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The document discusses different data compression techniques, focusing on lossless dictionary-based compression algorithms like Lempel-Ziv (LZ). It explains how LZ algorithms like LZW work by building an adaptive dictionary during compression and decompression. The LZW algorithm is described through examples. Advantages of LZW include no need to explicitly transfer the dictionary and fast encoding/decoding through table lookups. Problems like running out of dictionary space are also addressed.
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Assembly Language Programming By Ytha Yu, Charles Marut Chap 4 (Introduction to IBM ec Assembly. Language)
Module 1-System Software PROGRAMS.pptx
This document contains examples of SIC and SIC/XE assembly language instructions for performing various operations like arithmetic, data movement, string operations, and subroutines. It provides 17 multi-line examples of code sequences to perform tasks like setting variables to values, copying arrays, finding maximum array elements, and reading/writing records to devices.
Ieee2013_upgrading_knowledge_matlab_pt2
This document summarizes a MatLab workshop covering various topics:
- MatLab Central, an official user community for asking and answering questions
- Cipher systems for encrypting text by shifting letters
- Least squares methods for fitting linear models to noisy data
- Dynamic code generation to display customized text
- Techniques for accelerating MatLab code such as vectorization and memory preallocation
The workshop emphasized best practices like utilizing help functions, saving intermediate work, and having fun with programming.
Tech Days Paris Intoduction F# and Collective Intelligence
This document provides an overview of a presentation on the F# programming language. It discusses why F# was created, provides a taste of the F# language features like functional programming and object-oriented programming. It also demonstrates a complete F# application for classifying bloggers using a "Collective Intelligence" algorithm. The application uses hierarchical clustering to group similar blogs based on the frequency of words. The document discusses the architecture of the application and how it was implemented efficiently in F#.
Compiler unit 5
The document discusses code generation in compilers. It covers:
- The main tasks of a code generator are instruction selection, register allocation and assignment, and instruction ordering.
- Code generators map an intermediate representation to target machine code for a specific architecture. This mapping can vary in complexity depending on the level of the intermediate representation and the target architecture.
- Key issues in code generator design include addressing in the target code, representing the program as basic blocks and flow graphs, register allocation, and selecting machine instructions to represent operations.
Cs6660 compiler design may june 2016 Answer Key
The document describes the various phases of a compiler:
1. Lexical analysis breaks the source code into tokens.
2. Syntax analysis generates a parse tree from the tokens.
3. Semantic analysis checks for semantic correctness using the parse tree and symbol table.
4. Intermediate code generation produces machine-independent code.
5. Code optimization improves the intermediate code.
6. Code generation translates the optimized code into target machine code.
C language by Dr. D. R. Gholkar
C is a general purpose programming language that is like a sea - its depths cannot fully be explored even after years of practice. The document discusses the history of C and other programming languages it evolved from like B, highlights C's importance and flexibility. It also covers basic C programming concepts like data types, operators, statements and functions. The last few sections describe structures, unions, pointers and how to write and execute a simple C program.
Introducing Arc: A Common Intermediate Language for Unified Batch and Stream...
Today's end-to-end data pipelines need to combine many diverse workloads such as machine learning, relational operations, stream dataflows, tensor transformations, and graphs. For each of these workload types exist several frontends (e.g., DataFrames/SQL, Beam, Keras) based on different programming languages as well as different runtimes (e.g., Spark, Flink, Tensorflow) that target a particular frontend and possibly a hardware architecture (e.g., GPUs). Putting all the pieces of a data pipeline together simply leads to excessive data materialisation, type conversions and hardware utilisation as well as miss-matches of processing guarantees.
Our research group at RISE and KTH in Sweden has founded Arc, an intermediate language that bridges the gap between any frontend and a dataflow runtime (e.g., Flink) through a set of fundamental building blocks for expressing data pipelines. Arc incorporates Flink and Beam-inspired stream semantics such as windows, state and out of order processing as well as concepts found in batch computation models. With Arc, we can cross- compile and optimise diverse tasks written in any programming language into a unified dataflow program. Arc programs can run on various hardware backends efficiently as well as allowing seamless, distributed execution on dataflow runtimes. To that end, we showcase Arcon a concept runtime built in Rust that can execute Arc programs natively as well as presenting a minimal set of extensions to make Flink an Arc-ready runtime.
Algorithms and flowcharts
This document discusses algorithms, flowcharts, pseudocode, and decision structures. It begins by defining algorithms and their purpose in problem solving. It then explains flowchart symbols and how to represent algorithms visually using flowcharts. Several examples are provided of writing pseudocode, detailed algorithms, and corresponding flowcharts to solve problems. The document also covers decision structures like if-then-else statements and relational operators. It provides examples of algorithms using nested if statements. Finally, it presents an example of determining an employee bonus based on overtime worked and absences.
ITU - MDD - Textural Languages and Grammars
This presentation describes the use and design of textural domain specific language - DSL. It has two basic purposes:
Introduce you to some of the more important design criteria in language design
Introduce you to BNF
This presentation is developed for MDD 2010 course at ITU, Denmark.
lempel_ziv
This document discusses Lempel-Ziv source coding techniques for data compression. It describes LZ77 and LZ78, which are families of lossless data compression algorithms derived from the original LZ algorithms proposed in 1977 and 1978. LZ77 uses pointers to match repeated patterns, while LZ78 uses a dictionary. Variations include LZSS, LZH, LZB for LZ77, and LZW and LZFG for LZ78. The document analyzes the performance of these algorithms based on average bits per character. LZ78 variations generally perform better than LZ77 variations. In conclusion, Lempel and Ziv laid the foundation for most modern lossless compression algorithms.
Data compression & Classification
Data Compression, Lossy and Lossless Data Compression,Classification of Lossy and Lossless Data Compression, Huffman Codding method, LZW method of Lossless Compression and Compression Ratio
Traduccion a ensamblador
descripcion breve de como traducir sentencias de programa basicas como if en bloques de codigo ensamblador
Compiler Construction | Lecture 8 | Type Constraints
This lecture covers type checking with constraints. It introduces the NaBL2 meta-language for writing type specifications as constraint generators that map a program to constraints. The constraints are then solved to determine if a program is well-typed. NaBL2 supports defining name binding and type structures through scope graphs and constraints over names, types, and scopes. Examples show type checking patterns in NaBL2 including variables, functions, records, and name spaces.
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Design and Implementation of LZW Data Compression Algorithm
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More from anithabalaprabhu
Shannon Fano
The document discusses Shannon-Fano encoding, which is an early method for data compression that constructs efficient binary codes for information sources without memory. It works by assigning shorter codes to more frequent messages and longer codes to less frequent messages. The encoding process involves recursively splitting the set of messages in half based on probability until each message has its own unique code. While Shannon-Fano codes are reasonably efficient, they are not always optimal and the codes generated can depend on how the initial splitting of messages is done.
Ch 04 Arithmetic Coding ( P P T)
Arithmetic coding is an entropy encoding technique that maps a sequence of symbols to a number between 0 and 1. Each possible sequence is assigned a unique interval within this range. As symbols are processed, the interval boundaries are updated based on the symbol probabilities. This allows arithmetic coding to efficiently encode sequences without needing to pre-determine codes for all possible sequences. It can achieve compression close to the entropy limit for long sequences, and easily supports adaptive and context modeling to handle non-IID sources. The interval updates ensure a unique number can be sent to decode the full sequence.
Compression
The document discusses different types of data compression techniques. It explains that compression reduces the size of data to reduce bandwidth and storage requirements when transmitting or storing audio, video, and images. It describes how compression works by exploiting redundancies in the data as well as properties of human perception. Various compression methods are outlined, including lossless techniques that preserve all information as well as lossy methods for audio and video that can tolerate some loss of information. Specific algorithms discussed include Huffman coding, run-length coding, LZW coding, and arithmetic coding. The document also provides details on JPEG image compression and MPEG video compression standards.
Datacompression1
The document discusses entropy and Shannon's first theorem. It defines entropy as the average amount of information received per symbol from a source where symbols have different probabilities. Entropy measures the uncertainty in the probability distribution of the source. The entropy of a source is equal to the minimum expected code length needed to encode symbols from that source.
Speech Compression
This document summarizes various audio compression techniques used to reduce the required storage space and transmission bandwidth for digital audio. It discusses lossless compression techniques that remove redundant data without degrading quality, as well as lossy techniques that remove inaudible or irrelevant information, resulting in smaller file sizes but some loss of quality. The key techniques described include psychoacoustic modeling to determine inaudible components, spectral analysis using transforms or filter banks, noise allocation to minimize quantization noise, and additional methods like predictive coding, coupling/delta encoding, and Huffman coding.
Z24 4 Speech Compression
The document discusses several speech compression techniques:
1) Uncompressed audio rates are 64kbps for voice and 1.5Mbps for CD audio.
2) ADPCM reduces the data rate to 32kbps by transmitting differences between predicted and actual sample values.
3) SB-ADPCM splits audio into lower and upper sub-bands, compressing each at different rates (48kbps and 16kbps respectively) for total of 64kbps.
4) LPC represents speech as a combination of previous samples and residual error, transmitting just coefficients, enabling lower rates like those in GSM and other standards.
Module 4 Arithmetic Coding
This document discusses arithmetic coding, a technique for data compression. It begins by explaining how real numbers can be represented in binary and how this relates to encoding strings based on their probabilities. The document then provides examples of how to generate codes for strings and explains the coding and decoding algorithms. It discusses issues like scaling, integer arithmetic coding, adaptation of probabilities, and handling zero frequency and end of file problems. In the end, it compares arithmetic coding to Huffman coding, noting their similarities and differences in terms of compression performance, context handling, and adaptation.
Ch 04 Arithmetic Coding (Ppt)
Arithmetic coding is an entropy encoding technique that maps a sequence of symbols to a numeric interval between 0 and 1. Each symbol maps to a sub-interval of the current interval based on the symbol probabilities. As symbols are processed, the interval boundaries are updated according to the cumulative distribution function of the symbol probabilities. Arithmetic coding achieves better compression than Huffman coding by allowing coding of variable-length blocks without pre-specifying code lengths. It also handles conditional probability models more efficiently by updating interval boundaries based on context without needing pre-specified codebooks for all contexts.
Compression Ii
The document discusses various techniques for image compression including:
1) Variable length coding which reduces the first order entropy but not the second order entropy due to inter-pixel redundancy.
2) Predictive coding which codes only the difference between actual and predicted pixel values to remove inter-pixel redundancy.
3) Lossy compression techniques like transform coding and delta modulation which allow for higher compression ratios than lossless techniques by introducing some loss of information.
06 Arithmetic 1
This document discusses arithmetic coding, an entropy encoding technique. It begins with an introduction comparing arithmetic coding to Huffman coding. The document then provides pseudocode for the basic encoding and decoding algorithms. It describes how scaling techniques like E1 and E2 scaling allow for incremental encoding and decoding as well as achieving infinite precision with finite-precision integers. The document outlines applications of arithmetic coding in areas like JBIG, H.264, and JPEG 2000.
Arithmetic Coding
The document provides an overview of digital media basics including digitization, compression, representation, and standards. It discusses signal digitization through pulse code modulation and sampling. It also covers quantization, digitization examples, and lossless versus lossy compression. Specific compression techniques covered include transform coding, variable rate coding, predictive coding, and entropy coding like Huffman coding. The document also discusses psychoacoustic modeling and perceptual coding. It provides examples of speech, audio, image and video compression standards and techniques.
Lassy
The document discusses lossy compression techniques. It begins by explaining that lossy compression algorithms compress data by discarding some information, yielding much higher compression ratios than lossless compression but resulting in distorted approximations of the original data. It then covers various lossy compression methods including quantization, transform coding using the discrete cosine transform (DCT), wavelet-based coding using the discrete wavelet transform (DWT), and techniques like vector quantization (VQ) and the Karhunen-Loeve transform (KLT) that aim to decorrelate signal components before quantization. Key aspects like rate-distortion theory, various distortion measures, and algorithms for quantization are also described.
Compression Ii
The document discusses various image compression techniques including:
1) Variable length coding such as Huffman coding which reduces redundancy but is not optimal for the sample image due to inter-pixel redundancy.
2) Predictive coding which encodes pixel differences to remove redundancy, achieving higher compression ratios.
3) Lossy techniques like JPEG use quantization and discrete cosine transform to compress images further, introducing some distortion.
Lossy
The document discusses lossy compression techniques such as quantization and transform coding. It explains that lossy compression achieves higher compression rates by introducing some loss or distortion to the reconstructed data compared to the original. It describes various distortion measures used to evaluate the quality of lossy compression, including mean squared error and peak signal-to-noise ratio. It also discusses the discrete cosine transform, a widely used transform coding technique that decomposes images or signals into different frequency components for more efficient compression.
Planning
The document discusses various aspects of project planning and control, including defining control variables, product/process/resource characteristics, different control situations, and techniques for project planning and control. It covers archetypical control situations like realization, allocation, design, and exploration problems. Risk management strategies are also summarized, including identifying risk factors, determining risk exposure, and developing risk mitigation strategies.
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.
Losseless
This document discusses lossless data compression and introduces key concepts such as codecs, prefix codes, entropy, and Shannon's source coding theorem. Specifically:
- Lossless data compression allows for perfect retrieval of original files by storing/transmitting big files using fewer bytes, with examples like ZIP files.
- A codec consists of a coder that maps messages to codewords and a decoder that maps codewords back to the original messages. Prefix codes are codes where no codeword is a prefix of another.
- Entropy measures the uncertainty in a probabilistic source, with the entropy of a distribution being the expected self-entropy of messages drawn from that distribution.
- Shannon's source coding theorem states that the expected
Lec32
The document discusses Huffman coding, a lossless data compression algorithm that uses variable-length codewords to encode symbols based on their frequency of occurrence. It works by building a binary tree from the frequency of symbols, where more frequent symbols are encoded by shorter codewords. This allows for more efficient representation of frequent symbols and achieves compression close to the theoretical minimum possible given the frequencies. The algorithm and encoding/decoding process are explained step-by-step with an example.
Lec5 Compression
1. The document discusses different compression techniques for text, audio, images, and video.
2. It provides examples of compression ratios achieved using lossy and lossless compression methods. For example, text compression can achieve 3:1 ratios using Lempel-Ziv coding while audio compression can achieve ratios between 3:1 to 24:1 using MP3.
3. The techniques discussed include entropy encoding, run-length encoding, Huffman coding, discrete cosine transforms, and differential encoding which takes advantage of redundancies in the data. The best approach depends on the type of data and acceptable quality.
Huffman Student
The document discusses Huffman coding, which is a variable-length binary coding technique used for lossless data compression. It describes how Huffman codes are constructed using a Huffman tree to assign codewords to symbols based on their frequency of occurrence, with more frequent symbols assigned shorter codewords. The document outlines the Huffman coding algorithm and provides examples of how it works to generate an optimal prefix code with the minimum average codeword length.
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https://alandix.com/academic/papers/synergy2024-epistemic/
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Dictor
- 1. A Review of Data Compression Techniques Presented by Sudeepta Mishra Roll# CS200117052 At NIST,Berhampur Under the guidance of Mr. Rowdra Ghatak
- 11. The LZW Algorithm (Compression) Flow Chart START W= NULL IS EOF ? K=NEXT INPUT IS WK FOUND? W=WK OUTPUT INDEX OF W ADD WK TO DICTIONARY STOP W=K YES NO YES NO
- 22. LZW Decompression Algorithm read a character k; output k; w = k; while ( read a character k ) /* k could be a character or a code. */ { entry = dictionary entry for k; output entry; add w + entry[0] to dictionary; w = entry; }
- 23. LZW Decompression Algorithm Flow Chart START Output K IS EOF ? K=NEXT INPUT ENTRY=DICTIONARY INDEX (K) ADD W+ENTRY[0] TO DICTIONARY STOP W=ENTRY K=INPUT W=K YES NO Output ENTRY
- 34. Thank You