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Using packet classification algorithms in network equipment increases packet processing speed in Internet of Things (IoT). In the hardware implementation of these algorithms, ternary content-addressable memories (TCAMs) are often preferred to other implementations. As a common approach, TCAMs are used for the parallel search to match packet header information with the rules of the classifier. In two-stage architectures of hardware-based packet classifiers, first the decision tree is created, and then the rules are distributed among its leaves. In the second step, depending on the corresponding leaves, the second part of the rules, which includes the range of source and destination ports is stored in different blocks of TCAM. Due to inappropriate storage of port range fields, the existing architectures face the problem of wasting memory and growing power consumption. This paper proposes an efficient algorithm to encode the port range. This algorithm consists of three general steps including layering, bit allocation, and encoding. A greedy algorithm in the first step places the ranges with higher weights in higher layers. Next, an auction-based algorithm allocates several bits to each layer depending on the number of the ranges in that layer. Finally, in each layer, depending on the weight order of the ranges, the bits are given values for the intended range. The evaluation results show that unlike previous methods of storing range fields, the proposed method not only increases the speed of the classification but also uses the capacity of TCAM in the second stage more efficiently.

This paper studies the problem of video coding based on compressive sensing (CS) framework. We propose a new approach based on global sensing model applied to a residual frame computed as a difference between an input frame and its downsampled and upsampled version. The downsampled version of the frame is compressed via JPEG baseline making the codec compatible with JPEG. The sensed measurements are quantized, subsampled using a predefined look-up table, and encoded via context-adaptive binary range coder. The resulting bit stream is divided into network abstract layer packets which are embedded into an application part of the JPEG header. Experimental results show that the proposed approach outperforms existing algorithms based on CS in rate-distortion performance and generates a bit stream, which is significantly more robust to packet losses compared to conventional codecs.

The increase in coding efficiency and complexity of high efficiency video coding (HEVC) over H.264 is due to, among other factors, the time needed to find the optimal partition structure among the more flexible encoding modes for the coding units (CUs) and prediction units (PUs). Although many classification-based algorithms have been proposed to expedite the partition decision, the features that can be acquired from current HEVC encoding order are not sufficient to minimize the loss in coding efficiency. In this letter, we proposed an early-split (ES) order for HEVC CU-level encoding, where the encoder checks the split mode before the nonsquare PU partition modes and utilizes the encoding output of the subCUs to expedite subsequent encoding. Experiments show that the proposed algorithm can save 48% of encoding time on average with only about 0.8% loss in coding performance.

The speed requirement for the routing table lookup and the packet classification is rapidly increasing due to the increase in the number of packets needed to be processed per second. The hardware-based packet classification relies on ternary content addressable memory (TCAM) to meet this speed requirement. However, TCAM consumes huge power and also supports only for longest prefix match and exact match, where the classification rule also has a range match (RM) field. Hence, it is mandatory to encode the RM into prefix match to accommodate the rule in TCAM. In the worst case, one rule is encoded into (2 W -2) 2 rules (where W is a number of bits to represent range). This work proposes a novel RM architecture, and a detailed analysis about the range field on the standard dataset and the real-life classifier rules are presented. In the literature, the existing RM architecture is used to avoid the range to prefix conversion, but due to the serial operation, it lacks in performance. For constant time lookup, TCAM is the best option, but it does not support RM. The proposed architecture takes one clock cycle for RM and does not require any encoding/ conversion. Hence, there will be a single entry for every rule. It is observed that just 4% of the two-dimensional range rules are present in this dataset, and it will increase the rule set size by 4 times in the best case and nearly 30 times in the worst case. The proposed RM circuit is operated in parallel with TCAM without compromising the speed, and this circuit saves huge power around 70% and area around 61%, where the range to prefix conversion/encoding is completely avoided. The proposed architecture is well suited for current IPv4- and IPv6-based networks, as well as in software-defined networks in the near future.

TCAMs play a central role in the forwarding plane of physical SDN switches. Despite their capability for line-speed queries, they are not well suited for representing rules with range fields. In this poster, we report our latest work on TCAM range encodings, which is not only better in terms of encoding performance, but also easier to understand and more practical to implement. The preliminary evaluation shows that, our proposed encoding scheme has comparable encoding efficiency to the optimal encoding scheme, while achieving an order-of-magnitude improvement on encoding performance over the optimal encoding scheme on average.

Efficient representations of multi-field packet classifiers with fields represented by ranges is a core mechanism to express services on data plane. To implement classifiers in ternary addressable memory (TCAM), each range should be encoded into multiple ternary bit strings whose number is at most linear to the width (in bits) of a represented field independently from range encoding method. In this paper we introduce a notion of a subrange allowing to represent a field range on any chosen subset of bit indices that significantly improve efficiency of classifier representations. Our analytic results are confirmed with a comprehensive evaluation study showing applicability of our approach to implement desired levels of expressiveness and scalability in packet classifiers. TRUE pub

The enormous data inflow during three-dimensional 3D pavement surface data collection requires an efficient compression system for 3D data. However, with respect to the phase of lossless encoding, the commonly used Huffman Coding is inefficient in terms of speed and memory usage for encoding 3D pavement surfaces. The Fast Prefix Coding Algorithm FPCA is proposed in the article as an effective substitute of Huffman Coding at the stage of lossless encoding. It is demonstrated in the article that the FPCA is much faster and more memory efficient than Huffman Coding, while outperforming Shannon-Fano Coding in terms of both redundancy and time efficiency. The FPCA-based coding approach is a modification of the baseline JPEG algorithm to support 3D pavement data whose dynamic range is more than 12 bits. The presented modifications include algorithms for Quantization, Run-Length Encoding and Entropy Coding without limiting data depth in terms of dynamic range. Compared with the baseline JPEG approach, the proposed coding system is able to restrict the data loss more successfully and can achieve a significantly higher level of time efficiency and compression ratio over than 30:1 for most of the evaluated 3D images. With parallel computing techniques, encoding full-lane width pavement in 3D and at 1 mm resolution with an up-to-date desktop computer can be conducted at 150 MPH or even higher speed.

We present a specialized compressor designed for efficient data storage of FASTQ files produced by high-throughput DNA sequencers. Since the method has been optimized for compression quality, it is especially suitable for long-term storage and for genome research centers processing huge amount of data (counted in petabytes). The proposed compressor uses high-order statistical models for range encoding, similar to Markov models, but the whole input is considered in building a symbol context. Compression of DNA reads is performed according to LZ-style with the use of the 5–7th order model, while nucleotides’ scores are encoded with the 3rd order model.

In recent years, along with advances in processor technology, large-scale numerical simulation dealing with huge floating point data is an active research field. In the case of supercomputing using many processors via the network, the bottleneck is communication speed rather than the calculation one. Thus reduction of the communication cost is strongly required in the supercomputing and its straightforward technique is data compression. Most conventional floating-point data compression schemes use predictive coding based on an extrapolative prediction [1–2]. In general, it is known that interpolative prediction has higher accuracy than extrapolative one. This paper proposes a floating point data compression using combined predictor with interpolation and extrapolation, namely Inter-Extrapolative predictor. Proposed predictor is defined one-dimensional but it can be is easily expanded into 2-dimensional by cascading in the horizontal-vertical direction. Note that the interpolation violates causality because it uses future data. In order to avoid this problem, transmission order of data is arranged; first, odd index sequence; second, remaining even index one. The first even index sequence is extrapolated, then the odd index sequence. Similar to Lindstrom's method [1], each prediction residual is encoded by the range coder with '0' run-length of bitwise. Thus proposed method can gain the higher compression rate than [1] under the same number of arithmetic operation.

This paper compares the coding efficiency between the range coder in the Opus coder and the Huffman coder used in the MP-3 (MPEG-I Layer 3) and MPEG-2 AAC. The results show that the range coder has efficiency advantage of about 9 % at a rate of 128 kbps. The simulation, in a sense, indicates that transcoding from the Opus format to MP-3 or AAC format will lead to quality degradation.

In this paper, we propose a new hardware-efficient adaptive binary range coder (ABRC) and its very-large-scale integration (VLSI) architecture. To achieve this, we follow an approach that allows to reduce the bit capacity of the multiplication needed in the interval division part and shows how to avoid the need to use a loop in the renormalization part of ABRC. The probability estimation in the proposed ABRC is based on a lookup table free virtual sliding window. To obtain a higher compression performance, we propose a new adaptive window size selection algorithm. In comparison with an ABRC with a single window, the proposed system provides a faster probability adaptation at the initial encoding/decoding stage, and more accurate probability estimation for very low entropy binary sources. We show that the VLSI architecture of the proposed ABRC attains a throughput of 105.92 MSymbols/s on the FPGA platform, and consumes 18.15 mW for the dynamic part power. In comparison with the state-of-the-art MQ-coder (used in JPEG2000 standard) and the M-coder (used in H.264/Advanced Video Coding and H.265/High Efficiency Video Coding standards), the proposed ABRC architecture provides comparable throughput, reduced memory, and power consumption. Experimental results obtained for a wavelet video codec with JPEG2000-like bit-plane entropy coder show that the proposed ABRC allows to reduce the bit rate by 0.8%–8% in comparison with the MQ-coder and from 1.0%–24.2% in comparison with the M-coder.

Given the unprecedented size of three-dimensional ultraspectral sounder data with high spectral resolution, lossless compression is preferable to avoid substantial degradation of the geophysical retrieval. A lossless compression method for ultraspectral sounder data is therefore developed. A quantized-principal-component-analysis-based scheme is presented by combining 3D prediction, positive mapping, and histogram packing using binary indexing vectors positive packing followed by a range coder. In order to achieve the optimal trade-off between residual errors and side information, an algorithm is proposed to determine adaptively the number of selected PCs and quantization parameters. Numerical experiments show that the proposed method outperforms the state-of-the-art methods i.e. linear prediction with constant coefficients LP-CC and linear prediction with optimal granule ordering LP-OGO by 1.77% in terms of compression ratio.

Ethio-Telecom, the sole telecom service provider in Ethiopia, is expanding its mobile network infrastructures to reach its target of over 100 million subscribers in the next few years. As a result of this expansion and with the introduction of new services a considerable amount of electronic information (eg. E-mail, Short Message Service (SMS), etc.) written in local languages is expected to be generated. Amharic, as the official language in Ethiopia spoken by over thirty million primary and secondary speakers, will contribute to most of the generated traffic. Based on the current practice Universal Transformation Format (UTF-8) encoding scheme is used to represent each Amharic symbol with 16 bits. This is costly for individual users and also advertisers that send bulk SMSs. As an alternative the Latin alphabet, which uses 8 or 7 bits/symbol, is used to facilitate the information exchange in a cost effective manner. Even though using foreign alphabets to write Amharic texts is cost effective it creates confusion and discomfort due to unavailability of standard translations and low English literacy level in the population respectively. For efficient representation of written Amharic language, this work investigates the entropy of the language and proposes entropy-based source encoding techniques. The entropy estimation is performed with Shannon's N-gram conditional and Block estimation mechanisms while Huffman and Arithmetic encoding are used for the encoding. The results show that for N=1 the entropy is 7.981 bits/symbol whereas the entropy reduces to 1.074 bits/symbol for N=15. Moreover, the Arithmetic encoding provides compression up to 72.45%. To the best of our knowledge, this is the first work to compute entropy of the language.

J-bit encoding merupakan algoritme kompresi data lossless yang memanipulasi setiap bit data dalam file untuk meminimalkan ukuran dengan cara membagi data menjadi dua keluaran, kemudian dikombinasikan kembali menjadi satu keluaran. Makalah ini mengusulkan modifikasi algoritme J-bit encoding dengan cara mengeliminasi simbol nol dan satu dari keluaran pertama, sehingga keluaran pertama akan berisi data asli selain nol dan satu (dalam ukuran byte) dan keluaran kedua akan berisi nilai dua bit yang menjelaskan posisi byte nol, byte satu, dan byte selain nol dan satu. Perbandingan unjuk kerja kedua algoritme ini dilakukan dengan menggunakan empat skema kombinasi algoritmem yaitu (i) transformasi Burrows-Wheeler, Move to Front, J-bit encoding, dan pengkodean aritmatika, (ii) transformasi Burrows-Wheeler, Move to Front, algoritme hasil modifikasi, dan pengkodean aritmatika, (iii) transformasi Burrows-Wheeler, Move One From Front, J-bit encoding, dan pengkodean aritmatika, (iv) transformasi Burrows-Wheeler, Move One From Front, algoritme hasil modifikasi, dan pengkodean aritmatika. Dengan menggunakan data set Calgary Corpus dan Canterbury Corpus, hasil pengujian menunjukkan bahwa rata-rata rasio kompresi terbaik diperoleh dengan menggunakan skema kedua. Sedangkan dengan menggunakan empat file gambar, hasil pengujian menunjukkan bahwa rata-rata rasio kompresi terbaik diperoleh dengan menggunakan skema keempat.

We propose a novel efficient multialphabet multiplication-free adaptive arithmetic coder. First, we generalize probability estimation via virtual sliding window for the multialphabet case and show that it does not require multiplications and provides a tradeoff between the probability adaptation speed and the precision of the probability estimation. Second, we show how the generalized virtual sliding window can be used to eliminate multiplications and divisions. Finally, we demonstrate that the proposed arithmetic coder provides better compression performance than existing implementations based on state-of-the-art multiplication-free binary arithmetic coders.

This paper reports the effect of compression by applying delta encoding and Huffman coding schemes together on speech signals of American-English and Hindi from International Phonetic Alphabet database. First of all, these speech signals have been delta encoded and then compressed by Huffman coding. By doing so, it has been observed here that the Huffman coding gives high compression ratio for this delta encoded speech signals as compared to the compression on the input speech signals only incorporating Huffman coding.

The performance of optimal prefix-free encoding for memoryless sources with a large alphabet size is studied. It is shown that the redundancy of the Huffman code for almost all sources with a large alphabet size n is very close to that of the average distribution of the monotone sources with n symbols. This value lies between 0.02873 and 0.02877 bit for sufficiently large n.

Arithmetic coding is the data compression techniques, which encodes the data by generating the code string that represents a functional value between 0 and 1. In this paper, we propose a modified-Adaptive Binary-RC (Range Coder) or M-ABRC. Our algorithm minimizes the multiplication bit capacity through introducing the VLSI architecture, proposed algorithm uses the LUP (Look UP Table)-VSW (Virtual Sliding Window) for the probability estimation. In order to achieve the higher compression rate, our method M-ABRC has been implemented, this in terms provides the better adoption probability in encoding phase and also gives the absolute estimation of low-EBS(entropy binary sources). Moreover In order to evaluate the algorithm we have compared with the several existing technique, comparison takes place based on the two parameter i.e. device utilization and the power dissipation (static and dynamic).

The performance of LT code is highly related to the code length. A decoder is more likely to deplete degree-1 encoding symbols and terminate during early stage when the code length is short. In this work, we modify the robust Soliton distribution (RSD) and increase the degree-1 proportion. More degree-1 encoding symbols can be generated to relieve early decoding termination. The proportion of low degrees, except for degree-1, is also reduced. Therefore, receivers collect less encoding symbols carrying redundant information. In addition, Non-Repetitive (NR) encoding scheme is proposed to avoid producing repeated degree-1 encoding symbols. To improve video transmission quality, previous studies redesign LT codes to provide Unequal Error Protection (UEP) for different Scalable Video Coding (SVC) layers. Unlike those studies to modify the code structure, we integrate multiple NR encoders to achieve UEP ability. Experimental results show that our UEP scheme outperforms previous studies in terms of the PSNR.

This paper is dedicated to fast video coding based on three-dimensional discrete wavelet transform. First, we propose a novel low-complexity bit-plane entropy coding of wavelet subbands based on Levenstein zero-run coder for low entropy contexts and adaptive binary range coder for other contexts. Second, we propose a rate-distortion efficient criterion for skipping 2-D wavelet transforms and entropy encoding based on parent-child subband tree. Finally, we propose one pass rate control which uses virtual buffer concept for adaptive Lagrange multiplier selection. Simulations results show that the proposed video codec has a much lower computational complexity (from 2 to 6 times) for the same quality level compared to the H.264/AVC standard in the low complexity mode.

Source encoding algorithm has been widely concerned by the researchers, which aims to improve communication efficiency. In this paper, we put forward a new source encoding algorithm to decrease the redundancies between compiled code words in Shannon encoding. With the proposed algorithm, a code word is selected by considering whether the code is the prefix code of the other code words or not. The performance of the new encoding algorithm is investigated for both equal probability of source symbols and non-equal probability condition. Numerical results show that the proposed encoding algorithm can improve the encoding efficiency obviously, especially for the case of great probability differences between source symbols.

In this paper, we compare Huffman coding and Arithmetic coding with ASCII coding to observe their contribution to steganography. This way is to compress the embedded information first for enhance the payload of a cover image. There are many wonderful coding methods to compress this kind of string, such as Huffman coding and Arithmetic coding. The experiment results indicate that Huffman coding and Arithmetic coding as an important preprocess step of steganography can effectively reduce the amount of secret information with efficient encoding performance. And a theory opinion related to payload calculation is proposed which can be used to describe the relationship among the three parameters of steganography: imperceptibility, information embedding efficiency and payload.

In this paper, a new binary arithmetic coding strategy with adaptive-weight context classification is introduced to solve the context dilution and context quantization problems for bitplane coding. In our method, the weight, obtained using a regressive-prediction algorithm, represents the degree of importance of the current coefficient/block in the wavelet transform domain. Regarding the weights as contexts, the coder reduces the context number by classifying the weights using the Lloyd-Max algorithm, such that high-order is approximated as low-order context arithmetic coding. The experimental results show that our method effectively improves the arithmetic coding performance and outperforms the compression performances of SPECK, SPIHT and JPEG2000.

An efficient ternary content addressable memory (TCAM) encoding scheme using a binary reflected Gray code (BRGC) and the concept of elementary intervals is presented for efficiently storing arbitrary ranges in TCAM. The proposed layered BRGC range encoding scheme (L-BRGC) groups ranges into BRGC range sets in which each range can be encoded into a single ternary vector. The results of experiments performed on real-life and synthesized rule tables show that L-BRGC consumes less TCAM than all the existing range encoding schemes for all rule tables, except that the direct conversion scheme (EIGC) using elementary intervals and BRGC codes performs best for a small real-life ACL rule table.

Distributed arithmetic coding (DAC) is similar to syndrome coding, in the sense that message sequences sharing the same interval can be considered a coset of the space of the source sequences, and the codeword is the index of the coset. In this paper, the minimum Hamming distance of cosets is studied and it is proved that such a distance is as small as one. By only allowing the sequences with a large Hamming distance to overlap in the same interval, an improved DAC scheme is proposed. Simulation results show that, for equiprobable memoryless sources, this approach outperforms DAC in terms of decoding error rate at the same coding cost. In addition, at small sequence length, the decoding error rate of the proposed scheme is lower than that of distributed source coding based on low-density parity-check codes for highly correlated sources.

A progressive 3D triangular mesh compression algorithm built on the MOG-based Bayesian entropy coding and the gradual prediction scheme is proposed in this work. For connectivity coding, we employ MOG models to estimate the posterior probabilities of topology symbols given vertex geometries. Then, we encode the topology symbols using an arithmetic coder with different contexts, which depend on the posterior probabilities. For geometry coding, we propose the gradual prediction labeling and the dual-ring prediction to divide vertices into groups and predict later groups more efficiently using the information in already encoded groups. Simulation results demonstrate that the proposed algorithm provides significantly better performance than the conventional wavemesh coder, with the average bit rate reduction of about 16.9 %.

Arithmetic coding is a lossless compression algorithm with variable-length source coding. It is more flexible and efficient than the well-known Huffman coding. In this paper we present a non-adaptive FPGA implementation of a multi-alphabet arithmetic coding with separated statistical model of the data source. The alphabet of the data source is a 256-symbol ASCII character set and does not include the special end-of-file symbol. No context switching is used in the proposed design which gives maximal throughput without pipelining. We have synthesized the design for Xilinx FPGA devices and used their built-in hardware resources. K e y w o r d s: arithmetic coding, compression algorithm

We have proposed the concept of simple and fast binary arithmetic coder, STT-coder, in which arithmetic operation can be executed by referring a state transition table just like the case of Huffman coding. In our previous study, STT-coder with 3-bit interval register was designed and evaluated. Its coding efficiency was found to be satisfactory despite of the introduction of the simplification of the process. Then we have been trying to extend the register to 6-bit length in order to improve the performance of higher MPS probability sources. In this paper, we introduce a coding parameter of STT-coder, which is called as offset, and optimize the value to maximize the average coding efficiency. We further tried to enlarge the expected interval size after the renormalization to improve the coding efficiency of higher MPS probability sources by sacrificing the accuracy of interval division ratio in low MPS probability sources, which turned out to improve the coding efficiency in total. 

A coding method for information storage of DNA Sequences is described. The coding method developed in this paper is compared to a method which improved upon the Huffman coding technique to store DNA in various forms. The method used in this research, entails the utilization of specific mapping of base pairs to a numerical assignment through bit manipulation that enables efficient coding of characters, allowing eight alleles per integer. Therefore, the research presented below decreases the required storage space by one fourth. The methodology of this paper uses a lookup table based library with efficient and reliable information retrieval and assembly with uniquely designed mapping is described. The coding method is illustrated by translating existing DNA sequences into a numerical representation of the same sequence. The method discussed below, provides advantages over traditional Huffman coding by providing a simple, unambiguous method that lends itself to saving space when storing DNA.

Huffman coding [11] is a most popular technique for generating prefix-free codes [7, 10]. It is an efficient algorithm in the field of source coding. It produces the lowest possible number of code symbols of a single source symbol [1]. Huffman coding is a most widely used lossless compression technique [2]. However, there are some limitations that arise in Huffman coding [20, 21]. This method produces a code of few bits for a symbol having high probability of occurrence and large number of bits for a symbol having low probability of occurrence [3]. Instead of this, in Double Huffman Coding when the code word of the symbol has been generated it will be compressed on binary basis. Through this technique a better result be achieved. In this paper we discussed the technique of Huffman Coding and Double Huffman coding and compare their performance analysis.

In modern networks, faults in the network state, e.g., loops, black holes and access control violations that make services unavailable, arise commonly. To ensure networks' correctness, existing approaches verify network-wide invariants. However, these approaches cannot detect or prevent faults immediately as they arise, so that network performance is not affected. In this paper, we present a data plane verification algorithm with low latency. To provide live protection, our algorithm detects faulty rules issued by SDN applications, and prevent them from reaching the data plane of network. According to experiments which use real world network rule sets, our algorithm efficiently processes rule update and verification.

Arithmetic coding is a method for data compression. It produces a theoretically optimal compression under much weaker assumptions than Huffman and Shannon-Fano, and can compress within one bit of the limit imposed by Shannon's Noiseless Coding Theorem. Earlier presentations provided little in the way of proof of why the various steps in the encoding process were correct, particularly when it came to the specification of precisely what problem the implementation solved, and the details of why the inverse operation of decoding was correct. Our aim in these lectures is to provide a formal derivation of basic algorithms for coding and decoding. Our development makes heavy use of the algebraic laws of folds and unfolds. One novel result concerns a new pattern of computation, which we call streaming, whereby elements of an output list are produced as soon as they are determined (and which has nothing to do with lazy evaluation).

We present range encoding with no expansion (RENE)— a novel encoding scheme for short ranges on Ternary content addressable memory (TCAM), which, unlike previous solutions, does not impose row expansion , and uses bits proportionally to the maximal range length. We provide theoretical analysis to show that our encoding is the closest to the lower bound of number of bits used. In addition, we show several applications of our technique in the field of packet classification, and also, how the same technique could be used to efficiently solve other hard problems, such as the nearest-neighbor search problem and its variants. We show that using TCAM, one could solve such problems in much higher rates than previously suggested solutions, and outperform known lower bounds in traditional memory models. We show by experiments that the translation process of RENE on switch hardware induces only a negligible 2.5% latency overhead. Our nearest neighbor implementation on a TCAM device provides search rates that are up to four orders of magnitude higher than previous best prior-art solutions.

In order to familiarize and comprehend the learning of Huffman algorithm in a simpler way, we have implemented Huffman algorithm in Julia language which can be visualized in an interactive platform called IJulia supported by Jupyter and it creates a learning platform that increases the understanding of the coding technique for students. It involves a set of commands that are understandable and familiar to the user. Data compression is an essential requirement for lossless data transmission. Message bits are encoded in the transmitter end so as to avoid errors and redundancy during the transmission of information. Huffman code is a prefix type code which compresses the message bits by knowing the frequency of occurrence of each character or probability of each symbol. A Huffman/binary tree is formed based on the occurrence of the symbols and symbols are then encoded. Huffman algorithm implements bottom-up approach. Each symbol encoded with the Huffman tree logic is decodable. Each encoded information can be decoded by tracing from the top of the tree to the required symbol. This paper introduces the implementation of this algorithm using Julia programming language supported by Jupyter platform. This encoding algorithm is the most efficient way of compressing data.

Encoding a sequence of independent symbols over a large alphabet size is a challenging problem with applications in many fields. The most widely used adaptive entropy coding techniques (namely, arithmetic and Huffman coding) are known to achieve an average codeword length which may be significantly greater than the empirical entropy of the sequence, as the alphabet size increases. In this work we introduce an efficient and easy-to-implement method for large alphabet adaptive encoding. We propose a conceptual framework in which a sequence of symbols, over a large alphabet size, is decomposed into multiple "almost independent" sequences over a smaller alphabet. Then each of these sequences is encoded separately. This way, we allow encoding of small alphabet sequences, at the cost of the "remaining dependence" among the sequences. We demonstrate the advantages of our suggested scheme through a series of theorems and experiments, showing it reduces both the average codeword length and the compression runtime in many large alphabet setups.

In this paper a lossless compression method for hyperspectral image is given. RKLT-based scheme was first presented by combining with 3D prediction, principal component selection, positive mapping followed by a range coder. The proposed method avoids the float number of coefficient which can make it much more easier to be processed on hardware. Numerical experiments show that the proposed method outperforms the state-of-the-art methods (i.e., LUT-NN, LAIS-LUT, and AI) by 13% in terms of compression ratio.

In high efficiency video coding (HEVC), coding tree contributes to excellent compression performance. However, coding tree brings extremely high computational complexity. Innovative works for improving coding tree to further reduce encoding time are stated in this paper. A novel low complexity coding tree mechanism is proposed for HEVC fast coding unit (CU) encoding. Firstly, this paper makes an in-depth study of the relationship among CU distribution, quantization parameter (QP) and content change (CC). Secondly, a CU coding tree probability model is proposed for modeling and predicting CU distribution. Eventually, a CU coding tree probability update is proposed, aiming to address probabilistic model distortion problems caused by CC. Experimental results show that the proposed low complexity CU coding tree mechanism significantly reduces encoding time by 27% for lossy coding and 42% for visually lossless coding and lossless coding. The proposed low complexity CU coding tree mechanism devotes to improving coding performance under various application conditions.

This letter explores the use of adaptive prediction length in clustered differential pulse code modulation (C-DPCM) lossless compression method for hyperspectral images. In the C-DPCM method, linear prediction is performed using coefficients optimized for each spectral cluster separately. The difference between the predicted and original values is entropy coded using an adaptive range coder for each cluster. The results show that the C-DPCM-with-adaptive-prediction-length method has lower bit-per-pixel value than the original C-DPCM method for Consultative Committee for Space Data Systems 2006 AVIRIS test images. Both calibrated and uncalibrated image compression results are improved by adaptive prediction length.

When arithmetic components are parallelized, fault-prone interconnections can tamper results significantly. Constantly progressing technology scaling leads to a steady increase of errors caused by faulty transmission. Resilient data encoding schemes can be used to offset these negative effects. Focusing on parallel signed-digit based arithmetic frequently used in highspeed systems, we propose suitable data encodings that reduce error rates by 25%. Data encoding should be driven by the occurrence probabilities of digits. We develop a methodology to obtain these probabilities, show an example fault-tolerant encoding, and discuss its impact on communicating parallel arithmetic circuits in an example error scenario.

Adaptive arithmetic coding is a general technique for coding source symbols of a stochastic process based on an adaptive model. The adaptive model provides measures of the statistics of source symbols and is updated, along with encoding/decoding processes, when more encoded/decoded symbols are fed as samples to the adaptive model. The coding performance depends on how well the adaptive model fits the statistics of source symbols. If the number of source symbols is large and the number of samples is small, the adaptive model may not be able to provide valid measures of the statistics, which results in an inefficient coding performance of the adaptive arithmetic coder. To this end, this paper presents segmentation of source symbols to improve the performance of the adaptive arithmetic coder. Each source symbol is divided into several segments. Each segment is separately coded with an adaptive arithmetic coder. With this division, possible values of each segment are concentrated within a small range. Given the limited number of samples, this concentration leads to a better fit of the adaptive model to the statistics of source symbols and therefore to an improvement of the coding efficiency. The proposed coding algorithm is applied to lossless motion vector coding for video transmission as an application example to show its performance improvement and coding gains.

The adaptive Huffman coding with a window of limited distinct symbols is proposed. The window buffer is used to store a specified number of distinct symbols most recently processed. The total number of symbols within the window may vary, but number of distinct symbols does not exceed a specified value. The adaptive Huffman tree is constructed based on the probability distribution of symbols within the window. Then, a variant of the proposed method is introduced. The proposed variant uses two windows. A small primary window buffer is used to store the most recently processed symbols. A comparatively large secondary window buffer is used to store more past processed symbols. The two separate Huffman tree are constructed based on the probabilities of symbols within the two windows. A symbol is encoded using first Huffman tree if it is found in the first window. Otherwise, the symbol is encoded using second Huffman tree if it is found in the second window. The first proposed technique comparatively offers better results than its counterpart for most of the file type. The performance of the second proposed technique is also close to the other techniques.

A simple and efficient nonblock coding scheme for binary sources is suggested. It uses a probability model to map any source string to a unique integer, and thereby defines an enumeration of all possible strings. Contrary to normal enumerative coding, the new method is noncombinatorial and operates sequentially, incrementing the code value symbol by symbol, by simple arithmetic. It is akin to arithmetic coding but does not use intervals. For memoryless sources, the redundancy per symbol is shown to be asymptotically less than p3 bits, where p is the smaller of symbol probabilities. Especially, for integer-valued 1/p , the code is asymptotically optimal. These results are confirmed by both analysis and experiments with arbitrary-precision arithmetic. In a practical implementation, the source is partitioned into substrings enabling restricted-precision arithmetic. Thanks to subtle implicit coding, the additional redundancy is marginal. The source model can be also context-based, and even adaptivity can be incorporated. A peculiarity of the method is that decoding is done backwards (LIFO). Hence, for higher-order models, the string suffix, not prefix, is used as the context domain. The speed of the practical version is close to that of binary arithmetic coders.