Books about Data compression (found 539 titles)

Network Performance Management: Lag, Round-trip Time, Packet Loss, Retransmission, Throughput, Data Compression, Encryption

High Quality Content by WIKIPEDIA articles! Network performance management consists of measuring, modeling, planning, and optimizing networks to ensure that they carry traffic with the speed, reliability, and capacity that is appropriate for the nature of the application and the cost constraints of the organization. Different applications warrant different blends of capacity, latency, and reliability. Network managers perform many tasks; these include performance measurement, forensic analysis, capacity planning, and load-testing or load generation. They also work closely with application developers and IT departments who rely on them to deliver underlying network services.Unfortunately, not all networks are the same. As data is broken into component parts (often known frames, packets, or segments) for transmission, several factors can affect their delivery.

Portable Network Graphics: Raster Graphics, Image File Formats, Lossless Data Compression, Graphics Interchange Format, Recursive Acronym, RGB Color Model, Usenet Newsgroup

High Quality Content by WIKIPEDIA articles! Portable Network Graphics (PNG) is a bitmapped image format that employs lossless data compression. PNG was created to improve upon and replace GIF (Graphics Interchange Format) as an image-file format not requiring a patent license. It is pronounced /?p??/ or spelled out as P-N-G. The PNG acronym is optionally recursive, unofficially standing for ?PNG's Not GIF?. PNG supports palette-based (palettes of 24-bit RGB or 32-bit RGBA colors), greyscale, RGB, or RGBA images. PNG was designed for transferring images on the Internet, not professional graphics, and so does not support other color spaces (such as CMYK).

One-Way Compression Function: Cryptography, One-Way Function, Data Compression, Merkle?Damg?rd Construction, MDC-2, IBM, Collision Resistance, SHA Hash Functions, MD5

High Quality Content by WIKIPEDIA articles! In cryptography, a one-way compression function is a function that transforms two fixed length inputs to an output of the same size as one of the inputs. The transformation is "one-way", meaning that it is difficult given a particular output to compute inputs which compress to that output. One-way compression functions are not related to data compression, which by definition is invertible. A one-way compression function One-way compression functions are for instance used in the Merkle-Damg?rd construction inside cryptographic hash functions.

One-Way Function: One-Way Compression Function, Cryptography, Data Compression, Merkle?Damg?rd Construction, MDC-2, IBM, Collision Resistance, SHA Hash Functions, MD5, Computational Complexity

High Quality Content by WIKIPEDIA articles! In computer science, a one-way function is a function that is easy to compute on every input, but hard to invert given the image of a random input. Here "easy" and "hard" are to be understood in the sense of computational complexity theory, specifically the theory of polynomial time problems. The existence of such one-way functions is still an open conjecture. In fact, their existence would prove that the complexity classes P and NP are distinct, thus resolving the foremost unsolved question of theoretical computer science.

Vector Quantization: Quantization, Signal Processing, Data Compression, Centroid, K-Means Clustering, Clustering, Density Estimation, Self-Organizing Map, Simulated Annealing, Entropy Encoding

High Quality Content by WIKIPEDIA articles! Vector quantization is a classical quantization technique from signal processing which allows the modeling of probability density functions by the distribution of prototype vectors. It was originally used for data compression. It works by dividing a large set of points (vectors) into groups having approximately the same number of points closest to them. Each group is represented by its centroid point, as in k-means and some other clustering algorithms. The density matching property of vector quantization is powerful, especially for identifying the density of large and high-dimensioned data. Since data points are represented by the index of their closest centroid, commonly occurring data have low error, and rare data high error. This is why VQ is suitable for lossy data compression. It can also be used for lossy data correction and density estimation. Vector quantization is based on the competitive learning paradigm, so it is closely related to the self-organizing map model.

Predictive Artificial Neural Networks: A Block-adaptive Scheme for Lossless Telemetry Data Compression

Data compression deals with removal of redundancy, reducing bandwidth and thus lowering transmission and storage costs. Telemetry data can be sensitive to inaccuracies and require lossless compression for exact reconstruction at the receiver. One technology that has been successfully applied in a wide range of applications is artificial neural networks (ANN), a massively parallel system with pattern recognition capabilities. This monograph is a reproduction of the author?s postgraduate thesis work at Multimedia University, Malaysia. A two-stage predictor-encoder combination is proposed, incorporating a variety of feedforward, recurrent and radial basis ANN architectures, as the predictors. The encoders are well known compression algorithms. Characteristic features of the models, transmission issues and other practical considerations are taken into account to determine optimised configuration of the schemes. Significant compression results are reported, along with a critical review of the strengths and weaknesses of over 50 implementations simulated with satellite telemetry data.

Data Compression In Imaging (Synthesis Lectures on Image, Video, and Multimedia Processing)

High Performance Embedded Architectures and Compilers: 5th International Conference, HiPEAC 2010, Pisa, Italy, January 25-27, 2010, Proceedings (Lecture ... Computer Science and General Issues)

This book constitutes the refereed proceedings of the 5th International  Conference on High Performance Embedded Architectures and Compilers, HiPEAC 2010, held in Pisa, Italy, in January 2010.

The 23 revised full papers presented together with the abstracts of 2 invited keynote addresses were carefully reviewed and selected from 94 submissions. The papers are organized in topical sections on architectural support for concurrency; compilation and runtime systems; reconfigurable and customized architectures; multicore efficiency, reliability, and power; memory organization and optimization; and programming and analysis of accelerators.

Hyperspectral Data Compression

Hyperspectral Data Compression provides a survey of recent results in the field of compression of remote sensed 3D data, with a particular interest in hyperspectral imagery. Chapter 1 addresses compression architecture, and reviews and compares compression methods. Chapters 2 through 4 focus on lossless compression (where the decompressed image must be bit for bit identical to the original). Chapter 5, contributed by the editors, describes a lossless algorithm based on vector quantization with extensions to near lossless and possibly lossy compression for efficient browning and pure pixel classification. Chapter 6 deals with near lossless compression while. Chapter 7 considers lossy techniques constrained by almost perfect classification. Chapters 8 through 12 address lossy compression of hyperspectral imagery, where there is a tradeoff between compression achieved and the quality of the decompressed image. Chapter 13 examines artifacts that can arise from lossy compression.

Pattern-Based Compression of Multi-Band Image Data for Landscape Analysis

This book describes an integrated approach to using remotely sensed data in conjunction with geographic information systems for landscape analysis. Remotely sensed data are compressed into an analytical image-map that is compatible with the most popular geographic information systems as well as freeware viewers. The approach is most effective for landscapes that exhibit a pronounced mosaic pattern of land cover. The image maps are much more compact than the original remotely sensed data, which enhances utility on the internet. As value-added products, distribution of image-maps is not affected by copyrights on original multi-band image data.

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