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Introduction to data compression
Introduction to Data Compression, Third Edition, is a concise and comprehensive guide to data compression. This book introduces the reader to the theory underlying today's compression techniques with detailed instruction for their applications using several examples to explain the concepts. Encompassing the entire field of data compression, it covers lossless and lossy compression, Huffman coding, arithmetic coding, dictionary techniques, context based compression, scalar and vector quantization. It includes all the cutting edge updates the reader will need during the work day and in class. This edition adds new content on the topic of audio compression including a description of the mp3 algorithm, along with a new video coding standard and new facsimile standard explained. It explains in detail established and emerging standards in depth including JPEG 2000, JPEG-LS, MPEG-2, Group 3 and 4 faxes, JBIG 2, ADPCM, LPC, CELP, and MELP. Source code is provided via a companion web site that gives readers the opportunity to build their own algorithms, choose and implement techniques in their own applications. This book will appeal to professionals, software and hardware engineers, students, and to anyone interested in digital libraries and multimedia.
eBook
Multilinear subspace learning : dimensionality reduction of multidimensional data
\"Due to advances in sensor, storage, and networking technologies, data is being generated on a daily basis at an ever-increasing pace in a wide range of applications, including cloud computing, mobile Internet, and medical imaging. This large multidimensional data requires more efficient dimensionality reduction schemes than the traditional techniques. Addressing this need, multilinear subspace learning (MSL) reduces the dimensionality of big data directly from its natural multidimensional representation, a tensor. Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data gives a comprehensive introduction to both theoretical and practical aspects of MSL for the dimensionality reduction of multidimensional data based on tensors. It covers the fundamentals, algorithms, and applications of MSL. Emphasizing essential concepts and system-level perspectives, the authors provide a foundation for solving many of today's most interesting and challenging problems in big multidimensional data processing. They trace the history of MSL, detail recent advances, and explore future developments and emerging applications.The book follows a unifying MSL framework formulation to systematically derive representative MSL algorithms. It describes various applications of the algorithms, along with their pseudocode. Implementation tips help practitioners in further development, evaluation, and application. The book also provides researchers with useful theoretical information on big multidimensional data in machine learning and pattern recognition. MATLAB source code, data, and other materials are available at www.comp.hkbu.edu.hk/haiping/MSL.html\"-- Provided by publisher
Book
An Evolving TinyML Compression Algorithm for IoT Environments Based on Data Eccentricity
Currently, the applications of the Internet of Things (IoT) generate a large amount of sensor data at a very high pace, making it a challenge to collect and store the data. This scenario brings about the need for effective data compression algorithms to make the data manageable among tiny and battery-powered devices and, more importantly, shareable across the network. Additionally, considering that, very often, wireless communications (e.g., low-power wide-area networks) are adopted to connect field devices, user payload compression can also provide benefits derived from better spectrum usage, which in turn can result in advantages for high-density application scenarios. As a result of this increase in the number of connected devices, a new concept has emerged, called TinyML. It enables the use of machine learning on tiny, computationally restrained devices. This allows intelligent devices to analyze and interpret data locally and in real time. Therefore, this work presents a new data compression solution (algorithm) for the IoT that leverages the TinyML perspective. The new approach is called the Tiny Anomaly Compressor (TAC) and is based on data eccentricity. TAC does not require previously established mathematical models or any assumptions about the underlying data distribution. In order to test the effectiveness of the proposed solution and validate it, a comparative analysis was performed on two real-world datasets with two other algorithms from the literature (namely Swing Door Trending (SDT) and the Discrete Cosine Transform (DCT)). It was found that the TAC algorithm showed promising results, achieving a maximum compression rate of 98.33%. Additionally, it also surpassed the two other models regarding the compression error and peak signal-to-noise ratio in all cases.
Journal Article
Stream-Based Visually Lossless Data Compression Applying Variable Bit-Length ADPCM Encoding
Video applications have become one of the major services in the engineering field, which are implemented by server–client systems connected via the Internet, broadcasting services for mobile devices such as smartphones and surveillance cameras for security. Recently, the majority of video encoding mechanisms to reduce the data rate are mainly lossy compression methods such as the MPEG format. However, when we consider special needs for high-speed communication such as display applications and object detection ones with high accuracy from the video stream, we need to address the encoding mechanism without any loss of pixel information, called visually lossless compression. This paper focuses on the Adaptive Differential Pulse Code Modulation (ADPCM) that encodes a data stream into a constant bit length per data element. However, the conventional ADPCM does not have any mechanism to control dynamically the encoding bit length. We propose a novel ADPCM that provides a mechanism with a variable bit-length control, called ADPCM-VBL, for the encoding/decoding mechanism. Furthermore, since we expect that the encoded data from ADPCM maintains low entropy, we expect to reduce the amount of data by applying a lossless data compression. Applying ADPCM-VBL and a lossless data compression, this paper proposes a video transfer system that controls throughput autonomously in the communication data path. Through evaluations focusing on the aspects of the encoding performance and the image quality, we confirm that the proposed mechanisms effectively work on the applications that needs visually lossless compression by encoding video stream in low latency.
Journal Article
Imaging biological tissue with high-throughput single-pixel compressive holography
Single-pixel holography (SPH) is capable of generating holographic images with rich spatial information by employing only a single-pixel detector. Thanks to the relatively low dark-noise production, high sensitivity, large bandwidth, and cheap price of single-pixel detectors in comparison to pixel-array detectors, SPH is becoming an attractive imaging modality at wavelengths where pixel-array detectors are not available or prohibitively expensive. In this work, we develop a high-throughput single-pixel compressive holography with a space-bandwidth-
time
product (SBP-T) of 41,667 pixels/s, realized by enabling phase stepping naturally in time and abandoning the need for phase-encoded illumination. This holographic system is scalable to provide either a large field of view (~83 mm
2
) or a high resolution (5.80 μm × 4.31 μm). In particular, high-resolution holographic images of biological tissues are presented, exhibiting rich contrast in both amplitude and phase. This work is an important step towards multi-spectrum imaging using a single-pixel detector in biophotonics.
Single-pixel holography generates holographic images with a single-pixel detector making this relatively inexpensive. Here the authors report a high-throughput single-pixel compressive holography method for imaging biological tissue which can either provide a large field of view or high resolution.
Journal Article
Digital media pattern design compression and optimization method based on K-means clustering and LLE dimensionality reduction
The rapid development of digital media demands higher quality in pattern design. Simultaneously, significant redundant information hinders the transmission and sharing of these patterns, creating an urgent need for image compression. However, traditional image compression methods struggle to balance efficiency and quality. To address this, a new image compression model for digital media patterns is proposed, based on K-means clustering and Locally Linear Embedding methods. This model creates an efficient compression solution by integrating dynamic clustering parameter selection based on color histograms, multi-dimensional image segmentation of color and texture, and a local linear embedding dimensionality reduction algorithm using dynamic neighborhood selection. The model achieves a compression ratio of 84% and a Peak Signal-to-Noise Ratio of 41dB, with no significant quality difference before and after compression, validating the effectiveness of the improvements made to the basic methods. In practical application experiments, the model's multi-scale structural similarity reaches 0.71. When processing a large number of patterns, the fastest response time is 189ms, with a minimum memory usage of 16.1M. The shortest processing time on a single-core processor is 0.34s. These experimental results demonstrate that the model balances compression efficiency and quality, offering superior compression performance, good robustness, and the ability to handle various complex tasks and adapt to different application scenarios, meeting the high standards of the digital media industry for image compression.
Journal Article
Reducing bulky medical images via shape-texture decoupled deep neural networks
The explosive growth of medical data poses significant challenges for storage and sharing. Current compression techniques utilizing Implicit Neural Representations (INRs) effectively strike a balance between encoding accuracy and compression ratio, yet they suffer from slow encoding speeds. By contrast, data-driven compressors encode fast but heavily rely on the training data and cannot generalize well. To develop a practical compression tool overcoming all these limitations, we introduce Shape-Texture Decoupled Compression (DeepSTD), which focuses on the data set of the same modality and body parts and proposes decoupling the variations into shape and texture components for separate encoding. Disentangling two components facilitates designing proper encoding strategies suitable for their respective characteristics—swift shape encoding based on INRs and effective data-driven texture encoding. The proposed approach combines the advantages of INR-based and data-driven models, to achieve high fidelity, fast encoding speed, as well as good generalizability. Comprehensive evaluations on large-scale Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) datasets demonstrate superior performance across encoding quality, compression ratio, and speed. Besides, with features like parallel acceleration with multiple Graphics Processing Units (multi-GPU), flexible control of compression ratio, and broad applicability, DeepSTD offers a robust and efficient solution for the pressing demands of modern medical data compression.
The authors introduce Shape-Texture Decoupled Compression (DeepSTD), a practical compression tool for medical imaging data. They provide evaluations on different modality imaging datasets, providing a route towards a solution for the pressing demands of modern medical data compression.
Journal Article
zDUR: reference-free FASTQ compressor with high compression ratio and speed
Background
High-throughput sequencing technologies generate massive amounts of FASTQ data comprising nucleotide sequences, quality scores, and read identifiers, necessitating efficient compression to alleviate storage and transmission burdens. Compared to general-purpose compressors, specialized FASTQ compressors achieve higher compression performance by exploiting the inherent redundancy in FASTQ files. However, existing FASTQ-specialized compressors often suffer from limited data applicability and tend to over-optimize either compression ratio or compression speed at the expense of the other.
Results
We present zDUR, a reference-free FASTQ compressor designed for efficient and scalable handling of next-generation sequencing data across diverse platforms and sequencing data types. Benchmarking against six reference-free compressors on 15 representative datasets spanning four sequencing data types demonstrates that zDUR achieves a favorable overall balance between compression ratio and speed, with broad applicability across data types. In particular, on single-cell RNA-seq and spatial transcriptomics datasets, zDUR achieves over a tenfold increase in runtime performance while maintaining higher compression ratios than SPRING, one of the state-of-the-art reference-free FASTQ compressors.
Conclusions
zDUR offers a scalable and efficient solution for reference-free FASTQ compression, balancing performance, speed, and usability across diverse datasets.
Journal Article
Effective image compression using transformer and residual network for balanced handling of high and low-frequency information
Image compression has made significant progress through end-to-end deep-learning approaches in recent years. The Transformer network, coupled with self-attention mechanisms, efficiently captures high-frequency features during image compression. However, the low-frequency information in the image cannot be obtained well through the Transformer network. To address this issue, the paper introduces a novel end-to-end autoencoder architecture for image compression based on the transformer and residual network. This method, called Transformer and Residual Network (TRN), offers a comprehensive solution for efficient image compression, capturing essential image content while effectively reducing data size. The TRN employs a dual network, comprising a self-attention pathway and a residual network, intricately designed as a high-low-frequency mixer. This dual-network can preserve both high and low-frequency features during image compression. The end-to-end training of this model employs rate-distortion optimization (RDO methods). Experimental results demonstrate that the proposed TRN method outperforms the latest deep learning-based image compression methods, achieving an impressive 8.32% BD-rate (bit-rate distortion performance) improvement on the CLIC dataset. In comparison to traditional methods like JPEG, the proposed achieves a remarkable BD-rate improvement of 70.35% on the CLIC dataset.
Journal Article