We also discuss the application of the algorithm to the data encryption.

Joint Distortion Model for Progressive Image Transmission Using Error Correcting Arithmetic Codes

A novel joint source channel distortion model was proposed, which can essentially estimate the average distortion in progressive image transmission. To improve the precision of the model, the redundancy generated by a forbidden symbol in the arithmetic codes is used to distinguish the quantization distortion and the channel distortion, all the coefficients from the first error one to the end of the sequence are set to be a value within the variance range of the coefficients instead of zero, then the error propagation coming from the entropy coding can be essentially estimated, which is disregarded in the most conventional joint source channel coding (JSCC) systems. The precision of the model in terms of average peak-signal-to-noise has been improved about 0.5 dB compared to classical works. An efficient unequal error protection system based on the model is developed, and can be used in the wireless communication systems.

A New Method Which Combines Arithmetic Coding with RLE for Lossless Image Compression

This paper presents a new method of lossless image compression. An image is characterized by homogeneous parts. The bit planes, which are of high weight are characterized by sequences of 0 and 1 are successive encoded with RLE, whereas the other bit planes are encoded by the arithmetic coding (AC) (static or adaptive model). By combining an AC (adaptive or static) with the RLE, a high degree of adaptation and compression efficiency is achieved. The proposed method is compared to both static and adaptive model. Experimental results, based on a set of 12 gray-level images, demonstrate that the proposed scheme gives mean compression ratio that are higher those compared to the conventional arithmetic encoders.

Medical Image Compression Using Wrapping Based Fast Discrete Curvelet Transform and Arithmetic Coding

Due to the development of CT (Computed Tomography), MRI (Magnetic Resonance Imaging), PET (Positron Emission Tomography), EBCT (Electron Beam Computed Tomography), SMRI (Stereotactic Magnetic Resonance Imaging), etc. has enhanced the distinguishing rate and scanning rate of the imaging equipments. The diagnosis and the process of getting useful information from the image are got by processing the medical images using the wavelet technique. Wavelet transform has increased the compression rate. Increasing the compression performance by minimizing the amount of image data in the medical images is a critical task. Crucial medical information like diagnosing diseases and their treatments is obtained by modern radiology techniques. Medical Imaging (MI) process is used to acquire that information. For lossy and lossless image compression, several techniques were developed. Image edges have limitations in capturing them if we make use of the extension of 1-D wavelet transform. This is because wavelet transform cannot effectively transform straight line discontinuities, as well geographic lines in natural images cannot be reconstructed in a proper manner if 1-D transform is used. Differently oriented image textures are coded well using Curvelet Transform. The Curvelet Transform is suitable for compressing medical images, which has more curvy portions. This paper describes a method for compression of various medical images using Fast Discrete Curvelet Transform based on wrapping technique. After transformation, the coefficients are quantized using vector quantization and coded using arithmetic encoding technique. The proposed method is tested on various medical images and the result demonstrates significant improvement in performance parameters like Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR).

High throughput VLSI architecture for H.264/AVC context-based adaptive binary arithmetic coding(CABAC) decoding

Context-based adaptive binary arithmetic coding(CABAC) is the major entropy-coding algorithm employed in H.264/AVC.In this paper,we present a new VLSI architecture design for an H.264/AVC CABAC decoder,which optimizes both decode decision and decode bypass engines for high throughput,and improves context model allocation for efficient external memory access.Based on the fact that the most possible symbol(MPS) branch is much simpler than the least possible symbol(LPS) branch,a newly organized decode decision engine consisting of two serially concatenated MPS branches and one LPS branch is proposed to achieve better parallelism at lower timing path cost.A look-ahead context index(ctxIdx) calculation mechanism is designed to provide the context model for the second MPS branch.A head-zero detector is proposed to improve the performance of the decode bypass engine according to UEGk encoding features.In addition,to lower the frequency of memory access,we reorganize the context models in external memory and use three circular buffers to cache the context models,neighboring information,and bit stream,respectively.A pre-fetching mechanism with a prediction scheme is adopted to load the corresponding content to a circular buffer to hide external memory latency.Experimental results show that our design can operate at 250 MHz with a 20.71k gate count in SMIC18 silicon technology,and that it achieves an average data decoding rate of 1.5 bins/cycle.

基于Q-Coder算术编码器的IP核设计

设计了一种实现算术编码的集成电路IP核,可用于下一代静止图像压缩标准JPEG2000编码系统中。采取易于硬件实现的二进制算术编码算法,分析了该IP核的各个模块和时序,在ModelSim软件中进行了功能仿真,在QuartusⅡ软件中完成了综合以及布局布线,并在自行设计的一块FPGA的PCI开发板上进行了验证和性能分析。实验结果表明,对相同的图像进行编码,该IP核的处理时间仅为软件处理时间的41%。该文的研究对于JPEG2000在实际中的应用有着重要的意义。

Context-Based 2D-VLC Entropy Coder in AVS Video Coding Standard

In this paper, a Context-based 2D Variable Length Coding （C2DVLC） method for coding the transformed residuals in AVS video coding standard is presented. One feature in C2DVLC is the usage of multiple 2D-VLC tables and another feature is the usage of simple Exponential-Golomb codes. C2DVLC employs context-based adaptive multiple table coding to exploit the statistical correlation between DCT coefficients of one block for higher coding efficiency. ExpGolomb codes are applied to code the pairs of the run-length of zero coefficients and the nonzero coefficient for lower storage requirement. C2DVLC is a low complexity coder in terms of both computational time and memory requirement. The experimental results show that C2DVLC can gain 0.34dB in average for the tested videos when compared with the traditional 2D-VLC coding method like that used in MPEG-2. And compared with CAVLC in H.264/AVC, C2DVLC shows similar coding efficiency.

An adaptive pipelining scheme for H.264/AVC CABAC decoder

An adaptive pipelining scheme for H.264/AVC context-based adaptive binary arithmetic coding(CABAC) decoder for high definition(HD) applications is proposed to solve data hazard problems coming from the data dependencies in CABAC decoding process.An efficiency model of CABAC decoding pipeline is derived according to the analysis of a common pipeline.Based on that,several adaptive strategies are provided.The pipelining scheme with these strategies can be adaptive to different types of syntax elements(SEs) and the pipeline will not stall during decoding process when these strategies are adopted.In addition,the decoder proposed can fully support H.264/AVC high4:2:2 profile and the experimental results show that the efficiency of decoder is much higher than other architectures with one engine.Taking both performance and cost into consideration,our design makes a good tradeoff compared with other work and it is sufficient for HD real-time decoding.

Joint Source-Channel Decoding Scheme for Image Transmission over Wireless Channel

We improve the iterative decoding algorithm by utilizing the “leaked” residual redundancy at the output of the source encoder without changing the encoder structure for the noisy channel. The experimental results show that using the residual redundancy of the compressed source in channel decoding is an effective method to improve the error correction performance.

Compression of ECG signal using video codec technology-like scheme

In this paper, we present a method using video codec technology to compress ECG signals. This method exploits both intra-beat and inter-beat correlations of the ECG signals to achieve high compression ratios (CR) and a low percent root mean square difference (PRD). Since ECG signals have both intra-beat and inter-beat redundancies like video signals, which have both intra-frame and inter-frame correlation, video codec technology can be used for ECG compression. In order to do this, some pre-process will be needed. The ECG signals should firstly be segmented and normalized to a sequence of beat cycles with the same length, and then these beat cycles can be treated as picture frames and compressed with video codec technology. We have used records from MIT-BIH arrhythmia database to evaluate our algorithm. Results show that, besides compression efficiently, this algorithm has the advantages of resolution adjustable, random access and flexibility for irregular period and QRS false detection.

Quantitative Comparative Study of the Performance of Lossless Compression Methods Based on a Text Data Model

Data compression plays a key role in optimizing the use of memory storage space and also reducing latency in data transmission. In this paper, we are interested in lossless compression techniques because their performance is exploited with lossy compression techniques for images and videos generally using a mixed approach. To achieve our intended objective, which is to study the performance of lossless compression methods, we first carried out a literature review, a summary of which enabled us to select the most relevant, namely the following: arithmetic coding, LZW, Tunstall’s algorithm, RLE, BWT, Huffman coding and Shannon-Fano. Secondly, we designed a purposive text dataset with a repeating pattern in order to test the behavior and effectiveness of the selected compression techniques. Thirdly, we designed the compression algorithms and developed the programs (scripts) in Matlab in order to test their performance. Finally, following the tests conducted on relevant data that we constructed according to a deliberate model, the results show that these methods presented in order of performance are very satisfactory:- LZW- Arithmetic coding- Tunstall algorithm- BWT + RLELikewise, it appears that on the one hand, the performance of certain techniques relative to others is strongly linked to the sequencing and/or recurrence of symbols that make up the message, and on the other hand, to the cumulative time of encoding and decoding.

算术编码与数据加密

提出了一种利用算术编码实现数据加密的算法。除了能加密数据外，还能对文本数据达到一倍左右的压缩效果，因此特别适宜于机密数据的传输和保存。更进一步，通过预加密一个随机数的办法，消除了已知明文导出密钥的可能性。

基于矩阵编码的遗传算法研究

分析遗传算法求解矩阵函数的局限性,提出一种基于矩阵编码的遗传算法。定义该算法的选择算子、交叉算子、变异算子,编写各算子的Matlab函数,通过仿真求解二矩阵变量函数。实例证明,该算法能确保矩阵染色体的结构完整性,提高遗传算法的速度和寻优精度。