Vector Quantization
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Vector Quantization and Signal Compression
Author: Allen Gersho
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Herb Caen, a popular columnist for the San Francisco Chronicle, recently quoted a Voice of America press release as saying that it was reorganizing in order to "eliminate duplication and redundancy. " This quote both states a goal of data compression and illustrates its common need: the removal of duplication (or redundancy) can provide a more efficient representation of data and the quoted phrase is itself a candidate for such surgery. Not only can the number of words in the quote be reduced without losing informa tion, but the statement would actually be enhanced by such compression since it will no longer exemplify the wrong that the policy is supposed to correct. Here compression can streamline the phrase and minimize the em barassment while improving the English style. Compression in general is intended to provide efficient representations of data while preserving the essential information contained in the data. This book is devoted to the theory and practice of signal compression, i. e. , data compression applied to signals such as speech, audio, images, and video signals (excluding other data types such as financial data or general purpose computer data). The emphasis is on the conversion of analog waveforms into efficient digital representations and on the compression of digital information into the fewest possible bits. Both operations should yield the highest possible reconstruction fidelity subject to constraints on the bit rate and implementation complexity.
Vector Quantization and Data Compression Simplified
Author: Pasquale De Marco
language: en
Publisher: Pasquale De Marco
Release Date: 2025-04-13
Embark on a transformative journey into the realm of vector quantization (VQ), where data takes on a new level of efficiency and representation. Within this comprehensive guide, you'll discover the intricacies of VQ, from its fundamental concepts to its wide-ranging applications across various domains. Unravel the essence of VQ, a technique that revolutionizes data representation by partitioning continuous data spaces into a finite set of code vectors. Witness how this quantization process unlocks a world of possibilities, enabling efficient storage, transmission, and analysis of information. Delve into the diverse array of VQ techniques, each meticulously crafted to cater to specific applications and data characteristics. Explore Lloyd's algorithm, a cornerstone iterative procedure for codebook design, and delve into the refinements offered by the generalized Lloyd algorithm. Discover the elegance of splitting algorithms, instrumental in constructing efficient codebooks for large datasets. Witness the transformative power of VQ in data compression, where it reigns supreme in reducing data redundancy while preserving essential information. Learn how VQ fuels image compression, audio compression, video compression, and speech compression, achieving remarkable data reduction without compromising quality. Venture beyond data compression and uncover the multifaceted applications of VQ in communication systems. Enhance channel coding, source coding, error control coding, modulation, and demodulation techniques with the prowess of VQ. Harness its capabilities to optimize signal processing, machine learning, and information theory, unlocking new horizons of data manipulation and analysis. Unearth the untapped potential of VQ in emerging frontiers, where innovation thrives. Explore its integration with deep learning architectures, generative modeling, reinforcement learning, natural language processing, and quantum information processing. Witness VQ's pivotal role in shaping the future of 6G and beyond, quantum computing, edge computing, and the Internet of Things. With this comprehensive guide as your compass, navigate the intricate world of VQ, unlocking its secrets and harnessing its transformative power. Whether you seek to expand your knowledge, fuel your research, or simply satisfy your curiosity, this book serves as an invaluable resource, guiding you through the intricacies of VQ and inspiring you to explore its boundless possibilities. If you like this book, write a review on google books!
Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and Beyond
The book presents the peer-reviewed contributions of the 15th International Workshop on Self-Organizing Maps, Learning Vector Quantization and Beyond (WSOM$+$ 2024), held at the University of Applied Sciences Mittweida (UAS Mitt\-weida), Germany, on July 10–12, 2024. The book highlights new developments in the field of interpretable and explainable machine learning for classification tasks, data compression and visualization. Thereby, the main focus is on prototype-based methods with inherent interpretability, computational sparseness and robustness making them as favorite methods for advanced machine learning tasks in a wide variety of applications ranging from biomedicine, space science, engineering to economics and social sciences, for example. The flexibility and simplicity of those approaches also allow the integration of modern aspects such as deep architectures, probabilistic methods and reasoning as well as relevance learning. The book reflects both new theoretical aspects in this research area and interesting application cases. Thus, this book is recommended for researchers and practitioners in data analytics and machine learning, especially those who are interested in the latest developments in interpretable and robust unsupervised learning, data visualization, classification and self-organization.