Unit 1 Introduction to Data Compression
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This document provides an introduction to data compression techniques. It discusses lossless and lossy compression methods. Lossless compression removes statistical redundancy without any loss of information, while lossy compression removes unnecessary or less important information, providing higher compression but allowing the reconstructed data to differ from the original. The document also covers measures of compression performance such as compression ratio, rate of compression, and distortion for lossy techniques. Overall, the document serves as an introduction and overview of key concepts in data compression.
Unit 1 Introduction to Data Compression
- 1. Lecture Notes on Introduction to Data Compression for Open Educational Resource on Data Compression(CA209) by Dr. Piyush Charan Assistant Professor Department of Electronics and Communication Engg. Integral University, Lucknow
- 2. Content • UNIT-I: Introduction to Compression Techniques: Loss less compression, Lossy Compression, Measures of performance, Modeling and coding, Mathematical Preliminaries for Lossless compression. • Introduction to Information Theory and Models: Physical models, Probability models, Markov models. 2 February 2021 2 Dr. Piyush, Charan Dept. of ECE, Integral University, Lucknow
- 3. What is Data Compression? • Data Compression = Modeling + Coding • data compression consists of taking a stream of symbols and transforming them into codes. If the compression is effective, the resulting stream of codes will be smaller than the original symbols. • The decision to output a certain code for a certain symbol or set of symbols is based on a model. • The model is simply a collection of data and rules used to process input symbols and determine which code(s) to output. 2 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 3
- 4. Other Definitions • Data compression is the process of converting an input data stream (the source stream or the original raw data) into another data stream (the output, the bitstream, or the compressed stream) that has a smaller size. A stream is either a file or a buffer in memory. • The field of data compression is often called source coding. We imagine that the input symbols (such as bits, ASCII codes, bytes, audio samples, or pixel values) are emitted by a certain information source and have to be coded before being sent to their destination. The source can be memoryless, or it can have memory. 2 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 4
- 5. Need of Compression • Why Data Compression? – There are two practical motivations for compression: • Make optimal use of limited storage space (Reduction of storage requirements) • Save time and help to optimize resources – If compression and decompression are done in I/O processor, less time is required to move data to or from storage subsystem, freeing I/O bus for other work – In sending data over communication line: less time to transmit and less storage to host 2 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 5
- 6. Data Compression • Data compression, source coding, or bit-rate reduction is the process of encoding information using fewer bits than the original representation. Any particular compression is either lossy or lossless. • Lossless compression reduces bits by identifying and eliminating statistical redundancy. No information is lost in lossless compression. • Lossy compression reduces bits by removing unnecessary or less important information. • Typically, a device that performs data compression is referred to as an encoder, and one that performs the reversal of the process (decompression) as a decoder. 2 February 2021 6 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow
- 7. Data Compression contd… • In compression technique or compression algorithm, we are actually referring to two algorithms. • There is the compression algorithm that takes an input and generates a representation that requires fewer bits, and there is a reconstruction algorithm (decompression algorithm) that operates on the compressed representation to generate the reconstruction . 2 February 2021 7 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow
- 8. Data Compression contd… Fig.1. Compression and Reconstruction 2 February 2021 8 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow
- 9. Process of Data Compression 2 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 9
- 10. • Based on the requirements of reconstruction, data compression schemes can be divided into two broad classes: • lossless compression schemes, in which is identical to , and • lossy compression schemes, which generally provide much higher compression than lossless compression but allow to be different from . 2 February 2021 10 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow
- 11. Types of Data Compression • Data compression is about storing and sending a smaller number of bits. • There are two major categories for methods to compress data: lossless and lossy methods. 2 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 11
- 12. Lossless Compression Methods • In lossless methods, original data and the data after compression and decompression are exactly the same. • Redundant data is removed in compression and added during decompression. • Lossless methods are used when we can’t afford to lose any data: legal and medical documents, computer programs. 2 February 2021 12 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow
- 13. Lossy Compression Methods • Used for compressing images and video files (our eyes cannot distinguish subtle changes, so lossy data is acceptable). • These methods are cheaper, require less time and space. • Several methods: – JPEG: compress pictures and graphics – MPEG: compress video – MP3: compress audio 2 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 13
- 14. Measure of Performance • A compression algorithm can be evaluated in a number of different ways. • We could measure- – the relative complexity of the algorithm, – the memory required to implement the algorithm, – how fast the algorithm performs on a given machine, – the amount of compression, and – how closely the reconstruction resembles the original. 2 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 14
- 15. 1. Compression Ratio • A very logical way of measuring how well a compression algorithm compresses a given set of data is to look at the ratio of the number of bits required to represent the data before compression to the number of bits required to represent the data after compression. This ratio is called the compression ratio. 2 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 15
- 16. Example • Suppose storing an image made up of a square array of 256×256 pixels requires 65,536 bytes. The image is compressed and the compressed version requires 16,384 bytes. • The compression Ratio for the above compression is given by- Compression Ratio= Original Size Compressed Size Compression Ratio= 65536 = 4:1 16384 2 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 16
- 17. • We can also represent the compression ratio by expressing the reduction in the amount of data required as a percentage of the size of the original data. • Total Compression in percentage = Original-Compressed ×100% Original = 65536-16384 ×100% 65536 = 75% • In this particular example, the compression ratio calculated in this manner would be 75%. 2 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 17
- 18. 2. Rate of Compression • Compression performance can also be reported by providing the average number of bits required to represent a single sample. • This is generally referred to as the rate. • For example, in the case of the compressed image described previously, if we assume 8 bits per byte (or pixel), the average number of bits per pixel in the compressed representation is 2. • Thus, we would say that the rate is 2 bits per pixel. 2 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 18
- 19. 3. Distortion • In lossy compression, the reconstruction differs from the original data. • Therefore, in order to determine the efficiency of a compression algorithm, we have to have some way of quantifying the difference. • The difference between the original and the reconstruction is often called the distortion. 2 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 19