Compression methods: the 1 st generation
|
|
|
- Julianna Shelton
- 5 years ago
- Views:
Transcription
1 Compression methods: the 1 st generation Josef Pelikán CGG MFF UK Praha pepca@cgg.mff.cuni.cz Still1g 2017 Josef Pelikán, 1 / 32
2 Basic terminology S x1, x2,... x n Source alphabet: Probability of occurence of the symbol x i is p i Code K of the symbol x i has length d i Message (source string) is sequence: X x, x,... x 1 2 i i i k Entropy (information) of the symbol x i : E i log 2 p i bits Still1g 2017 Josef Pelikán, 2 / 32
3 Basic terminology II Average entropy (information) of a symbol: n AE p E p log p i1 i i n i1 i 2 i bits Message entropy: k E X pi log p j 2 j1 i j bits Length of the message X: L X k d i j1 j bits Still1g 2017 Josef Pelikán, 3 / 32
4 Basic terminology III Code redundancy K for message X: R K L X E X d p log 2 p i i i j1 Average length of a codeword for the code K: Average redundancy of the code K: k n i1 j j j ALK n i1 AR K AL K AE pi di log 2 p p i d i i bits bits bits Still1g 2017 Josef Pelikán, 4 / 32
5 Image compression terminology k, j, k Source sample (pixel value): u u Sample after quantization (reconstructed): u k j k, u, k, j, k Sample prediction: u u k, j, k Prediction error: e e Still1g 2017 Josef Pelikán, 5 / 32
6 Reconstruction quality Mean squared error (MSE): RMS 2 N M 1 ui, j u, MN i1 j1 i j 2 Signal to noise ratio: SNR 10 log10 P 2 RMS 2 db P is range of source values, e.g.: SNR log10 RMS 2 2 db Still1g 2017 Josef Pelikán, 6 / 32
7 Pulse-code modulation (PCM) continuous analog signal digital channel time sampling, quantization, coding (modulation) sample and hold quantizer digital modulation u t R everything is continuous u t R real values in discrete time r t N discrete values in discrete time Still1g 2017 Josef Pelikán, 7 / 32
8 Quantization ti, i 1, 2,... L 1 ti ti 1 Decision values: ri, i 1, 2,... L Reconstruction values: q: t, t r i i 1 i output r 9 r 8 r 7 input r 5 Examples: A-law, m-law t 1 t 2 t 3 t 7 t 8 t 9 t 10 r 4 r 3 r 2 r 1 Still1g 2017 Josef Pelikán, 8 / 32
9 Linear quantizer Best for uniformly distributed source values: t t k 1 q k r t q k 1 k q t L t L t 1 output input q q r 1 dead zone Still1g 2017 Josef Pelikán, 9 / 32
10 Lloyd-Max quantizer Minimizes mean squared error of the quantization: E E u u 2 x u x p x 2 dx t 1 r r k 2 k1 k r xp x dx p x dx k t k1 k1 t k t T t 1 1 p(x).. probability density function of x (discr. histogram) t t k Still1g 2017 Josef Pelikán, 10 / 32
11 Predictive compression neighbour samples are dependent (spatial correlation) pixel values usually change slowly implementation using predictor function previous (neighbour) samples are using to predict recent value only residuals are coded (differences between predicted and actual values) if successful, residuals have smaller amplitude Still1g 2017 Josef Pelikán, 11 / 32
12 Predictive quantization DPCM encoder (Differential Pulse-Code Modulation): u k + - u k e k quantizer predictor & memory u k e k + + encoder k k k u u e ek uk uk prediction: k k1 k2 u P u, u,... Still1g 2017 Josef Pelikán, 12 / 32
13 Predictive quantization II DPCM decoder: decoder e k + u k + predictor & memory u k u k Still1g 2017 Josef Pelikán, 13 / 32
14 Delta modulation quantizer has only two output levels (+q,-q) basically a thresholding circuit prediction is implemented by a delay circuit k k1 k k k1 u u e u u output signal needs not be quantized integrator circuit instead of predictor analog signal coding (audio) Still1g 2017 Josef Pelikán, 14 / 32
15 Delta modulation II DM encoder: u t e t e t LP filter + u t + threshold integrator & delay DM decoder: e integrator t u t LP filtr Still1g 2017 Josef Pelikán, 15 / 32
16 DM flaws u t u t slope overload granularity granularity reduction three-state DM (+q,0,-q) Still1g 2017 Josef Pelikán, 16 / 32
17 DPCM Markov chain (order of p) mean value of the current sample is linearly dependent on p previous samples nonlinear predictors polynomials are more popular 2D and ND predictors match 2D image characteristics (3D in case of video) u p k aj uk j j1 Still1g 2017 Josef Pelikán, 17 / 32
18 2D DPCM B C D A uk aa bb cc dd optimal case: a b d c ac 0 B/W image: DPCM is in theory by cca 20dB better than PCM (ratio : 1) Still1g 2017 Josef Pelikán, 18 / 32
19 Adaptive predictive techniques predictor sets for different correlation directions (horizontal, vertical, oblique,..) algorithm can adapt itselft picks more effective predictor (max correlation) adaptive gain control (quantization error) quality (distortion) of quantization is controlled acording to variance of predictor error image region classification quick adaptation to local pixel neighbourhood block classification e.g. 1616px blocks, ~4 patterns Still1g 2017 Josef Pelikán, 19 / 32
20 Transform methods General scheme: transform quantizer coder T Q C C -1 Q -1 T -1 decoder dequantizer expander inverse transform Still1g 2017 Josef Pelikán, 20 / 32
21 Transform methods II try to process the whole image block (vector) at once can be extended to block quantization reducing maximum amount of redundancy (among components of coded vector) actually used transform function is important transform function 1D (image scanline) 2D (the whole image, blocks KK) Still1g 2017 Josef Pelikán, 21 / 32
22 Transform function T the most important information should be concentrated in a small number of coefficients throwing away of the rest of coefficients leads to a big compression ratio quantization distortion or dropping coefficients should have as little effect on image quality as possible output coefficients should be little/not dependent computation efficiency: T and T -1 (SW, HW) Still1g 2017 Josef Pelikán, 22 / 32
23 Still1g 2017 Josef Pelikán, 23 / 32 1D linear transform methods A Q 1 Q 2... Q N linear transform N quantizers u u u u N v v v N v v v v v N decorrelation
24 Karhunen-Loeve transform input values (vector components) have the Gaussian distribution covariance matrix (dependency of the components) is known goal: to define transform coder with minimum RMS error and maximum decorrelation: optimal Karhunen-Loeve transform coefficients (v i ) are completely independent big time complexity (covariance matrix eigenvectors) Still1g 2017 Josef Pelikán, 24 / 32
25 Practical transforms suboptimal transforms a little bit worse than optimal KLT better eficiency: fast algorihtms in O(N log N), O(N) goniometric transforms (Fourier, sin, cos, Hartley) complex & real arithmetics, good decorrelation Walsh-type transforms partially constant functions, only additive operations are needed (Hadamard), very fast implementations wavelets (Haar, CDF(9,7),...) good representation of inhomogeneous data, hierarchical Still1g 2017 Josef Pelikán, 25 / 32
26 Zonal coding of coefficients Accuracy according to information value (variance) of coefficients. Example of the bit-allocation: Zonal method (only a fixed region/zone is transferred) Still1g 2017 Josef Pelikán, 26 / 32
27 Threshold coding Only K coefficients with biggest amplitudes are transferred. Example (actual coefficient values): Transferred zone is different block to block (address coding) Still1g 2017 Josef Pelikán, 27 / 32
28 Adaptive transform methods (statistical) analysis of image blocks transform algorithm adaptates to actual data transform function switching (parameter-sets switching) zone adaptation (set of transferred coefficients) quantizer adaptation Still1g 2017 Josef Pelikán, 28 / 32
29 Hybrid methods Transform & predictive approach together DPCM coder T... DPCM DPCM coder coder MUX thrown-away coefficients transform Still1g 2017 Josef Pelikán, 29 / 32
30 Interpolation methods only some pixels/samples are coded omitted samples are reconstructed by interpolation linear interpolation (higher-order polynomials are not much better) static methods set of encoded samples is constant dynamic methods adaptation to image characteristics (e.g. variance) Still1g 2017 Josef Pelikán, 30 / 32
31 Alternating interpolation Half of the pixels is encoded, adaptive linear interpolation is used for the rest: B A X C D X A C nebo B or D 2 2 (the one with less diference) Still1g 2017 Josef Pelikán, 31 / 32
32 The End More information: A. Jain: Image Data Compression: A Review, Proceedings of the IEEE, vol.69, #3, 1981 A. Jain: Fundamentals of Digital Image Processing, Prentice-Hall, 1989 ed. by H.-M. Hang, J. Woods: Handbook of Visual Communications, Academic Press, San Diego, 1995 Still1g 2017 Josef Pelikán, 32 / 32
Pulse-Code Modulation (PCM) :
PCM & DPCM & DM 1 Pulse-Code Modulation (PCM) : In PCM each sample of the signal is quantized to one of the amplitude levels, where B is the number of bits used to represent each sample. The rate from
Basic Principles of Video Coding
Basic Principles of Video Coding Introduction Categories of Video Coding Schemes Information Theory Overview of Video Coding Techniques Predictive coding Transform coding Quantization Entropy coding Motion
Multimedia Networking ECE 599
Multimedia Networking ECE 599 Prof. Thinh Nguyen School of Electrical Engineering and Computer Science Based on lectures from B. Lee, B. Girod, and A. Mukherjee 1 Outline Digital Signal Representation
Waveform-Based Coding: Outline
Waveform-Based Coding: Transform and Predictive Coding Yao Wang Polytechnic University, Brooklyn, NY11201 http://eeweb.poly.edu/~yao Based on: Y. Wang, J. Ostermann, and Y.-Q. Zhang, Video Processing and
Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur
Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Lesson 7 Delta Modulation and DPCM Instructional Objectives At the end of this lesson, the students should be able to: 1. Describe a lossy predictive coding scheme.
Objectives of Image Coding
Objectives of Image Coding Representation of an image with acceptable quality, using as small a number of bits as possible Applications: Reduction of channel bandwidth for image transmission Reduction
Digital Image Processing Lectures 25 & 26
Lectures 25 & 26, Professor Department of Electrical and Computer Engineering Colorado State University Spring 2015 Area 4: Image Encoding and Compression Goal: To exploit the redundancies in the image
Image Data Compression
Image Data Compression Image data compression is important for - image archiving e.g. satellite data - image transmission e.g. web data - multimedia applications e.g. desk-top editing Image data compression
Transform coding - topics. Principle of block-wise transform coding
Transform coding - topics Principle of block-wise transform coding Properties of orthonormal transforms Discrete cosine transform (DCT) Bit allocation for transform Threshold coding Typical coding artifacts
Transform Coding. Transform Coding Principle
Transform Coding Principle of block-wise transform coding Properties of orthonormal transforms Discrete cosine transform (DCT) Bit allocation for transform coefficients Entropy coding of transform coefficients
EE 5345 Biomedical Instrumentation Lecture 12: slides
EE 5345 Biomedical Instrumentation Lecture 1: slides 4-6 Carlos E. Davila, Electrical Engineering Dept. Southern Methodist University slides can be viewed at: http:// www.seas.smu.edu/~cd/ee5345.html EE
Lecture 7 Predictive Coding & Quantization
Shujun LI (李树钧): INF-10845-20091 Multimedia Coding Lecture 7 Predictive Coding & Quantization June 3, 2009 Outline Predictive Coding Motion Estimation and Compensation Context-Based Coding Quantization
L. Yaroslavsky. Fundamentals of Digital Image Processing. Course
L. Yaroslavsky. Fundamentals of Digital Image Processing. Course 0555.330 Lec. 6. Principles of image coding The term image coding or image compression refers to processing image digital data aimed at
Review of Quantization. Quantization. Bring in Probability Distribution. L-level Quantization. Uniform partition
Review of Quantization UMCP ENEE631 Slides (created by M.Wu 004) Quantization UMCP ENEE631 Slides (created by M.Wu 001/004) L-level Quantization Minimize errors for this lossy process What L values to
SYDE 575: Introduction to Image Processing. Image Compression Part 2: Variable-rate compression
SYDE 575: Introduction to Image Processing Image Compression Part 2: Variable-rate compression Variable-rate Compression: Transform-based compression As mentioned earlier, we wish to transform image data
Multimedia Communications. Differential Coding
Multimedia Communications Differential Coding Differential Coding In many sources, the source output does not change a great deal from one sample to the next. This means that both the dynamic range and
Predictive Coding. Prediction Prediction in Images
Prediction Prediction in Images Predictive Coding Principle of Differential Pulse Code Modulation (DPCM) DPCM and entropy-constrained scalar quantization DPCM and transmission errors Adaptive intra-interframe
BASIC COMPRESSION TECHNIQUES
BASIC COMPRESSION TECHNIQUES N. C. State University CSC557 Multimedia Computing and Networking Fall 2001 Lectures # 05 Questions / Problems / Announcements? 2 Matlab demo of DFT Low-pass windowed-sinc
Predictive Coding. Prediction
Predictive Coding Prediction Prediction in Images Principle of Differential Pulse Code Modulation (DPCM) DPCM and entropy-constrained scalar quantization DPCM and transmission errors Adaptive intra-interframe
BASICS OF COMPRESSION THEORY
BASICS OF COMPRESSION THEORY Why Compression? Task: storage and transport of multimedia information. E.g.: non-interlaced HDTV: 0x0x0x = Mb/s!! Solutions: Develop technologies for higher bandwidth Find
Principles of Communications
Principles of Communications Weiyao Lin, PhD Shanghai Jiao Tong University Chapter 4: Analog-to-Digital Conversion Textbook: 7.1 7.4 2010/2011 Meixia Tao @ SJTU 1 Outline Analog signal Sampling Quantization
encoding without prediction) (Server) Quantization: Initial Data 0, 1, 2, Quantized Data 0, 1, 2, 3, 4, 8, 16, 32, 64, 128, 256
General Models for Compression / Decompression -they apply to symbols data, text, and to image but not video 1. Simplest model (Lossless ( encoding without prediction) (server) Signal Encode Transmit (client)
CODING SAMPLE DIFFERENCES ATTEMPT 1: NAIVE DIFFERENTIAL CODING
5 0 DPCM (Differential Pulse Code Modulation) Making scalar quantization work for a correlated source -- a sequential approach. Consider quantizing a slowly varying source (AR, Gauss, ρ =.95, σ 2 = 3.2).
Digital communication system. Shannon s separation principle
Digital communication system Representation of the source signal by a stream of (binary) symbols Adaptation to the properties of the transmission channel information source source coder channel coder modulation
Quantization 2.1 QUANTIZATION AND THE SOURCE ENCODER
2 Quantization After the introduction to image and video compression presented in Chapter 1, we now address several fundamental aspects of image and video compression in the remaining chapters of Section
Lossless Image and Intra-frame Compression with Integer-to-Integer DST
1 Lossless Image and Intra-frame Compression with Integer-to-Integer DST Fatih Kamisli, Member, IEEE arxiv:1708.07154v1 [cs.mm] 3 Aug 017 Abstract Video coding standards are primarily designed for efficient
IMAGE COMPRESSION-II. Week IX. 03/6/2003 Image Compression-II 1
IMAGE COMPRESSION-II Week IX 3/6/23 Image Compression-II 1 IMAGE COMPRESSION Data redundancy Self-information and Entropy Error-free and lossy compression Huffman coding Predictive coding Transform coding
Scalar and Vector Quantization. National Chiao Tung University Chun-Jen Tsai 11/06/2014
Scalar and Vector Quantization National Chiao Tung University Chun-Jen Tsai 11/06/014 Basic Concept of Quantization Quantization is the process of representing a large, possibly infinite, set of values
Lecture 2: Introduction to Audio, Video & Image Coding Techniques (I) -- Fundaments
Lecture 2: Introduction to Audio, Video & Image Coding Techniques (I) -- Fundaments Dr. Jian Zhang Conjoint Associate Professor NICTA & CSE UNSW COMP9519 Multimedia Systems S2 2006 jzhang@cse.unsw.edu.au
SUBOPTIMALITY OF THE KARHUNEN-LOÈVE TRANSFORM FOR FIXED-RATE TRANSFORM CODING. Kenneth Zeger
SUBOPTIMALITY OF THE KARHUNEN-LOÈVE TRANSFORM FOR FIXED-RATE TRANSFORM CODING Kenneth Zeger University of California, San Diego, Department of ECE La Jolla, CA 92093-0407 USA ABSTRACT An open problem in
E303: Communication Systems
E303: Communication Systems Professor A. Manikas Chair of Communications and Array Processing Imperial College London Principles of PCM Prof. A. Manikas (Imperial College) E303: Principles of PCM v.17
EE 121: Introduction to Digital Communication Systems. 1. Consider the following discrete-time communication system. There are two equallly likely
EE 11: Introduction to Digital Communication Systems Midterm Solutions 1. Consider the following discrete-time communication system. There are two equallly likely messages to be transmitted, and they are
Lecture 2: Introduction to Audio, Video & Image Coding Techniques (I) -- Fundaments. Tutorial 1. Acknowledgement and References for lectures 1 to 5
Lecture : Introduction to Audio, Video & Image Coding Techniques (I) -- Fundaments Dr. Jian Zhang Conjoint Associate Professor NICTA & CSE UNSW COMP959 Multimedia Systems S 006 jzhang@cse.unsw.edu.au Acknowledgement
Vector Quantization Encoder Decoder Original Form image Minimize distortion Table Channel Image Vectors Look-up (X, X i ) X may be a block of l
Vector Quantization Encoder Decoder Original Image Form image Vectors X Minimize distortion k k Table X^ k Channel d(x, X^ Look-up i ) X may be a block of l m image or X=( r, g, b ), or a block of DCT
Overview. Analog capturing device (camera, microphone) PCM encoded or raw signal ( wav, bmp, ) A/D CONVERTER. Compressed bit stream (mp3, jpg, )
Overview Analog capturing device (camera, microphone) Sampling Fine Quantization A/D CONVERTER PCM encoded or raw signal ( wav, bmp, ) Transform Quantizer VLC encoding Compressed bit stream (mp3, jpg,
Image and Multidimensional Signal Processing
Image and Multidimensional Signal Processing Professor William Hoff Dept of Electrical Engineering &Computer Science http://inside.mines.edu/~whoff/ Image Compression 2 Image Compression Goal: Reduce amount
STATISTICS FOR EFFICIENT LINEAR AND NON-LINEAR PICTURE ENCODING
STATISTICS FOR EFFICIENT LINEAR AND NON-LINEAR PICTURE ENCODING Item Type text; Proceedings Authors Kummerow, Thomas Publisher International Foundation for Telemetering Journal International Telemetering
Information and Entropy
Information and Entropy Shannon s Separation Principle Source Coding Principles Entropy Variable Length Codes Huffman Codes Joint Sources Arithmetic Codes Adaptive Codes Thomas Wiegand: Digital Image Communication
Introduction p. 1 Compression Techniques p. 3 Lossless Compression p. 4 Lossy Compression p. 5 Measures of Performance p. 5 Modeling and Coding p.
Preface p. xvii Introduction p. 1 Compression Techniques p. 3 Lossless Compression p. 4 Lossy Compression p. 5 Measures of Performance p. 5 Modeling and Coding p. 6 Summary p. 10 Projects and Problems
Image Compression. Fundamentals: Coding redundancy. The gray level histogram of an image can reveal a great deal of information about the image
Fundamentals: Coding redundancy The gray level histogram of an image can reveal a great deal of information about the image That probability (frequency) of occurrence of gray level r k is p(r k ), p n
at Some sort of quantization is necessary to represent continuous signals in digital form
Quantization at Some sort of quantization is necessary to represent continuous signals in digital form x(n 1,n ) x(t 1,tt ) D Sampler Quantizer x q (n 1,nn ) Digitizer (A/D) Quantization is also used for
Gaussian source Assumptions d = (x-y) 2, given D, find lower bound of I(X;Y)
Gaussian source Assumptions d = (x-y) 2, given D, find lower bound of I(X;Y) E{(X-Y) 2 } D
EE67I Multimedia Communication Systems
EE67I Multimedia Communication Systems Lecture 5: LOSSY COMPRESSION In these schemes, we tradeoff error for bitrate leading to distortion. Lossy compression represents a close approximation of an original
ON SCALABLE CODING OF HIDDEN MARKOV SOURCES. Mehdi Salehifar, Tejaswi Nanjundaswamy, and Kenneth Rose
ON SCALABLE CODING OF HIDDEN MARKOV SOURCES Mehdi Salehifar, Tejaswi Nanjundaswamy, and Kenneth Rose Department of Electrical and Computer Engineering University of California, Santa Barbara, CA, 93106
Source Coding for Compression
Source Coding for Compression Types of data compression: 1. Lossless -. Lossy removes redundancies (reversible) removes less important information (irreversible) Lec 16b.6-1 M1 Lossless Entropy Coding,
UNIT I INFORMATION THEORY. I k log 2
UNIT I INFORMATION THEORY Claude Shannon 1916-2001 Creator of Information Theory, lays the foundation for implementing logic in digital circuits as part of his Masters Thesis! (1939) and published a paper
IMAGE COMPRESSION IMAGE COMPRESSION-II. Coding Redundancy (contd.) Data Redundancy. Predictive coding. General Model
IMAGE COMRESSIO IMAGE COMRESSIO-II Data redundancy Self-information and Entropy Error-free and lossy compression Huffman coding redictive coding Transform coding Week IX 3/6/23 Image Compression-II 3/6/23
Intraframe Prediction with Intraframe Update Step for Motion-Compensated Lifted Wavelet Video Coding
Intraframe Prediction with Intraframe Update Step for Motion-Compensated Lifted Wavelet Video Coding Aditya Mavlankar, Chuo-Ling Chang, and Bernd Girod Information Systems Laboratory, Department of Electrical
Laboratory 1 Discrete Cosine Transform and Karhunen-Loeve Transform
Laboratory Discrete Cosine Transform and Karhunen-Loeve Transform Miaohui Wang, ID 55006952 Electronic Engineering, CUHK, Shatin, HK Oct. 26, 202 Objective, To investigate the usage of transform in visual
Audio Coding. Fundamentals Quantization Waveform Coding Subband Coding P NCTU/CSIE DSPLAB C.M..LIU
Audio Coding P.1 Fundamentals Quantization Waveform Coding Subband Coding 1. Fundamentals P.2 Introduction Data Redundancy Coding Redundancy Spatial/Temporal Redundancy Perceptual Redundancy Compression
ECE533 Digital Image Processing. Embedded Zerotree Wavelet Image Codec
University of Wisconsin Madison Electrical Computer Engineering ECE533 Digital Image Processing Embedded Zerotree Wavelet Image Codec Team members Hongyu Sun Yi Zhang December 12, 2003 Table of Contents
C.M. Liu Perceptual Signal Processing Lab College of Computer Science National Chiao-Tung University
Quantization C.M. Liu Perceptual Signal Processing Lab College of Computer Science National Chiao-Tung University http://www.csie.nctu.edu.tw/~cmliu/courses/compression/ Office: EC538 (03)5731877 cmliu@cs.nctu.edu.tw
Selective Use Of Multiple Entropy Models In Audio Coding
Selective Use Of Multiple Entropy Models In Audio Coding Sanjeev Mehrotra, Wei-ge Chen Microsoft Corporation One Microsoft Way, Redmond, WA 98052 {sanjeevm,wchen}@microsoft.com Abstract The use of multiple
Run-length & Entropy Coding. Redundancy Removal. Sampling. Quantization. Perform inverse operations at the receiver EEE
General e Image Coder Structure Motion Video x(s 1,s 2,t) or x(s 1,s 2 ) Natural Image Sampling A form of data compression; usually lossless, but can be lossy Redundancy Removal Lossless compression: predictive
Image Compression using DPCM with LMS Algorithm
Image Compression using DPCM with LMS Algorithm Reenu Sharma, Abhay Khedkar SRCEM, Banmore -----------------------------------------------------------------****---------------------------------------------------------------
Source Coding: Part I of Fundamentals of Source and Video Coding
Foundations and Trends R in sample Vol. 1, No 1 (2011) 1 217 c 2011 Thomas Wiegand and Heiko Schwarz DOI: xxxxxx Source Coding: Part I of Fundamentals of Source and Video Coding Thomas Wiegand 1 and Heiko
3 rd Generation Approach to Video Compression for Multimedia
3 rd Generation Approach to Video Compression for Multimedia Pavel Hanzlík, Petr Páta Dept. of Radioelectronics, Czech Technical University in Prague, Technická 2, 166 27, Praha 6, Czech Republic Hanzlip@feld.cvut.cz,
Rate-Distortion Based Temporal Filtering for. Video Compression. Beckman Institute, 405 N. Mathews Ave., Urbana, IL 61801
Rate-Distortion Based Temporal Filtering for Video Compression Onur G. Guleryuz?, Michael T. Orchard y? University of Illinois at Urbana-Champaign Beckman Institute, 45 N. Mathews Ave., Urbana, IL 68 y
4. Quantization and Data Compression. ECE 302 Spring 2012 Purdue University, School of ECE Prof. Ilya Pollak
4. Quantization and Data Compression ECE 32 Spring 22 Purdue University, School of ECE Prof. What is data compression? Reducing the file size without compromising the quality of the data stored in the
Chapter 10 Applications in Communications
Chapter 10 Applications in Communications School of Information Science and Engineering, SDU. 1/ 47 Introduction Some methods for digitizing analog waveforms: Pulse-code modulation (PCM) Differential PCM
EE5356 Digital Image Processing. Final Exam. 5/11/06 Thursday 1 1 :00 AM-1 :00 PM
EE5356 Digital Image Processing Final Exam 5/11/06 Thursday 1 1 :00 AM-1 :00 PM I), Closed books and closed notes. 2), Problems carry weights as indicated. 3), Please print your name and last four digits
CS578- Speech Signal Processing
CS578- Speech Signal Processing Lecture 7: Speech Coding Yannis Stylianou University of Crete, Computer Science Dept., Multimedia Informatics Lab yannis@csd.uoc.gr Univ. of Crete Outline 1 Introduction
SIGNAL COMPRESSION. 8. Lossy image compression: Principle of embedding
SIGNAL COMPRESSION 8. Lossy image compression: Principle of embedding 8.1 Lossy compression 8.2 Embedded Zerotree Coder 161 8.1 Lossy compression - many degrees of freedom and many viewpoints The fundamental
Multimedia Systems Giorgio Leonardi A.A Lecture 4 -> 6 : Quantization
Multimedia Systems Giorgio Leonardi A.A.2014-2015 Lecture 4 -> 6 : Quantization Overview Course page (D.I.R.): https://disit.dir.unipmn.it/course/view.php?id=639 Consulting: Office hours by appointment:
Contents. Acknowledgments
Table of Preface Acknowledgments Notation page xii xx xxi 1 Signals and systems 1 1.1 Continuous and discrete signals 1 1.2 Unit step and nascent delta functions 4 1.3 Relationship between complex exponentials
Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur
Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS Lesson 5 Other Coding Techniques Instructional Objectives At the end of this lesson, the students should be able to:. Convert a gray-scale image into bit-plane
Multimedia Communications. Scalar Quantization
Multimedia Communications Scalar Quantization Scalar Quantization In many lossy compression applications we want to represent source outputs using a small number of code words. Process of representing
Wavelet Scalable Video Codec Part 1: image compression by JPEG2000
1 Wavelet Scalable Video Codec Part 1: image compression by JPEG2000 Aline Roumy aline.roumy@inria.fr May 2011 2 Motivation for Video Compression Digital video studio standard ITU-R Rec. 601 Y luminance
On Optimal Coding of Hidden Markov Sources
2014 Data Compression Conference On Optimal Coding of Hidden Markov Sources Mehdi Salehifar, Emrah Akyol, Kumar Viswanatha, and Kenneth Rose Department of Electrical and Computer Engineering University
Rate-Constrained Multihypothesis Prediction for Motion-Compensated Video Compression
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL 12, NO 11, NOVEMBER 2002 957 Rate-Constrained Multihypothesis Prediction for Motion-Compensated Video Compression Markus Flierl, Student
A Systematic Description of Source Significance Information
A Systematic Description of Source Significance Information Norbert Goertz Institute for Digital Communications School of Engineering and Electronics The University of Edinburgh Mayfield Rd., Edinburgh
On Compression Encrypted Data part 2. Prof. Ja-Ling Wu The Graduate Institute of Networking and Multimedia National Taiwan University
On Compression Encrypted Data part 2 Prof. Ja-Ling Wu The Graduate Institute of Networking and Multimedia National Taiwan University 1 Brief Summary of Information-theoretic Prescription At a functional
IMAGE COMPRESSION OF DIGITIZED NDE X-RAY RADIOGRAPHS. Brian K. LoveweIl and John P. Basart
IMAGE COMPRESSIO OF DIGITIZED DE X-RAY RADIOGRAPHS BY ADAPTIVE DIFFERETIAL PULSE CODE MODULATIO Brian K. LoveweIl and John P. Basart Center for ondestructive Evaluation and the Department of Electrical
Physical Layer and Coding
Physical Layer and Coding Muriel Médard Professor EECS Overview A variety of physical media: copper, free space, optical fiber Unified way of addressing signals at the input and the output of these media:
Quantization. Introduction. Roadmap. Optimal Quantizer Uniform Quantizer Non Uniform Quantizer Rate Distorsion Theory. Source coding.
Roadmap Quantization Optimal Quantizer Uniform Quantizer Non Uniform Quantizer Rate Distorsion Theory Source coding 2 Introduction 4 1 Lossy coding Original source is discrete Lossless coding: bit rate
Lloyd-Max Quantization of Correlated Processes: How to Obtain Gains by Receiver-Sided Time-Variant Codebooks
Lloyd-Max Quantization of Correlated Processes: How to Obtain Gains by Receiver-Sided Time-Variant Codebooks Sai Han and Tim Fingscheidt Institute for Communications Technology, Technische Universität
ELECTRONICS & COMMUNICATIONS DIGITAL COMMUNICATIONS
EC 32 (CR) Total No. of Questions :09] [Total No. of Pages : 02 III/IV B.Tech. DEGREE EXAMINATIONS, APRIL/MAY- 207 Second Semester ELECTRONICS & COMMUNICATIONS DIGITAL COMMUNICATIONS Time: Three Hours
SCALABLE AUDIO CODING USING WATERMARKING
SCALABLE AUDIO CODING USING WATERMARKING Mahmood Movassagh Peter Kabal Department of Electrical and Computer Engineering McGill University, Montreal, Canada Email: {mahmood.movassagh@mail.mcgill.ca, peter.kabal@mcgill.ca}
Source Coding. Scalar Quantization
Source Coding Source Coding The Communications Toolbox includes some basic functions for source coding. Source coding, also known as quantization or signal formatting, includes the concepts of analog-to-digital
MARKOV CHAINS A finite state Markov chain is a sequence of discrete cv s from a finite alphabet where is a pmf on and for
MARKOV CHAINS A finite state Markov chain is a sequence S 0,S 1,... of discrete cv s from a finite alphabet S where q 0 (s) is a pmf on S 0 and for n 1, Q(s s ) = Pr(S n =s S n 1 =s ) = Pr(S n =s S n 1
Video Coding with Motion Compensation for Groups of Pictures
International Conference on Image Processing 22 Video Coding with Motion Compensation for Groups of Pictures Markus Flierl Telecommunications Laboratory University of Erlangen-Nuremberg mflierl@stanford.edu
Estimation-Theoretic Delayed Decoding of Predictively Encoded Video Sequences
Estimation-Theoretic Delayed Decoding of Predictively Encoded Video Sequences Jingning Han, Vinay Melkote, and Kenneth Rose Department of Electrical and Computer Engineering University of California, Santa
The Secrets of Quantization. Nimrod Peleg Update: Sept. 2009
The Secrets of Quantization Nimrod Peleg Update: Sept. 2009 What is Quantization Representation of a large set of elements with a much smaller set is called quantization. The number of elements in the
Compression. Encryption. Decryption. Decompression. Presentation of Information to client site
DOCUMENT Anup Basu Audio Image Video Data Graphics Objectives Compression Encryption Network Communications Decryption Decompression Client site Presentation of Information to client site Multimedia -
Chapter 3 Current SAR Data Compression Techniques 3.1 Introduction
Chapter 3 Current SAR Data Compression Techniques 3.1 Introduction Data compression is the reduction in the number of bits required to represent the data. It is motivated by physical limitations: (i) limited
SCELP: LOW DELAY AUDIO CODING WITH NOISE SHAPING BASED ON SPHERICAL VECTOR QUANTIZATION
SCELP: LOW DELAY AUDIO CODING WITH NOISE SHAPING BASED ON SPHERICAL VECTOR QUANTIZATION Hauke Krüger and Peter Vary Institute of Communication Systems and Data Processing RWTH Aachen University, Templergraben
Digital Signal Processing 2/ Advanced Digital Signal Processing Lecture 3, SNR, non-linear Quantisation Gerald Schuller, TU Ilmenau
Digital Signal Processing 2/ Advanced Digital Signal Processing Lecture 3, SNR, non-linear Quantisation Gerald Schuller, TU Ilmenau What is our SNR if we have a sinusoidal signal? What is its pdf? Basically
PCM Reference Chapter 12.1, Communication Systems, Carlson. PCM.1
PCM Reference Chapter 1.1, Communication Systems, Carlson. PCM.1 Pulse-code modulation (PCM) Pulse modulations use discrete time samples of analog signals the transmission is composed of analog information
Audio /Video Signal Processing. Lecture 2, Quantization, SNR Gerald Schuller, TU Ilmenau
Audio /Video Signal Processing Lecture 2, Quantization, SNR Gerald Schuller, TU Ilmenau Quantization Signal to Noise Ratio (SNR). Assume we have a A/D converter with a quantizer with a certain number of
EE368B Image and Video Compression
EE368B Image and Video Compression Homework Set #2 due Friday, October 20, 2000, 9 a.m. Introduction The Lloyd-Max quantizer is a scalar quantizer which can be seen as a special case of a vector quantizer
THE dictionary (Random House) definition of quantization
IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 44, NO. 6, OCTOBER 1998 2325 Quantization Robert M. Gray, Fellow, IEEE, and David L. Neuhoff, Fellow, IEEE (Invited Paper) Abstract The history of the theory
Intensity Transformations and Spatial Filtering: WHICH ONE LOOKS BETTER? Intensity Transformations and Spatial Filtering: WHICH ONE LOOKS BETTER?
: WHICH ONE LOOKS BETTER? 3.1 : WHICH ONE LOOKS BETTER? 3.2 1 Goal: Image enhancement seeks to improve the visual appearance of an image, or convert it to a form suited for analysis by a human or a machine.
Communication Engineering Prof. Surendra Prasad Department of Electrical Engineering Indian Institute of Technology, Delhi
Communication Engineering Prof. Surendra Prasad Department of Electrical Engineering Indian Institute of Technology, Delhi Lecture - 41 Pulse Code Modulation (PCM) So, if you remember we have been talking
EE5356 Digital Image Processing
EE5356 Digital Image Processing INSTRUCTOR: Dr KR Rao Spring 007, Final Thursday, 10 April 007 11:00 AM 1:00 PM ( hours) (Room 111 NH) INSTRUCTIONS: 1 Closed books and closed notes All problems carry weights
CS6304 / Analog and Digital Communication UNIT IV - SOURCE AND ERROR CONTROL CODING PART A 1. What is the use of error control coding? The main use of error control coding is to reduce the overall probability
+ (50% contribution by each member)
Image Coding using EZW and QM coder ECE 533 Project Report Ahuja, Alok + Singh, Aarti + + (50% contribution by each member) Abstract This project involves Matlab implementation of the Embedded Zerotree
Linear, Worst-Case Estimators for Denoising Quantization Noise in Transform Coded Images
Linear, Worst-Case Estimators for Denoising Quantization Noise in Transform Coded Images 1 Onur G. Guleryuz DoCoMo Communications Laboratories USA, Inc. 181 Metro Drive, Suite 3, San Jose, CA 91 guleryuz@docomolabs-usa.com,
Half-Pel Accurate Motion-Compensated Orthogonal Video Transforms
Flierl and Girod: Half-Pel Accurate Motion-Compensated Orthogonal Video Transforms, IEEE DCC, Mar. 007. Half-Pel Accurate Motion-Compensated Orthogonal Video Transforms Markus Flierl and Bernd Girod Max
sine wave fit algorithm
TECHNICAL REPORT IR-S3-SB-9 1 Properties of the IEEE-STD-57 four parameter sine wave fit algorithm Peter Händel, Senior Member, IEEE Abstract The IEEE Standard 57 (IEEE-STD-57) provides algorithms for
Multimedia Communications. Mathematical Preliminaries for Lossless Compression
Multimedia Communications Mathematical Preliminaries for Lossless Compression What we will see in this chapter Definition of information and entropy Modeling a data source Definition of coding and when