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In this paper, we consider a network that supports multiple multicast flows with network coding. It is well-known that network coding can be used to increase the throughput of a delay-free network. However, as there are multiple multicast flows in the network, packets might be delayed due to contention. As packets from the same flow have to be synchronized for network coding, additional buffers, known as em synchronization buffers in the literature, are required. In certain networks, such as multistage interconnection networks in switch fabrics , the size of internal buffers could be quite limited. One of the key contributions of the paper is to propose a packet scheduling algorithm so that synchronization buffers can be bounded by a em finite constant that only depends on the maximum hop count of the flows. We also show that our scheduling algorithm does not cause any throughput degradation as it can stabilize the network for any em admissible traffic. Moreover, our algorithm is em universal and it does not require to know the rates of the flows in the network. The main idea of our algorithm is to introduce em fictitious packets in the dynamic frame sizing algorithm recently proposed in for stabilizing switches and em wired networks (without network coding). By so doing, packets that might be stalled in synchronization buffers can be flushed out. We show that the number of fictitious packets in each frame is also bounded by a finite constant that only depends on the maximum hop count. [ABSTRACT FROM PUBLISHER]

The embedded deterministic test-based compression uses cube merging to reduce a pattern count, the amount of test data, and test time. It gradually expands a test pattern by incorporating compatible test cubes. This paper demonstrates that compression ratios can be order of magnitude higher, if the cube merging continues despite conflicts on certain positions. Our novel solution produces test clusters, each comprising a parent pattern and a number of its derivatives obtained by imposing extra bits on it. In order to load scan chains with patterns that feature original test cubes, only data necessary to recreate parent patterns as well as information regarding locations and values of the corresponding conflicting bits are required. A test controller can then deliver tests by repeatedly applying the same parent pattern, every time using a different control pattern to decide whether a given scan chain receives data from the parent pattern, or another pattern is used instead to recover content of the original test cube. Compression of incompatible test cubes preserves all benefits of continuous flow decompression and offers compression ratios of order 1000\times with encoding efficiency much higher than 1.0. We also demonstrate that test clusters make it possible to deliver test patterns in a flexible power-aware fashion. This framework achieves significant reductions in switching activity during scan loading as well as additional test data volume reductions due to encoding algorithms employed to compress parent and control vectors. [ABSTRACT FROM PUBLISHER]

The size of a high-density-camera-array (HDCA)-based light field image (LFI) is usually very large, containing hundreds of high-resolution views. Therefore, there is an urgent need to efficiently compress it. Currently, no compression algorithms, specially, for the HDCA-based LFI have been designed. In this paper, we propose an algorithm based on a quadtree-based 2D hierarchical coding framework for the HDCA-based LFI data compression. The proposed framework has the following contributions. First, we organize the views of the HDCA-based LFI into a quadtree-based coding structure. Under this structure, all of the views are divided into four quadrants at the first level. Each quadrant is further sub-divided into four quadrants at each subsequent level. The process continues until the desired depth is reached. This quadtree-based coding structure can make full use of the strong inter-view correlations to improve the coding efficiency. In addition, the proposed quadtree-based structure can be easily extended to a general 2D hierarchical structure with variable group of pictures (GOP) sizes to adapt to the reference frame buffer constraint. Second, we try to improve the performance of the 2D hierarchical coding framework using the distance-based criteria for both the reference frame selection and motion vector scaling. Third, a one-pass optimal bit allocation scheme is proposed to further optimize the performance by taking the quality dependencies among various views into consideration. The proposed framework is implemented in the newest video coding standard, high efficiency video coding (HEVC). The experimental results show that the proposed quadtree-based 2D hierarchical coding framework can achieve an average of over 25% bitrate saving compared with the 1D hierarchical coding structure. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

Higher circuit densities in system-on-chip (SOC) designs have led to drastic increase in test data volume. Larger test data size demands not only higher memory requirements, but also an increase in testing time. Test data compression addresses this problem by reducing the test data volume without affecting the overall system performance. This paper proposes a novel test data compression technique using bitmasks which provides a substantial improvement in the compression efficiency without introducing any additional decompression penalty. The major contributions of this paper are as follows: 1) it develops an efficient bitmask selection technique for test data in order to create maximum matching patterns; 2) it develops an efficient dictionary selection method which takes into account the bitmask based compression; and 3) it proposes a test compression technique using efficient dictionary and bitmask selection to significantly reduce the testing time and memory requirements .We have applied our method on various test data sets and compared our results with other existing test compression techniques. Our algorithm outperforms existing dictionary-based approaches by up to 30%, giving a best possible test compression of 92%. [ABSTRACT FROM AUTHOR]

In this paper, we propose three improved codebook searching algorithms for vector quantization (VQ). Our improved schemes are based on three fast searching methods proposed by Huang et al., IEEE Transactions on Image Processing, 1(3), 1992, 413-416 and the double test (DT) method proposed by Torres and Huguet, IEEE Transactions on Communications, 42(2|3|4), 1994, 208-210. Compared with three codebook searching schemes proposed by Huang et al. [9], we find our proposed algorithms have better performance in terms of encoding time and distortion computations according to tile experimental results. [ABSTRACT FROM AUTHOR]

The rapid growth in the amount of data in the digital world leads to the need for data compression, and so forth, reducing the number of bits needed to represent a text file, an image, audio, or video content. Compressing data saves storage capacity and speeds up data transmission. In this paper, we focus on the text compression and provide a comparison of algorithms (in particular, entropy-based arithmetic and dictionary-based Lempel–Ziv–Welch (LZW) methods) for text compression in different languages (Croatian, Finnish, Hungarian, Czech, Italian, French, German, and English). The main goal is to answer a question: "How does the language of a text affect the compression ratio?" The results indicated that the compression ratio is affected by the size of the language alphabet, and size or type of the text. For example, The European Green Deal was compressed by 75.79%, 76.17%, 77.33%, 76.84%, 73.25%, 74.63%, 75.14%, and 74.51% using the LZW algorithm, and by 72.54%, 71.47%, 72.87%, 73.43%, 69.62%, 69.94%, 72.42% and 72% using the arithmetic algorithm for the English, German, French, Italian, Czech, Hungarian, Finnish, and Croatian versions, respectively. [ABSTRACT FROM AUTHOR]

Natural type selection was originally proposed by Zamir and Rose for universal single description coding. In this paper, we generalize this principle to universal multiple description coding (MDC). Two schemes based on random codebooks are proposed: one is of fixed distortion and the other is of fixed weight. Their operational sum-rate-distortion functions are derived, which coincide with the EGC (El Gamal-Cover) sum-rate bound if the parameters of the schemes are optimized. It is also shown that in both schemes the joint type of reconstruction codewords can be used to improve the rate-distortion (R-D) performance. Based on our theoretical results, a practical universal scheme is proposed by leveraging the MDC methods based on low-density generator matrix (LDGM) codes. The performance of this scheme is compared experimentally with the EGC bound, which shows its effectiveness. [ABSTRACT FROM AUTHOR]

SUMMARY The Semantic Web contains many billions of statements, which are released using the resource description framework (RDF) data model. To better handle these large amounts of data, high performance RDF applications must apply a compression technique. Unfortunately, because of the large input size, even this compression is challenging. In this paper, we propose a set of distributed MapReduce algorithms to efficiently compress and decompress a large amount of RDF data. Our approach uses a dictionary encoding technique that maintains the structure of the data. We highlight the problems of distributed data compression and describe the solutions that we propose. We have implemented a prototype using the Hadoop framework, and evaluate its performance. We show that our approach is able to efficiently compress a large amount of data and scales linearly on both input size and number of nodes. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Information theory traditionally deals with “conventional data,” be it textual data, image, or video data. However, databases of various sorts have come into existence in recent years for storing “unconventional data” including biological data, social data, web data, topographical maps, and medical data. In compressing such data, one must consider two types of information: the information conveyed by the structure itself, and the information conveyed by the data labels implanted in the structure. In this paper, we attempt to address the former problem by studying information of graphical structures (i.e., unlabeled graphs). As the first step, we consider the Erdös–Rényi graphs \cal G(n,p) over n vertices in which edges are added independently and randomly with probability p. We prove that the structural entropy of \cal G(n,p) is $n\choose 2h(p)-\log n!+o(1)=n\choose2h(p)-n\log n+O(n),$ where h(p)=-p\log p-(1-p)\log (1-p) is the entropy rate of a conventional memoryless binary source. Then, we propose a two-stage compression algorithm that asymptotically achieves the structural entropy up to the n\log n term (i.e., the first two leading terms) of the structural entropy. Our algorithm runs either in time O(n^2) in the worst case for any graph or in time O(n+e) on average for graphs generated by \cal G(n,p), where e is the average number of edges. To the best of our knowledge, this is the first provable (asymptotically) optimal graph compressor for Erdös-Rényi graph models. We use combinatorial and analytic techniques such as generating functions, Mellin transform, and poissonization to establish these findings. Our experiments confirm the theoretical results and also show the usefulness of our algorithm for some real-world graphs such as the Internet, biological networks, and social networks. [ABSTRACT FROM PUBLISHER]

Huffman encoding and arithmetic coding algorithms have shown great potential in the field of image compression. These algorithms are the origin of current image compression techniques. Nevertheless, there are some deficiencies in both algorithms that use the frequencies of the characters in the data. They aim to represent the symbols used in the data in the shortest bit sequence. However, they represent data that has a low frequency of use with very long bit sequences. The arithmetic coding algorithm was developed to address the shortcomings of the Huffman encoding algorithm. This paper proposes an efficient, alternative encoding algorithm that uses the Huffman encoding algorithm. The main objective of the proposed algorithm is to reduce the number of bits that are symbolized with long bit codewords by the Huffman encoding algorithm. Initially, the Huffman encoding algorithm is applied to the data. The characters that are represented by the short bit sequence from the Huffman encoding algorithm are ignored. Flag bits are then added according to whether the successive symbols are on the same leaf. If the next character is not on the same leaf, flag bit "0" is added, otherwise flag bit "1" is added between the characters. In other words, the key significance of this algorithm is that it uses the effective aspects of the Huffman encoding algorithm, and it also proposes a solution to long bit sequences that cannot be efficiently represented. Most importantly, the validity of the algorithm is meticulously evaluated with three different groups of images. Randomly selected images from the USC-SIPI and STARE databases, and randomly selected standard images on internet, are used. The algorithm encodes compressing operations for images successfully. Some images that have a balanced tree structure have yielded close results compared to other algorithms. However, when the total results are inspected, the proposed encoding algorithm achieved excellent results. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of universal lossless coding on a countable infinite alphabet. It focuses on some classes of sources defined by an envelope condition on the marginal distribution, namely exponentially decreasing envelope classes with exponent \alpha. The minimax redundancy of exponentially decreasing envelope classes is proved to be equivalent to 1 \over 4 \alpha \log e \log^2 n. Then, an adaptive algorithm is proposed, whose maximum redundancy is equivalent to the minimax redundancy. [ABSTRACT FROM PUBLISHER]

In this paper, an improved tree-structured codebook search algorithm is proposed. It is called the two-level codebook search algorithm (TLCS). The goal of this method is to reduce the computation cost of the multi-path-searching algorithm for TSVQ. It speeds up the codebook search by searching only a two-level codebook tree instead of the whole TSVQ codebook tree. According to the experimental results, a significant time reduction can be achieved with this method, compared to the multi-path-searching algorithm. In other words, our TLCS outperforms the multi-path-searching algorithm. [ABSTRACT FROM AUTHOR]

While compressing a file with a Huffman code, it is possible that the size of the file grows temporarily. This happens when the source letters with low frequencies (to which long codewords are assigned) are encoded first. The maximum data expansion is the average growth in bits per source letter resulting from the encoding of a source letter with a long codeword. It is a measure of the worst case temporary growth of the file. In this paper we study the maximum data expansion of Huffman codes. We provide some new properties of the maximum data expansion δ of Huffman codes and using these properties we prove that δ < 1.256. [ABSTRACT FROM AUTHOR]

The more advanced multi-view extension, MV-HEVC, effectively exploits visual similarities between multi-view videos and enables high compression efficiency. Each view in the multi-view sequence depends on the captured scene, the distance between cameras and recording angles. Increasing the distance between dependent viewpoints generates an inter-view disparity. This impacts the inter-view similarities, affects the disparity estimation and further increases the computational complexity of the MV-HEVC encoder. In this paper, an efficient earlier disparity estimation is proposed for low complexity MV-HEVC. This algorithm is based on reducing the complexity of disparity estimation by eliminating the inter-view offset. Moreover, the inter-view similarities are controlled by considering the reliability of each coding unit size in the search range. This reliability is estimated by reducing the number of searching points within a new limited window. For reliable motion estimation, we further proposed an earlier decision of coding units splitting in the dependent views according to those in the reference views. Experimental results show that the proposed algorithm can achieve an average encoding time saving of 20.37%–40,61% with marginal performance degradation. • Visual adjustment between views can be improved by eliminating inter-view offsets. • Eliminating inter-view offsets reduces search time in the disparity estimation. • The visual adjustment allows a trade-off between encoding time and video quality. • An earlier decision of coding units splitting for reliable motion estimation. [ABSTRACT FROM AUTHOR]

Privacy amplification (PA) is an indispensable component in classical and quantum cryptography. Error correction (EC) and data compression (DC) algorithms are also indispensable in classical and quantum information theory. We here study these three algorithms (PA, EC, and DC) in the presence of quantum side information, and show that they all become equivalent in the one-shot scenario. As an application of this equivalence, we take previously known security bounds of PA, and translate them into coding theorems for EC and DC which have not been obtained previously. Further, we apply these results to simplify and improve our previous result that the two prevalent approaches to the security proof of quantum key distribution (QKD) are equivalent. We also propose a new method to simplify the security proof of QKD. [ABSTRACT FROM AUTHOR]

The paper proposes a novel method to compress color images with imperceptible quality loss. The algorithm explores the difference in error perceptibility of human visual system (HVS) for various areas. It is done by implementing different non-uniform quantizers for flat, detail and random blocks of pixels. These blocks are classified based on principle component analysis (PCA) and prediction error. For hardware implementation purpose, the algorithm is designed to use very low line memory. Simulation results show that the proposed compression is visually lossless in all categories of tested images with high compression ratio. [ABSTRACT FROM PUBLISHER]

In this paper, a new and low-complexity embedded compression (EC) algorithm for the JPEG2000 encoder system is proposed to efficiently reduce memory requirements. The proposed EC algorithm is used to achieve a fixed compression ratio of 50% under the near-losslesscompression constraint. Through the EC technique, the memory requirement for intermediate low-frequency coefficients during multiple DWT stages can be reduced by a factor of 2 compared with direct implementation of the JPEG2000 encoder. By jointly considering the coding flow of both block-based 2D-DWT and bit-plane coders, the EC scheme combines an efficient quantization and simple entropy coder to reduce memory transaction bandwidth between DWT and bit-plane coder (BPC) to 50%. Furthermore, this EC reduces the size of code-block memory from DWT to BPC. Experiment results based on standard test image benchmarks show that our proposed EC algorithm presents only PSNR degradation of 1.05 dB on average when the target compression ratio is fixed at 50%. Our EC can be applicable to the JPEG2000 encoding system to save memory size and bandwidth requirements with minor picture quality degradation. The proposed EC algorithm is also computationally simple, reducing latency time and creating sufficient accessibility1. [ABSTRACT FROM AUTHOR]

This paper presents a new compression method for compressed Firmware-over-the-air (FOTA) that reconstructs a new compressed version from an old compressed version of firmware stored in NAND flash memory. The compressed FOTA requires a small delta that minimizes the amount of update data and an efficient compression method that provides fast encoding and decoding to achieve fast updating and extraction. A fast dictionarybased compression algorithm for the program code is introduced. With a high compression ratio, our method provides relatively fast encoding and very fast decoding. We demonstrate that the compression time during the update process can be reduced by utilizing the copy information stored in the delta without increasing the size of the delta. We present some experimental results to show the impact of this method in terms of the compression ratio, extraction time and updating time1. [ABSTRACT FROM AUTHOR]

In this paper, an efficient, region-of-interest (ROT) coding scheme achieved by modifying the implicit ROT encoding method is proposed. This new method reduces the priority of background coefficients in the ROT code-block without compromising algorithm complexity. It is suitable for applications in which it may be desirable to encode the ROT to a higher quality level than the background. In addition, several EBCOT-based ROT coding schemes for image compression are discussed. Experimental results demonstrate that the proposed ROT coding scheme improves the compression efficiency and combines the advantages of the implicit ROT encoding method (low complexity) and the maxshift method (good ROT rate distortion performance). [ABSTRACT FROM AUTHOR]

Memory and network bandwidth are decisive bottlenecks when handling high-resolution multidimensional data sets in visualization applications, and they increasingly demand suitable data compression strategies. We introduce a novel lossy compression algorithm for multidimensional data over regular grids. It leverages the higher-order singular value decomposition (HOSVD), a generalization of the SVD to three dimensions and higher, together with bit-plane, run-length and arithmetic coding to compress the HOSVD transform coefficients. Our scheme degrades the data particularly smoothly and achieves lower mean squared error than other state-of-the-art algorithms at low-to-medium bit rates, as it is required in data archiving and management for visualization purposes. Further advantages of the proposed algorithm include very fine bit rate selection granularity and the ability to manipulate data at very small cost in the compression domain, for example to reconstruct filtered and/or subsampled versions of all (or selected parts) of the data set. [ABSTRACT FROM AUTHOR]

We present a generic and real-time time-varying point cloud codec for 3D immersive video. This codec is suitable for mixed reality applications in which 3D point clouds are acquired at a fast rate. In this codec, intra frames are coded progressively in an octree subdivision. To further exploit inter-frame dependencies, we present an inter-prediction algorithm that partitions the octree voxel space in $N \times N \times N$ macroblocks ( $N=8,16,32$ ). The algorithm codes points in these blocks in the predictive frame as a rigid transform applied to the points in the intra-coded frame. The rigid transform is computed using the iterative closest point algorithm and compactly represented in a quaternion quantization scheme. To encode the color attributes, we defined a mapping of color per vertex attributes in the traversed octree to an image grid and use legacy image coding method based on JPEG. As a result, a generic compression framework suitable for real-time 3D tele-immersion is developed. This framework has been optimized to run in real time on commodity hardware for both the encoder and decoder. Objective evaluation shows that a higher rate-distortion performance is achieved compared with available point cloud codecs. A subjective study in a state-of-the-art mixed reality system shows that introduced prediction distortions are negligible compared with the original reconstructed point clouds. In addition, it shows the benefit of reconstructed point cloud video as a representation in the 3D virtual world. The codec is available as open source for integration in immersive and augmented communication applications and serves as a base reference software platform in JTC1/SC29/WG11 (MPEG) for the further development of standardized point-cloud compression solutions. [ABSTRACT FROM PUBLISHER]

A major difficulty that plagues the practical use of Slepian-Wolf (SW) coding (and distributed source coding in general) is that the precise correlation among sources needs to be known a priori. To resolve this problem, we propose an adaptive asymmetric SW decoding scheme using particle based belief propagation (PBP). We explain the adaptive scheme for asymmetric setup in detail and then further extend it to the non-asymmetric setup based on the code partitioning approach. Moreover, we introduce a Metropolis-Hastings (MH) algorithm in the resampling step, which efficiently decreases the number of simulation iterations. We show through experiments that the proposed algorithm can simultaneously reconstruct the compressed sources and estimate the joint correlation among sources. Further, comparing to the conventional SW decoder based on standard belief propagation, the proposed approach can achieve higher compression under varying correlation statistics. [ABSTRACT FROM AUTHOR]

Variable length codes (VLCs) are widely adopted in many compression standards due to their good coding efficiency on average codeword length. However, an inherent problem with a VLC is that an error of even one bit can cause serious error propagation and thus loss of synchronization at the receiver, which would lead to a series of non-correctly decoded symbols. Reversible variable length codes (RVLCs) were introduced to significantly mitigate this phenomenon. In this work, a method to find an optimal RVLC in terms of the minimum average codeword length is first formulated as a tree-searching problem, and then, instead of performing an exhaustive search, an A*-based construction algorithm is proposed to find an optimal RVLC. The proposed algorithm has been applied to several benchmarks for sources and has found respective optimal symmetric and asymmetric RVLCs. [ABSTRACT FROM PUBLISHER]