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nesThe document discusses different techniques for compressing multimedia data such as text, images, audio and video. It describes how compression works by removing redundancy in digital data and exploiting properties of human perception. It then explains different compression methods including lossless compression, lossy compression, entropy encoding, and specific algorithms like Huffman encoding and arithmetic coding. The goal of compression is to reduce the size of files to reduce storage and bandwidth requirements for transmission.
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![[object Object],[object Object],Why Compress ? In almost all multimedia applications, a technique known as compression is first applied to the source information prior to its Transmission. This is done either to reduce the volume of information to be transmitted – text , fax and images or to reduce the bandwidth that is required for its transmission – speech , audio and video.](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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3) Popular lossless compression techniques, like Run Length Encoding and Huffman coding, exploit coding and interpixel redundancies. Lossy techniques introduce controlled loss for further compression.
Paper id 24201469
This document proposes an Android application that uses Huffman encoding to compress SMS messages. It summarizes that Huffman coding assigns shorter code words to more frequently used symbols, allowing SMS text to be compressed. The application requires installation on both the sender and receiver's phones to decompress messages. Testing showed the technique achieved up to 89% compression, reducing the size of example SMS texts. The summary provides an overview of the key points about using Huffman coding for SMS compression and the proposed mobile application.
Huffman Coding
A complete slide that represent huffman coding and tree with examples explained and very easy to understantd
Data comm4&5Data Communications (under graduate course) Lecture 3 of 5
Undergraduate course content:
Introduction: Types and sources of data, communication models, standards.
Data transmission: techniques, transmission media and characteristics.
Information theory: Information sources, information measure, entropy, source codes.
Line codes: characteristics, return-to-zero and non-return-to-zero signaling, bipolar alternate mark inversion, code (radix, redundancy and efficiency), important codes in current use, frequency spectra characteristics of common line codes, receiver clock synchronization, optical fiber systems, scramblers.
Modems: characteristics, modulation, equalization, control, V-standards.
Error Control: Transmission impairments, forward error control, linear block codes, feedback error control.
A research paper_on_lossless_data_compre
This document provides an overview of lossless data compression techniques. It discusses Huffman coding, Shannon-Fano coding, and Run Length Encoding as common lossless compression algorithms. Huffman coding assigns variable length binary codes to symbols based on their frequency, with more common symbols getting shorter codes. Shannon-Fano coding similarly generates a binary tree to assign codes but aims for a roughly equal probability between left and right subtrees. Run Length Encoding replaces repeated sequences with the length of the run and the symbol. The document contrasts lossless techniques that preserve all data with lossy techniques used for media that can tolerate some loss of information.
2.3 unit-ii-text-compression-a-outline-compression-techniques-run-length-codi...
This document discusses various techniques for lossless data compression, including run-length coding, Huffman coding, adaptive Huffman coding, arithmetic coding, and Shannon-Fano coding. It provides details on how each technique works, such as assigning shorter codes to more frequent symbols in Huffman coding and dynamically updating codes based on the data stream in adaptive Huffman coding. The document also discusses the importance of compression techniques for reducing the number of bits needed to store or transmit data.
Ijrdtvlis11 140006
This document summarizes and compares entropy and dictionary-based techniques for lossless data compression. It discusses how entropy coding like Huffman coding assigns shorter codes to more frequent symbols, making it well-suited for JPEG images. Dictionary techniques like LZW replace strings with codes, performing better on files with repetitive data like TIFFs. While entropy coding has simpler encoding, dictionary methods have better compression ratios and decoding capability. The document also presents the proposed Huffman coding of a bitmap image to assign variable-length codes based on color probabilities.
Huffman ppt
The document discusses Huffman coding, which is a lossless data compression algorithm that uses variable-length codes to encode symbols based on their frequency of occurrence. It begins with definitions of Huffman coding and related terms. It then describes the encoding and decoding processes, which involve constructing a Huffman tree based on symbol frequencies and traversing the tree to encode or decode data. An example is provided that shows the full process of constructing a Huffman tree for a sample frequency table and determining the Huffman codes, average code length, and total encoded length.
VII Compression Introduction
The document discusses data compression fundamentals including why compression is needed, information theory basics, classification of compression algorithms, and the data compression model. It notes that digital representations of analog signals require huge storage and bandwidth for transmission. Compression aims to represent source data with as few bits as possible while maintaining acceptable fidelity through modeling and coding phases. Algorithms can be lossless or lossy depending on whether reconstruction is exact. Performance is evaluated based on compression ratio, quality, complexity, and delay.
Dn4301681689
This document discusses error-correcting codes that can be used to protect encoder and decoder circuitry in memory from soft errors. It introduces Euclidean Geometry Low-Density Parity-Check (EG-LDPC) codes that have fault-secure detector capabilities and can tolerate high bit or nanowire defect rates. Using some smaller EG-LDPC codes, the entire memory system failure-in-time rate can be kept at or below one for memory blocks of 10Mb or larger, with a memory density of 1011 bit/cm and 10nm nanowire pitch. Larger EG-LDPC codes can achieve even higher reliability and lower area overhead.
Text compression
Lossless Compression ,Text compression (lossless) , Run-length Encoding , Huffman Coding , Shannon-FANO Coding.
Data representation
This document discusses various methods of data representation and compression. It begins by defining data representation as how information is conceived, manipulated, and stored, which can vary between environments. It then discusses several common data representation formats including ASCII, EBCDIC, and Shift-JIS character encodings as well as network data formats like ASN.1 and XDR. The document also covers popular image, audio, and video file compression standards and algorithms like JPEG, PNG, MP3, and MPEG.
Data representation
The document discusses various topics related to data representation including:
1) How data is represented depends on the environment and each has its own rules and standards.
2) Popular network data representations include ASN.1 and XDR, with ASN.1 being a more robust standard that describes data structures unambiguously while XDR is simpler.
3) ASN.1 is commonly used for applications such as network management, secure email, and telephony due to its ability to exchange structured data across networks and platforms.
B034205010
The document provides an overview of techniques for secure speech communication, including speech coding, speaker identification, and encryption/decryption. It discusses various speech coding techniques like waveform coding, parametric coding, and hybrid coding that can compress speech signals while maintaining quality. It also describes speaker identification methods using hashing to authenticate users. For encryption, it outlines symmetric techniques like AES that use a shared key, and asymmetric techniques like RSA that use public/private key pairs. The goal is to integrate these methods to provide a high level of security for speech communication by removing redundancy, authenticating speakers, and strongly encrypting signals.
Lecture 6 -_presentation_layer
The presentation layer is responsible for data representation, compression, encryption and formatting for transmission between applications. It encodes application data into messages and decodes received messages. Common data representations include ASN.1 and XDR. Lossy and lossless compression techniques are used to reduce file sizes. Encryption transforms plaintext into ciphertext using keys to protect confidentiality during transmission.
Compressionbasics
The document discusses data compression fundamentals including why compression is needed, information theory basics, classification of compression algorithms, and compression performance metrics. It notes that high quality audio, video, and images require huge storage and bandwidth that compression addresses. Compression algorithms involve modeling data redundancy and entropy encoding. Lossy compression achieves higher compression but with reconstruction error, while lossless compression exactly reconstructs data. Key metrics include compression ratio, subjective quality scores, and objective measures like PSNR.
Lossless
The document discusses various lossless compression techniques including entropy coding methods like Huffman coding and arithmetic coding. It also covers dictionary-based coding like LZW, as well as spatial compression techniques like run-length coding, quadtrees for images, and lossless JPEG.
Similar to Chapter%202%20 %20 Text%20compression(2) (20)
Sunzip user tool for data reduction using huffman algorithm
Sunzip user tool for data reduction using huffman algorithm
Data comm4&5Data Communications (under graduate course) Lecture 3 of 5
Data comm4&5Data Communications (under graduate course) Lecture 3 of 5
2.3 unit-ii-text-compression-a-outline-compression-techniques-run-length-codi...
2.3 unit-ii-text-compression-a-outline-compression-techniques-run-length-codi...
Chapter%202%20 %20 Text%20compression(2)
- 1. Text Compression Chapter 2
- 7. Source Encoders and destination decoders Prior to transmitting the source information relating to a multimedia application , a compression algorithm is applied to it. This implies that in order for the destination to reproduce the original source information or in some instances, a nearly exact copy of it – a matching decompression algorithm must be applied to it. The application of the compression algorithm is the main function carried out by the source encoder and the decompression algorithm is carried out by the destination decoder .
- 8. Source Encoders and destination decoders In applications which involve two computers communicating with each other, the time required to perform the compression and decompression algorithm is not always critical. So both algorithms are normally implemented in software within the two computers. Source information Source encoder program Destination Decoder Program Copy of Source Information Network Source encoder / destination decoder : Software only Source Computer Destination Computer
- 9. Source Encoders and destination decoders In other applications, however the time required to perform the compression and decompression algorithms in software is not acceptable and instead the two algorithms must be performed by special processors in separate units . Source information Source encoder Processor Destination Decoder Processor Copy of Source Information Network Source Computer Destination Computer Source encoder / destination decoder : Special processors/hardware
- 14. Statistical Encoding In this technique, patterns of bits (word) or that are more frequent are recorded using shorter codes. It uses a set of variable length codewords with the shortest codewords used to represent the most frequently occurring symbols. For example : In a string of text , the character A may occurs more frequently than say the character P which occurs more frequently than the character Z , and so on… Statistical encoding exploits this property by using a set of variable length Codewords With the shortest codewords used to represent the most frequently occurring symbols.
- 16. In practice , the use of variable-length codewords is not quite as straight forward as it first appears. Clearly as with run-length encoding , the destination must know the set of codewords being used by the source. With variable length codewords , however in order for the decoding operation to be carried out correctly , it is necessary to ensure that a shorter codeword in the set does not form the start of a longer codeword otherwise the decoder will interpret the string on the wrong codeword boundaries. A codeword set that avoids this happening is said to process the prefix and an Encoding scheme that generates codewords that have this property is the Huffman encoding algorithm.
- 21. Static Huffman Encoding 7 00 7 01 9 10 6 111 2 1100 4 1101 14 21 9 12 7 7 6 6 2 4 0 1 0 0 0 0 1 1 1 1
- 22. Decoding Algorithm End Is codeword already stored ? Begin Set CODEWORD to empty Read next bit from BITSTREAM and append to existing bits in CODEWORD Load matching ASCII Character into Receive _buffer All bits in BITSTREAM Processed n n