A Reference-Free Lossless Compression Algorithm for DNA Sequences Using a Competitive Prediction of Two Classes of Weighted Models

The development of efficient data compressors for DNA sequences is crucial not only for reducing the storage and the bandwidth for transmission, but also for analysis purposes. In particular, the development of improved compression models directly influences the outcome of anthropological and biomedical compression-based methods. In this paper, we describe a new lossless compressor with improved compression capabilities for DNA sequences representing different domains and kingdoms. The reference-free method uses a competitive prediction model to estimate, for each symbol, the best class of models to be used before applying arithmetic encoding. There are two classes of models: weighted context models (including substitutional tolerant context models) and weighted stochastic repeat models. Both classes of models use specific sub-programs to handle inverted repeats efficiently. The results show that the proposed method attains a higher compression ratio than state-of-the-art approaches, on a balanced and diverse benchmark, using a competitive level of computational resources. An efficient implementation of the method is publicly available, under the GPLv3 license.Peer reviewe

Distributed Joint Source-Channel Coding in Wireless Sensor Networks

Considering the fact that sensors are energy-limited and the wireless channel conditions in wireless sensor networks, there is an urgent need for a low-complexity coding method with high compression ratio and noise-resisted features. This paper reviews the progress made in distributed joint source-channel coding which can address this issue. The main existing deployments, from the theory to practice, of distributed joint source-channel coding over the independent channels, the multiple access channels and the broadcast channels are introduced, respectively. To this end, we also present a practical scheme for compressing multiple correlated sources over the independent channels. The simulation results demonstrate the desired efficiency

A General Model for the Design of Efficient Sign-Coding Tools for Wavelet-Based Encoders

[EN] Traditionally, it has been assumed that the compression of the sign of wavelet coefficients is not worth the effort because they form a zero-mean process. However, several image encoders such as JPEG 2000 include sign-coding capabilities. In this paper, we analyze the convenience of including sign-coding techniques into wavelet-based image encoders and propose a methodology that allows the design of sign-prediction tools for whatever kind of wavelet-based encoder. The proposed methodology is based on the use of metaheuristic algorithms to find the best sign prediction with the most appropriate context distribution that maximizes the resulting sign-compression rate of a particular wavelet encoder. Following our proposal, we have designed and implemented a sign-coding module for the LTW wavelet encoder, to evaluate the benefits of the sign-coding tool provided by our proposed methodology. The experimental results show that sign compression can save up to 18.91% of bit-rate when enabling sign-coding capabilities. Also, we have observed two general behaviors when coding the sign of wavelet coefficients: (a) the best results are provided from moderate to high compression rates; and (b) the sign redundancy may be better exploited when working with high-textured images.This research was supported by the Spanish Ministry of Economy and Competitiveness under Grant RTI2018-098156-B-C54, co-financed by FEDER funds (MINECO/FEDER/UE).López-Granado, OM.; Martínez-Rach, MO.; Martí-Campoy, A.; Cruz-Chávez, MA.; Pérez Malumbres, M. (2020). A General Model for the Design of Efficient Sign-Coding Tools for Wavelet-Based Encoders. Electronics. 9(11):1-17. https://doi.org/10.3390/electronics9111899S117911Said, A., & Pearlman, W. A. (1996). A new, fast, and efficient image codec based on set partitioning in hierarchical trees. IEEE Transactions on Circuits and Systems for Video Technology, 6(3), 243-250. doi:10.1109/76.499834ISO/IEC 15444-1:2019. Information technology—JPEG 2000 Image Coding System—Part 1: Core Coding Systemhttps://www.iso.org/standard/78321.htmlTaubman, D. (2000). High performance scalable image compression with EBCOT. IEEE Transactions on Image Processing, 9(7), 1158-1170. doi:10.1109/83.847830Bilgin, A., Sementilli, P. J., & Marcellin, M. W. (1999). Progressive image coding using trellis coded quantization. IEEE Transactions on Image Processing, 8(11), 1638-1643. doi:10.1109/83.799891Oliver, J., & Malumbres, M. P. (2006). Low-Complexity Multiresolution Image Compression Using Wavelet Lower Trees. IEEE Transactions on Circuits and Systems for Video Technology, 16(11), 1437-1444. doi:10.1109/tcsvt.2006.883505Cho, Y., & Pearlman, W. A. (2007). Hierarchical Dynamic Range Coding of Wavelet Subbands for Fast and Efficient Image Decompression. IEEE Transactions on Image Processing, 16(8), 2005-2015. doi:10.1109/tip.2007.901247Deever, A. T., & Hemami, S. S. (2003). Efficient sign coding and estimation of zero-quantized coefficients in embedded wavelet image codecs. IEEE Transactions on Image Processing, 12(4), 420-430. doi:10.1109/tip.2003.811499Mallat, S., & Zhong, S. (1992). Characterization of signals from multiscale edges. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(7), 710-732. doi:10.1109/34.142909López-Granado, O., Galiano, V., Martí, A., Migallón, H., Martínez-Rach, M., Piñol, P., & Malumbres, M. P. (2013). Improving image compression through the use of evolutionary computing algorithms. Data Management and Security. doi:10.2495/data130041Kodak Lossless True Color Image Suitehttp://r0k.us/graphics/kodak/Rawzor—Lossless Compression Software for Camera Raw Imageshttp://imagecompression.info/test_images

Fixed Block Compression Boosting in FM-Indexes : Theory and Practice

The FM index (Ferragina and Manzini in J ACM 52(4):552-581, 2005) is a widely-used compressed data structure that stores a string T in a compressed form and also supports fast pattern matching queries. In this paper, we describe new FM-index variants that combine nice theoretical properties, simple implementation and improved practical performance. Our main theoretical result is a new technique called fixed block compression boosting, which is a simpler and faster alternative to optimal compression boosting and implicit compression boosting used in previous FM-indexes. We also describe several new techniques for implementing fixed-block boosting efficiently, including a new, fast, and space-efficient implementation of wavelet trees. Our extensive experiments show the new indexes to be consistently fast and small relative to the state-of-the-art, and thus they make a good off-the-shelf choice for many applications.Peer reviewe

Low-Complexity Approaches to Slepian–Wolf Near-Lossless Distributed Data Compression

This paper discusses the Slepian–Wolf problem of distributed near-lossless compression of correlated sources. We introduce practical new tools for communicating at all rates in the achievable region. The technique employs a simple “source-splitting” strategy that does not require common sources of randomness at the encoders and decoders. This approach allows for pipelined encoding and decoding so that the system operates with the complexity of a single user encoder and decoder. Moreover, when this splitting approach is used in conjunction with iterative decoding methods, it produces a significant simplification of the decoding process. We demonstrate this approach for synthetically generated data. Finally, we consider the Slepian–Wolf problem when linear codes are used as syndrome-formers and consider a linear programming relaxation to maximum-likelihood (ML) sequence decoding. We note that the fractional vertices of the relaxed polytope compete with the optimal solution in a manner analogous to that observed when the “min-sum” iterative decoding algorithm is applied. This relaxation exhibits the ML-certificate property: if an integral solution is found, it is the ML solution. For symmetric binary joint distributions, we show that selecting easily constructable “expander”-style low-density parity check codes (LDPCs) as syndrome-formers admits a positive error exponent and therefore provably good performance

On the Information Rates of the Plenoptic Function

The {\it plenoptic function} (Adelson and Bergen, 91) describes the visual information available to an observer at any point in space and time. Samples of the plenoptic function (POF) are seen in video and in general visual content, and represent large amounts of information. In this paper we propose a stochastic model to study the compression limits of the plenoptic function. In the proposed framework, we isolate the two fundamental sources of information in the POF: the one representing the camera motion and the other representing the information complexity of the "reality" being acquired and transmitted. The sources of information are combined, generating a stochastic process that we study in detail. We first propose a model for ensembles of realities that do not change over time. The proposed model is simple in that it enables us to derive precise coding bounds in the information-theoretic sense that are sharp in a number of cases of practical interest. For this simple case of static realities and camera motion, our results indicate that coding practice is in accordance with optimal coding from an information-theoretic standpoint. The model is further extended to account for visual realities that change over time. We derive bounds on the lossless and lossy information rates for this dynamic reality model, stating conditions under which the bounds are tight. Examples with synthetic sources suggest that in the presence of scene dynamics, simple hybrid coding using motion/displacement estimation with DPCM performs considerably suboptimally relative to the true rate-distortion bound.Comment: submitted to IEEE Transactions in Information Theor

Multiband and Lossless Compression of Hyperspectral Images

Hyperspectral images are widely used in several real-life applications. In this paper, we investigate on the compression of hyperspectral images by considering different aspects, including the optimization of the computational complexity in order to allow implementations on limited hardware (i.e., hyperspectral sensors, etc.). We present an approach that relies on a three-dimensional predictive structure. Our predictive structure, 3D-MBLP, uses one or more previous bands as references to exploit the redundancies among the third dimension. The achieved results are comparable, and often better, with respect to the other state-of-art lossless compression techniques for hyperspectral images