Run-length encoding (RLE) is a simple data compression technique that represents sequences of identical values (or "runs") in a more compact form. The basic principle of RLE is to replace consecutive occurrences of the same data value with a single value and a count of how many times that value occurs consecutively. ### How It Works 1. **Input**: Take a sequence of data that has repeated values.
SDCH stands for "Shared Data Compression Header." It is a technology related to data compression and web communication, specifically developed for use with HTTP. The SDCH format allows web browsers and servers to negotiate and share compressed data more efficiently, helping to reduce the size of transmitted data and improve loading times for web pages. SDCH works by enabling the server to send a secondary header that informs the client about how to decode the compressed data.
Scribal abbreviation refers to a writing practice used by scribes in which certain words, phrases, or letters are shortened or represented by symbols to save space and time while copying texts. This was especially common in medieval manuscripts where space on parchment was limited and the volume of text to be copied was large. Different types of scribal abbreviations were used, including: 1. **Contraction**: A part of the word is omitted, and the rest of the word is written out.
A self-extracting archive is a type of compressed file that contains both the compressed data and a small executable program that allows the user to extract the contents of the archive without needing additional software to do so. ### Key Features: 1. **Executable File**: Self-extracting archives are typically packaged as executable files (often with extensions like .exe on Windows). When the user runs this file, it automatically extracts the contents to a specified directory.
The Sequitur algorithm is a data compression algorithm that identifies and exploits patterns in sequences, making it particularly effective for tasks like data compression and pattern discovery. Developed by the researcher Nevill-Manning and Witten in the mid-1990s, the algorithm seeks to find repeated substrings in a given sequence and encode them in a way that reduces the overall size of the data.
Set partitioning in hierarchical trees refers to a method of organizing data into a hierarchical structure where elements are grouped into subsets based on certain criteria. This approach is commonly used in various fields like computer science, data mining, and organizational studies to manage and analyze complex data structures. Here’s an overview of the concept: ### Key Concepts: 1. **Hierarchical Tree Structure**: - A hierarchical tree is a data structure consisting of nodes arranged in a parent-child relationship.
Set redundancy compression refers to techniques used to reduce the size of data sets by eliminating redundancy within the data. This method aims to store the same information more efficiently, thereby minimizing the storage space required and improving the speed of data retrieval. ### Key Concepts of Set Redundancy Compression: 1. **Redundant Data:** In many datasets, particularly those containing large volumes of repeated elements or values, redundancy can occur.
Shannon coding, also known as Shannon-Fano coding, is a technique for data compression and encoding based on the principles laid out by Claude Shannon, one of the founders of information theory. It aims to represent symbols of a dataset (or source) using variable-length codes based on the probabilities of those symbols. The primary goal is to minimize the total number of bits required to encode a message while ensuring that different symbols have uniquely distinguishable codes.
Shannon–Fano coding is a method of lossless data compression that assigns variable-length codes to input characters based on their probabilities of occurrence. It is a precursor to more advanced coding techniques like Huffman coding. The fundamental steps involved in Shannon–Fano coding are as follows: 1. **Character Frequency Calculation**: Determine the frequency or probability of each character that needs to be encoded. 2. **Sorting**: List the characters in decreasing order of their probabilities or frequencies.
Shannon–Fano–Elias coding is a method of lossless data compression based on the principles of information theory developed by Claude Shannon and refined by others, including Robert Fano and Paul Elias. It is an algorithm that constructs variable-length prefix codes, which are used to encode symbols based on their probabilities. ### Overview of Shannon–Fano–Elias Coding: 1. **Probability Assignment**: Each symbol in the input data is assigned a probability based on its frequency of occurrence.
Silence compression, often referred to in the context of audio and speech processing, is a technique used to reduce the size of audio files by removing or minimizing periods of silence within the audio signal. This is particularly useful in various applications, such as telecommunication, podcasting, and audio streaming, where it is essential to optimize bandwidth and improve file storage efficiency.
The Smallest Grammar Problem (SGP) is a task in computational linguistics and formal language theory that involves finding the smallest possible grammar that can generate a given set of strings (a language). Specifically, the problem can be described as follows: Given a finite set of strings, the objective is to compute the smallest context-free grammar (CFG) or, in some contexts, the smallest regular grammar that generates exactly those strings.
Smart Bitrate Control (SBC) is a technology or methodology used primarily in video streaming and encoding to optimize the amount of data used during transmission. The main goal of Smart Bitrate Control is to ensure a balance between video quality and bandwidth efficiency, allowing for the best possible viewing experience without unnecessarily consuming available network resources.
Smart Data Compression refers to advanced techniques and algorithms used to reduce the size of data files while maintaining the integrity and usability of the information contained within them. Unlike traditional data compression methods, which may simply apply generic algorithms to reduce file size, smart data compression leverages contextual information, patterns within the data, and machine learning techniques to enhance the efficiency and effectiveness of the compression process.
Snappy is a compression and decompression library developed by Google designed for high throughput and low latency. Unlike some other compression algorithms that prioritize maximum compression ratio, Snappy focuses on speed and efficiency, making it particularly suitable for applications where speed is critical and where some loss in the compression ratio can be tolerated. ### Key Features of Snappy: 1. **Speed**: Snappy is optimized for fast compression and decompression, making it ideal for real-time applications.
Solid compression is a method used in data compression, particularly when compressing files or data structures that consist of multiple items, such as archives (like .zip or .tar files). Unlike traditional compression techniques, which typically compress data in a more generic way, solid compression treats a group of files or a complete dataset as a single block of data. The main idea behind solid compression is to achieve better compression ratios by eliminating redundancy across multiple files.
Speech coding, also known as speech compression or speech encoding, is the process of converting spoken language into a digital format that can be transmitted, stored, or processed efficiently. The primary goal of speech coding is to reduce the amount of data needed to represent speech while retaining sufficient quality for intelligibility and recognition. **Key aspects of speech coding include:** 1. **Compression Techniques**: Speech coders use various techniques to compress audio data.
Standard test images are reference images used primarily in the fields of image processing, computer vision, and image quality assessment. These images serve as benchmarks for evaluating algorithms, techniques, and systems by providing consistent and reproducible data for testing. They often contain a variety of features such as textures, colors, and patterns, making them suitable for assessing different aspects of image processing and analysis.
The Stanford Compression Forum is a research group based at Stanford University that focuses on the study and development of data compression techniques and algorithms. It serves as a platform for collaboration among researchers, industry professionals, and students interested in the field of compression, which encompasses various domains including image, video, audio, and general data compression. The forum aims to advance theoretical understanding, improve existing methods, and explore new compression technologies. It often brings together experts to share ideas, conduct workshops, and publish research findings.
Static Context Header Compression (SCHC) is a technique used to reduce the size of header information in machine-to-machine (M2M) communication, particularly in low-power wide-area networks (LPWANs) and Internet of Things (IoT) applications. It optimizes the transmission of packets in environments where bandwidth is constrained and energy efficiency is crucial. ### Key Features of SCHC: 1. **Contextualization**: SCHC utilizes a predefined static context to encode and decode headers.