# Redundant Data Elimination for Image Compression and Internet Transmission using MATLAB

Save this PDF as:

Size: px
Start display at page:

Download "Redundant Data Elimination for Image Compression and Internet Transmission using MATLAB"

## Transcription

1 Redundant Data Elimination for Image Compression and Internet Transmission using MATLAB R. Challoo, I.P. Thota, and L. Challoo Texas A&M University-Kingsville Kingsville, Texas , U.S.A. ABSTRACT Data collection, representation, and transmission are important topics in science and engineering education. Data is a mean to convey information. The main objective of this paper is to minimize the number of bits required to represent an image for internet transmission and improve the accuracy of discrete interpolation using the discrete cosine transform to map input data sequence that satisfies the boundary conditions. The critical boundary conditions are set and mapping for the input data is developed. A typical example is simulated to show the efficiency of the proposed algorithm. The data compression scheme was simulated using MATLAB. This paper can help the reader in understanding image compression using Discrete Cosine Transform. Redundant data elimination and high data compression rate can result in high Internet transmission rate. Keywords: Image Compression, Data Transmission, MATLAB 1. INTRODUCTION An image can be represented with many pixels along with some redundant data. Elimination of redundant data reduces the number of bits used to represent the data. In some cases repetition of data contains less important information which can be eliminated and the image can be represented with less number of bits with greater efficiency. Here, we take advantage of Discrete Cosine Transform (DCT) to get a better quality image rather than using Discrete Fourier Transform (DFT) for image compression and we remove redundant data to increase storage capacity and transmission rate. 2. IMPLEMENTATION OF DISCRETE COSINE TRANSFORM A. The Process Steps The following is a general overview of the JPEG process. The picture is represented as 8x8 blocks of pixels. From left to right, top to bottom, the DCT is applied to every block and the D matrix is calculated. Every block is compressed using method of quantization. The group of compressed blocks that comprise the image is saved in a significantly decreased space. The image is recovered through a process of decompression when required, a method that utilizes the Inverse Discrete Cosine Transform (IDCT). B. Quantization Our 8x8 block of DCT coefficients is now set for compression by the process of quantization. Extremely useful and remarkable characteristics of the process of JPEG are that in this process, different stages of compression of picture and quality are available by selection of specific quantization matrices. It allows the user to decide on quality levels in borders from 1 to 100, where 1 deals the poorest quality of picture and the highest compression, while 100 gives the best quality and the lowest compression [5]. Individual examinations concerning the human being visual scheme have resulted in the typical quantization matrix JPEG. With a quality level of 50, this matrix renders both high compression and also excels in quality of the decompressed picture.

2 First we take an image which can be represented with 8x8 elements and the total of 64 pixels. This is a gray scale image and the pixel values for black and white picture range from -127 to 128. Subtract 128 from all the pixel values and perform DCT on that image. Quantization can be performed by dividing each and every individual pixel value in matrix D which is also known as transformed image matrix by the corresponding pixel values of the quantization matrix and rounded off to their nearest whole number. For our example quantization level 50 matrix is used: C i, j = (rounding off) D i, j / Q i, j To compress the image we use quantization. This quantization process is performed on the resultant matrix after the DCT is applied. We can choose the quality levels for this process. Quality level 50 is used for our compression which gives great quality image with high compression. In the final resultant matrix, zero pixel values indicate lower frequencies and they are of less importance, whereas, non-zero pixel values indicate higher frequencies and are more important in reconstructing the original image. To implement DCT practically, we chose 10 random images. We first created the basis: a) 3D plot with basic resolution (64 plots of 8x8 pixels) using "surf" function. b) 3D plot with x20 resolution (64 plots of 160x160 pixels) using "mesh" function. c) 2D plot with x10 resolution (64 plots of 80x80 pixels) using "mesh" function. d) 2D plot with x10 resolution (64 plots of 80x80 pixels) using "imshow" function. The coefficients located near the upper-left corner represent the lower frequencies to which the human eye is most susceptible of the picture block. The pixel value of zero represents the less important features in the image and the nonzero values represent the highest frequency and also more important features in that image. Only nonzero pixel value elements are used to recover the original image. 3. METHODOLOGY In this paper we compress the images using DCT and reconstruct the original image from the compressed data using IDCT. Then we performed DCT on these ten images. We have also shown the power of DCT coefficients which shows that the power of the coefficients decreases from top to bottom. Signal-to-Noise Ratio (SNR) graph is also plotted for image number 8. Also, a pseudo color graph was plotted for the penny image using three dimensional image which looks like the original penny image. 4. RESULTS AND DISCUSSIONS A. Details of the algorithm: A basic JPEG compression (gray level image): 1) Take an image (two dimensional matrix) and divide it to 8x8 matrices

3 2) For every matrix (8x8) utilize the DCT conversion (from the signal processing toolbox). An (8x8) matrix is obtained. 3) Build an 8x8 matrix, which is the total of all the matrices, such that sum_matrix = A + B + C ) Sort rudiments of the 8x8 matrix from the maximum to the minimum and get the indices listing. 5) Sum the last matrix with part of the elements which have the higher coefficients, until you have a sufficient ratio (let s say 80%). [7] For example: a) 3D plot with basic resolution (64 plots of 8x8 pixels) using "surf" function. idx = sort (sum_matrix (:)); part_of_energy=sum_matrix (idx(1:20)); all_energy = sum_matrix (:); Ratio = part_of_energy/all_energy; 6) Save the partial list of index, number of matrices (rows, ) and from every matrix from 2 nd step save only these coefficients B. For reconstruction: 1) Build matrices of 2 nd step by the zero command: A = zero(8,8); B = zero(8,8);... b) 3D plot with x20 resolution (64 plots of 160x160 pixels) using "mesh" function 2) In every matrix, save the coefficients in the correct places, so that the new matrix A is equivalent to the matrix A from 2 nd step of the encoding. 3) For each matrix perform IDCT. 4) Compose these inverse-transformed matrices back into the big matrix which is the reconstructed image. Usually, you will have most of the energy inside the upper-left coefficient (1, 1) which corresponds to the DC (or average value) of the whole picture. c) 2D plot with x10 resolution (64 plots of 80x80 pixels) using "mesh" function

4 d) 2D plot with x10 resolution (64 plots of 80x80 pixels) using "imshow" function. A 2 dimensional DCT on 8x8 block was done for each picture from 0 to 9 and the DCT was saved inside a cell of the size: 10 cells of 128x128 matrix. DCT 8x8 block transform for each picture is shown. Power of DCT coefficients Statistics on the cell array of the DCT transforms was performed. A matrix of 8x8 which will describe the value of each "DCT-base" over the transform of the 10 given pictures was created. Since some of the values were negative, and we were interested in the energy of the coefficients, abs()^2 values were

5 added into the matrix. This is consistent with the definition of the "Perceval relation" in Fourier Coefficients. The deviation from the original image is considered to be the noise therefore: Noise= original image - compressed image. The SNR is defined as: SNR=energy_of_image/energy_of_noise Which yields: SNR=energy_of_image/((original_imagecompressed_image) ^2) C. Dimensional Plot We have used this matrix to choose only the wanted number of coefficients. The matrix is initialized to zeros. coef_selection_matrix = zeros (8, 8); [7] Compressed picture set (to show the degrading). Compressed set = [ ]; SNR Graph for picture number 8 The drawings of a conspiracy in pseudo color with the shine proportional to Laplacian of the height. A cell is bright if its height is bigger than the average of his four neighbors and sinks if its height is less than the average of his four neighbors. It is a model of unaccustomed illumination ", but it produces a picture which resembles a photograph of a cent. Finally, a three dimensional copper colored, surface plot with the Laplacian lighting model was produced. 5. CONCLUSIONS calc_snr - calculates the SNR of a figure being compressed. Assumption: SNR calculation is done in the following manner: Image compression is one of the necessities of modern Digital world. For instance, with the explosive growth of the Internet there is a growing need for audio compression or data compression in general. Goals of such compressions are to minimize the storage memory, communication channel bandwidth, and transmission time.

6 Data compression using transformations such as the DCT and the DFT are the basis for many coding standards such as JPEG, MP3 and AC-3. In this paper DCT was used for the compression of an Image/sound signal and the data compression scheme was simulated using MATLAB. 6. REFERENCES [1] sform [2] form [3] Gurjar, M. and Jagannathan, P., Image Compression: Review and Comparison of Haar Wavelet Transform and Vector Quantization. URL: [4] tro_ip.html [5] Smith, S. W., The Scientist and Engineer s Guide to Digital Signal Processing, California Publishing Company, URL: [6] Watson, A. B., Image Compression Using the Discrete Cosine Transform, NASA AMES Research Center. URL: [7] Gonzalez R.C., Woods R.E., and Eddins S.C., Digital Image Processing using MATLAB, 2003.

### JPEG Compression Using MATLAB

JPEG Compression Using MATLAB Anurag, Sonia Rani M.Tech Student, HOD CSE CSE Department, ITS Bhiwani India ABSTRACT Creating, editing, and generating s in a very regular system today is a major priority.

### A NEW ENTROPY ENCODING ALGORITHM FOR IMAGE COMPRESSION USING DCT

A NEW ENTROPY ENCODING ALGORITHM FOR IMAGE COMPRESSION USING DCT D.Malarvizhi 1 Research Scholar Dept of Computer Science & Eng Alagappa University Karaikudi 630 003. Dr.K.Kuppusamy 2 Associate Professor

### IMAGE COMPRESSION. Image Compression. Why? Reducing transportation times Reducing file size. A two way event - compression and decompression

IMAGE COMPRESSION Image Compression Why? Reducing transportation times Reducing file size A two way event - compression and decompression 1 Compression categories Compression = Image coding Still-image

### Multimedia Communications. Transform Coding

Multimedia Communications Transform Coding Transform coding Transform coding: source output is transformed into components that are coded according to their characteristics If a sequence of inputs is transformed

### HYBRID TRANSFORMATION TECHNIQUE FOR IMAGE COMPRESSION

31 st July 01. Vol. 41 No. 005-01 JATIT & LLS. All rights reserved. ISSN: 199-8645 www.jatit.org E-ISSN: 1817-3195 HYBRID TRANSFORMATION TECHNIQUE FOR IMAGE COMPRESSION 1 SRIRAM.B, THIYAGARAJAN.S 1, Student,

### IMAGE COMPRESSION USING HYBRID TRANSFORM TECHNIQUE

Volume 4, No. 1, January 2013 Journal of Global Research in Computer Science RESEARCH PAPER Available Online at www.jgrcs.info IMAGE COMPRESSION USING HYBRID TRANSFORM TECHNIQUE Nikita Bansal *1, Sanjay

### HYBRID IMAGE COMPRESSION TECHNIQUE

HYBRID IMAGE COMPRESSION TECHNIQUE Eranna B A, Vivek Joshi, Sundaresh K Professor K V Nagalakshmi, Dept. of E & C, NIE College, Mysore.. ABSTRACT With the continuing growth of modern communication technologies,

### Topic 5 Image Compression

Topic 5 Image Compression Introduction Data Compression: The process of reducing the amount of data required to represent a given quantity of information. Purpose of Image Compression: the reduction of

### ISSN Vol.06,Issue.10, November-2014, Pages:

ISSN 2348 2370 Vol.06,Issue.10, November-2014, Pages:1169-1173 www.ijatir.org Designing a Image Compression for JPEG Format by Verilog HDL B.MALLESH KUMAR 1, D.V.RAJESHWAR RAJU 2 1 PG Scholar, Dept of

### COLOR IMAGE COMPRESSION USING DISCRETE COSINUS TRANSFORM (DCT)

COLOR IMAGE COMPRESSION USING DISCRETE COSINUS TRANSFORM (DCT) Adietiya R. Saputra Fakultas Ilmu Komputer dan Teknologi Informasi, Universitas Gunadarma Jl. Margonda Raya no. 100, Depok 16424, Jawa Barat

### Priyanka Dixit CSE Department, TRUBA Institute of Engineering & Information Technology, Bhopal, India

An Efficient DCT Compression Technique using Strassen s Matrix Multiplication Algorithm Manish Manoria Professor & Director in CSE Department, TRUBA Institute of Engineering &Information Technology, Bhopal,

### Statistical Image Compression using Fast Fourier Coefficients

Statistical Image Compression using Fast Fourier Coefficients M. Kanaka Reddy Research Scholar Dept.of Statistics Osmania University Hyderabad-500007 V. V. Haragopal Professor Dept.of Statistics Osmania

### Enhancing the Image Compression Rate Using Steganography

The International Journal Of Engineering And Science (IJES) Volume 3 Issue 2 Pages 16-21 2014 ISSN(e): 2319 1813 ISSN(p): 2319 1805 Enhancing the Image Compression Rate Using Steganography 1, Archana Parkhe,

### 7.5 Dictionary-based Coding

7.5 Dictionary-based Coding LZW uses fixed-length code words to represent variable-length strings of symbols/characters that commonly occur together, e.g., words in English text LZW encoder and decoder

### Huffman Coding and Position based Coding Scheme for Image Compression: An Experimental Analysis

Huffman Coding and Position based Coding Scheme for Image Compression: An Experimental Analysis Jayavrinda Vrindavanam Ph D student, Dept of E&C, NIT, Durgapur Saravanan Chandran Asst. Professor Head,

### IMAGE PROCESSING USING DISCRETE WAVELET TRANSFORM

IMAGE PROCESSING USING DISCRETE WAVELET TRANSFORM Prabhjot kour Pursuing M.Tech in vlsi design from Audisankara College of Engineering ABSTRACT The quality and the size of image data is constantly increasing.

### A Image Comparative Study using DCT, Fast Fourier, Wavelet Transforms and Huffman Algorithm

International Journal of Engineering Research and General Science Volume 3, Issue 4, July-August, 15 ISSN 91-2730 A Image Comparative Study using DCT, Fast Fourier, Wavelet Transforms and Huffman Algorithm

DIGITAL IMAGE PROCESSING WRITTEN REPORT ADAPTIVE IMAGE COMPRESSION TECHNIQUES FOR WIRELESS MULTIMEDIA APPLICATIONS SUBMITTED BY: NAVEEN MATHEW FRANCIS #105249595 INTRODUCTION The advent of new technologies

### DIGITAL IMAGE WATERMARKING BASED ON A RELATION BETWEEN SPATIAL AND FREQUENCY DOMAINS

DIGITAL IMAGE WATERMARKING BASED ON A RELATION BETWEEN SPATIAL AND FREQUENCY DOMAINS Murat Furat Mustafa Oral e-mail: mfurat@cu.edu.tr e-mail: moral@mku.edu.tr Cukurova University, Faculty of Engineering,

### Robert Matthew Buckley. Nova Southeastern University. Dr. Laszlo. MCIS625 On Line. Module 2 Graphics File Format Essay

1 Robert Matthew Buckley Nova Southeastern University Dr. Laszlo MCIS625 On Line Module 2 Graphics File Format Essay 2 JPEG COMPRESSION METHOD Joint Photographic Experts Group (JPEG) is the most commonly

### IMAGE COMPRESSION. Chapter - 5 : (Basic)

Chapter - 5 : IMAGE COMPRESSION (Basic) Q() Explain the different types of redundncies that exists in image.? (8M May6 Comp) [8M, MAY 7, ETRX] A common characteristic of most images is that the neighboring

### CHAPTER 6. 6 Huffman Coding Based Image Compression Using Complex Wavelet Transform. 6.3 Wavelet Transform based compression technique 106

CHAPTER 6 6 Huffman Coding Based Image Compression Using Complex Wavelet Transform Page No 6.1 Introduction 103 6.2 Compression Techniques 104 103 6.2.1 Lossless compression 105 6.2.2 Lossy compression

### Lossless Image Compression having Compression Ratio Higher than JPEG

Cloud Computing & Big Data 35 Lossless Image Compression having Compression Ratio Higher than JPEG Madan Singh madan.phdce@gmail.com, Vishal Chaudhary Computer Science and Engineering, Jaipur National

### Digital Image Processing

Digital Image Processing Third Edition Rafael C. Gonzalez University of Tennessee Richard E. Woods MedData Interactive PEARSON Prentice Hall Pearson Education International Contents Preface xv Acknowledgments

### Perceptual Coding. Lossless vs. lossy compression Perceptual models Selecting info to eliminate Quantization and entropy encoding

Perceptual Coding Lossless vs. lossy compression Perceptual models Selecting info to eliminate Quantization and entropy encoding Part II wrap up 6.082 Fall 2006 Perceptual Coding, Slide 1 Lossless vs.

### IMAGING. Images are stored by capturing the binary data using some electronic devices (SENSORS)

IMAGING Film photography Digital photography Images are stored by capturing the binary data using some electronic devices (SENSORS) Sensors: Charge Coupled Device (CCD) Photo multiplier tube (PMT) The

### Practical Image and Video Processing Using MATLAB

Practical Image and Video Processing Using MATLAB Chapter 18 Feature extraction and representation What will we learn? What is feature extraction and why is it a critical step in most computer vision and

### JPEG. Wikipedia: Felis_silvestris_silvestris.jpg, Michael Gäbler CC BY 3.0

JPEG Wikipedia: Felis_silvestris_silvestris.jpg, Michael Gäbler CC BY 3.0 DFT vs. DCT Image Compression Image compression system Input Image MAPPER QUANTIZER SYMBOL ENCODER Compressed output Image Compression

### NOVEL ALGORITHMS FOR FINDING AN OPTIMAL SCANNING PATH FOR JPEG IMAGE COMPRESSION

NOVEL ALGORITHMS FOR FINDING AN OPTIMAL SCANNING PATH FOR JPEG IMAGE COMPRESSION Smila Mohandhas and Sankar. S Student, Computer Science and Engineering, KCG College of Engineering, Chennai. Associate

### In the first part of our project report, published

Editor: Harrick Vin University of Texas at Austin Multimedia Broadcasting over the Internet: Part II Video Compression Borko Furht Florida Atlantic University Raymond Westwater Future Ware Jeffrey Ice

### IMAGE DE-NOISING IN WAVELET DOMAIN

IMAGE DE-NOISING IN WAVELET DOMAIN Aaditya Verma a, Shrey Agarwal a a Department of Civil Engineering, Indian Institute of Technology, Kanpur, India - (aaditya, ashrey)@iitk.ac.in KEY WORDS: Wavelets,

### Image Enhancement: To improve the quality of images

Image Enhancement: To improve the quality of images Examples: Noise reduction (to improve SNR or subjective quality) Change contrast, brightness, color etc. Image smoothing Image sharpening Modify image

### Biometrics Technology: Image Processing & Pattern Recognition (by Dr. Dickson Tong)

Biometrics Technology: Image Processing & Pattern Recognition (by Dr. Dickson Tong) References: [1] http://homepages.inf.ed.ac.uk/rbf/hipr2/index.htm [2] http://www.cs.wisc.edu/~dyer/cs540/notes/vision.html

### Extensions of One-Dimensional Gray-level Nonlinear Image Processing Filters to Three-Dimensional Color Space

Extensions of One-Dimensional Gray-level Nonlinear Image Processing Filters to Three-Dimensional Color Space Orlando HERNANDEZ and Richard KNOWLES Department Electrical and Computer Engineering, The College

### Image Manipulation in MATLAB Due Monday, July 17 at 5:00 PM

Image Manipulation in MATLAB Due Monday, July 17 at 5:00 PM 1 Instructions Labs may be done in groups of 2 or 3 (i.e., not alone). You may use any programming language you wish but MATLAB is highly suggested.

### Compression Part 2 Lossy Image Compression (JPEG) Norm Zeck

Compression Part 2 Lossy Image Compression (JPEG) General Compression Design Elements 2 Application Application Model Encoder Model Decoder Compression Decompression Models observe that the sensors (image

### Denoising and Edge Detection Using Sobelmethod

International OPEN ACCESS Journal Of Modern Engineering Research (IJMER) Denoising and Edge Detection Using Sobelmethod P. Sravya 1, T. Rupa devi 2, M. Janardhana Rao 3, K. Sai Jagadeesh 4, K. Prasanna

### Steganography: Hiding Data In Plain Sight. Ryan Gibson

Steganography: Hiding Data In Plain Sight Ryan Gibson What Is Steganography? The practice of concealing messages or information within other nonsecret text or data. Comes from the Greek words steganos

### A Miniature-Based Image Retrieval System

A Miniature-Based Image Retrieval System Md. Saiful Islam 1 and Md. Haider Ali 2 Institute of Information Technology 1, Dept. of Computer Science and Engineering 2, University of Dhaka 1, 2, Dhaka-1000,

### 11. Image Data Analytics. Jacobs University Visualization and Computer Graphics Lab

11. Image Data Analytics Motivation Images (and even videos) have become a popular data format for storing information digitally. Data Analytics 377 Motivation Traditionally, scientific and medical imaging

### 06/12/2017. Image compression. Image compression. Image compression. Image compression. Coding redundancy: image 1 has four gray levels

Theoretical size of a file representing a 5k x 4k colour photograph: 5000 x 4000 x 3 = 60 MB 1 min of UHD tv movie: 3840 x 2160 x 3 x 24 x 60 = 36 GB 1. Exploit coding redundancy 2. Exploit spatial and

### Audio Compression Using DCT and DWT Techniques

Audio Compression Using DCT and DWT Techniques Jithin James 1, Vinod J Thomas 2 1 PG Scholar, 2 Assistant Professor Department of Electronics and Communication Engineering Vimal Jyothi Engineering College

### Introduction to Wavelets

Lab 11 Introduction to Wavelets Lab Objective: In the context of Fourier analysis, one seeks to represent a function as a sum of sinusoids. A drawback to this approach is that the Fourier transform only

### REVIEW ON IMAGE COMPRESSION TECHNIQUES AND ADVANTAGES OF IMAGE COMPRESSION

REVIEW ON IMAGE COMPRESSION TECHNIQUES AND ABSTRACT ADVANTAGES OF IMAGE COMPRESSION Amanpreet Kaur 1, Dr. Jagroop Singh 2 1 Ph. D Scholar, Deptt. of Computer Applications, IK Gujral Punjab Technical University,

### Comparison of Digital Image Watermarking Algorithms. Xu Zhou Colorado School of Mines December 1, 2014

Comparison of Digital Image Watermarking Algorithms Xu Zhou Colorado School of Mines December 1, 2014 Outlier Introduction Background on digital image watermarking Comparison of several algorithms Experimental

### 15 Data Compression 2014/9/21. Objectives After studying this chapter, the student should be able to: 15-1 LOSSLESS COMPRESSION

15 Data Compression Data compression implies sending or storing a smaller number of bits. Although many methods are used for this purpose, in general these methods can be divided into two broad categories:

### CHAPTER 3 DIFFERENT DOMAINS OF WATERMARKING. domain. In spatial domain the watermark bits directly added to the pixels of the cover

38 CHAPTER 3 DIFFERENT DOMAINS OF WATERMARKING Digital image watermarking can be done in both spatial domain and transform domain. In spatial domain the watermark bits directly added to the pixels of the

### Image Compression. CS 6640 School of Computing University of Utah

Image Compression CS 6640 School of Computing University of Utah Compression What Reduce the amount of information (bits) needed to represent image Why Transmission Storage Preprocessing Redundant & Irrelevant

### CMPT 365 Multimedia Systems. Media Compression - Video

CMPT 365 Multimedia Systems Media Compression - Video Spring 2017 Edited from slides by Dr. Jiangchuan Liu CMPT365 Multimedia Systems 1 Introduction What s video? a time-ordered sequence of frames, i.e.,

### Multimedia Signals and Systems Motion Picture Compression - MPEG

Multimedia Signals and Systems Motion Picture Compression - MPEG Kunio Takaya Electrical and Computer Engineering University of Saskatchewan March 9, 2008 MPEG video coding A simple introduction Dr. S.R.

### Optimized Progressive Coding of Stereo Images Using Discrete Wavelet Transform

Optimized Progressive Coding of Stereo Images Using Discrete Wavelet Transform Torsten Palfner, Alexander Mali and Erika Müller Institute of Telecommunications and Information Technology, University of

### CHAPTER 6 A SECURE FAST 2D-DISCRETE FRACTIONAL FOURIER TRANSFORM BASED MEDICAL IMAGE COMPRESSION USING SPIHT ALGORITHM WITH HUFFMAN ENCODER

115 CHAPTER 6 A SECURE FAST 2D-DISCRETE FRACTIONAL FOURIER TRANSFORM BASED MEDICAL IMAGE COMPRESSION USING SPIHT ALGORITHM WITH HUFFMAN ENCODER 6.1. INTRODUCTION Various transforms like DCT, DFT used to

### Does everyone have an override code?

Does everyone have an override code? Project 1 due Friday 9pm Review of Filtering Filtering in frequency domain Can be faster than filtering in spatial domain (for large filters) Can help understand effect

### ECE 176 Digital Image Processing Handout #14 Pamela Cosman 4/29/05 TEXTURE ANALYSIS

ECE 176 Digital Image Processing Handout #14 Pamela Cosman 4/29/ TEXTURE ANALYSIS Texture analysis is covered very briefly in Gonzalez and Woods, pages 66 671. This handout is intended to supplement that

### Image coding and compression

Image coding and compression Robin Strand Centre for Image Analysis Swedish University of Agricultural Sciences Uppsala University Today Information and Data Redundancy Image Quality Compression Coding

### ANALYSIS OF DIFFERENT DOMAIN WATERMARKING TECHNIQUES

ANALYSIS OF DIFFERENT DOMAIN WATERMARKING TECHNIQUES 1 Maneet, 2 Prabhjot Kaur 1 Assistant Professor, AIMT/ EE Department, Indri-Karnal, India Email: maneetkaur122@gmail.com 2 Assistant Professor, AIMT/

### compression and coding ii

compression and coding ii Ole-Johan Skrede 03.05.2017 INF2310 - Digital Image Processing Department of Informatics The Faculty of Mathematics and Natural Sciences University of Oslo After original slides

### CS 335 Graphics and Multimedia. Image Compression

CS 335 Graphics and Multimedia Image Compression CCITT Image Storage and Compression Group 3: Huffman-type encoding for binary (bilevel) data: FAX Group 4: Entropy encoding without error checks of group

### What is multimedia? Multimedia. Continuous media. Most common media types. Continuous media processing. Interactivity. What is multimedia?

Multimedia What is multimedia? Media types +Text + Graphics + Audio +Image +Video Interchange formats What is multimedia? Multimedia = many media User interaction = interactivity Script = time 1 2 Most

### IMAGE COMPRESSION. October 7, ICSY Lab, University of Kaiserslautern, Germany

Lossless Compression Multimedia File Formats Lossy Compression IMAGE COMPRESSION 69 Basic Encoding Steps 70 JPEG (Overview) Image preparation and coding (baseline system) 71 JPEG (Enoding) 1) select color

### Image compression. Stefano Ferrari. Università degli Studi di Milano Methods for Image Processing. academic year

Image compression Stefano Ferrari Università degli Studi di Milano stefano.ferrari@unimi.it Methods for Image Processing academic year 2017 2018 Data and information The representation of images in a raw

### 2.2: Images and Graphics Digital image representation Image formats and color models JPEG, JPEG2000 Image synthesis and graphics systems

Chapter 2: Representation of Multimedia Data Audio Technology Images and Graphics Video Technology Chapter 3: Multimedia Systems Communication Aspects and Services Chapter 4: Multimedia Systems Storage

### JPEG compression of monochrome 2D-barcode images using DCT coefficient distributions

Edith Cowan University Research Online ECU Publications Pre. JPEG compression of monochrome D-barcode images using DCT coefficient distributions Keng Teong Tan Hong Kong Baptist University Douglas Chai

### ADVANCED IMAGE PROCESSING METHODS FOR ULTRASONIC NDE RESEARCH C. H. Chen, University of Massachusetts Dartmouth, N.

ADVANCED IMAGE PROCESSING METHODS FOR ULTRASONIC NDE RESEARCH C. H. Chen, University of Massachusetts Dartmouth, N. Dartmouth, MA USA Abstract: The significant progress in ultrasonic NDE systems has now

### Operation of machine vision system

ROBOT VISION Introduction The process of extracting, characterizing and interpreting information from images. Potential application in many industrial operation. Selection from a bin or conveyer, parts

### Multimedia. What is multimedia? Media types. Interchange formats. + Text +Graphics +Audio +Image +Video. Petri Vuorimaa 1

Multimedia What is multimedia? Media types + Text +Graphics +Audio +Image +Video Interchange formats Petri Vuorimaa 1 What is multimedia? Multimedia = many media User interaction = interactivity Script

### CS1114 Section 8: The Fourier Transform March 13th, 2013

CS1114 Section 8: The Fourier Transform March 13th, 2013 http://xkcd.com/26 Today you will learn about an extremely useful tool in image processing called the Fourier transform, and along the way get more

### REAL-TIME DIGITAL SIGNAL PROCESSING

REAL-TIME DIGITAL SIGNAL PROCESSING FUNDAMENTALS, IMPLEMENTATIONS AND APPLICATIONS Third Edition Sen M. Kuo Northern Illinois University, USA Bob H. Lee Ittiam Systems, Inc., USA Wenshun Tian Sonus Networks,

### CSE237A: Final Project Mid-Report Image Enhancement for portable platforms Rohit Sunkam Ramanujam Soha Dalal

CSE237A: Final Project Mid-Report Image Enhancement for portable platforms Rohit Sunkam Ramanujam (rsunkamr@ucsd.edu) Soha Dalal (sdalal@ucsd.edu) Project Goal The goal of this project is to incorporate

### Motivation. Gray Levels

Motivation Image Intensity and Point Operations Dr. Edmund Lam Department of Electrical and Electronic Engineering The University of Hong ong A digital image is a matrix of numbers, each corresponding

### Comparative Analysis of Image Compression Using Wavelet and Ridgelet Transform

Comparative Analysis of Image Compression Using Wavelet and Ridgelet Transform Thaarini.P 1, Thiyagarajan.J 2 PG Student, Department of EEE, K.S.R College of Engineering, Thiruchengode, Tamil Nadu, India

### CSCD 443/533 Advanced Networks Fall 2017

CSCD 443/533 Advanced Networks Fall 2017 Lecture 18 Compression of Video and Audio 1 Topics Compression technology Motivation Human attributes make it possible Audio Compression Video Compression Performance

### Hybrid Image Compression Using DWT, DCT and Huffman Coding. Techniques

Hybrid Image Compression Using DWT, DCT and Huffman Coding Techniques Veerpal kaur, Gurwinder kaur Abstract- Here in this hybrid model we are going to proposed a Nobel technique which is the combination

### A new predictive image compression scheme using histogram analysis and pattern matching

University of Wollongong Research Online University of Wollongong in Dubai - Papers University of Wollongong in Dubai 00 A new predictive image compression scheme using histogram analysis and pattern matching

### to ensure that both image processing and the intermediate representation of the coefficients are performed without significantly losing quality. The r

2-D Wavelet Transform using Fixed-Point Number Representation Λ A. Ruizy, J.R. Arnauy, J. M. Ordu~nay, V. Arnauy, F. Sillaz, andj. Duatoz yuniversidad de Valencia. Departamento de Informática. Av. Vicente

### Digital Image Representation. Image Representation. Color Models

Digital Representation Chapter : Representation of Multimedia Data Audio Technology s and Graphics Video Technology Chapter 3: Multimedia Systems Communication Aspects and Services Chapter 4: Multimedia

### Hybrid Image Compression Technique using Huffman Coding Algorithm

Technology Volume 1, Issue 2, October-December, 2013, pp. 37-45, IASTER 2013 www.iaster.com, Online: 2347-6109, Print: 2348-0017 ABSTRT Hybrid Image Compression Technique using Huffman Coding Algorithm

### DESIGNING A REAL TIME SYSTEM FOR CAR NUMBER DETECTION USING DISCRETE HOPFIELD NETWORK

DESIGNING A REAL TIME SYSTEM FOR CAR NUMBER DETECTION USING DISCRETE HOPFIELD NETWORK A.BANERJEE 1, K.BASU 2 and A.KONAR 3 COMPUTER VISION AND ROBOTICS LAB ELECTRONICS AND TELECOMMUNICATION ENGG JADAVPUR

### Lossy Coding 2 JPEG. Perceptual Image Coding. Discrete Cosine Transform JPEG. CS559 Lecture 9 JPEG, Raster Algorithms

CS559 Lecture 9 JPEG, Raster Algorithms These are course notes (not used as slides) Written by Mike Gleicher, Sept. 2005 With some slides adapted from the notes of Stephen Chenney Lossy Coding 2 Suppose

### Wavelet Based Image Compression Using ROI SPIHT Coding

International Journal of Information & Computation Technology. ISSN 0974-2255 Volume 1, Number 2 (2011), pp. 69-76 International Research Publications House http://www.irphouse.com Wavelet Based Image

### EECS 556 Image Processing W 09. Image enhancement. Smoothing and noise removal Sharpening filters

EECS 556 Image Processing W 09 Image enhancement Smoothing and noise removal Sharpening filters What is image processing? Image processing is the application of 2D signal processing methods to images Image

### Forensic Image Recognition using a Novel Image Fingerprinting and Hashing Technique

Forensic Image Recognition using a Novel Image Fingerprinting and Hashing Technique R D Neal, R J Shaw and A S Atkins Faculty of Computing, Engineering and Technology, Staffordshire University, Stafford

### Reduced Time Complexity for Detection of Copy-Move Forgery Using Discrete Wavelet Transform

Reduced Time Complexity for of Copy-Move Forgery Using Discrete Wavelet Transform Saiqa Khan Computer Engineering Dept., M.H Saboo Siddik College Of Engg., Mumbai, India Arun Kulkarni Information Technology

### Survey of the Mathematics of Big Data

Survey of the Mathematics of Big Data Issues with Big Data, Mathematics to the Rescue Philippe B. Laval KSU Fall 2015 Philippe B. Laval (KSU) Math & Big Data Fall 2015 1 / 28 Introduction We survey some

### A real-time SNR scalable transcoder for MPEG-2 video streams

EINDHOVEN UNIVERSITY OF TECHNOLOGY Department of Mathematics and Computer Science A real-time SNR scalable transcoder for MPEG-2 video streams by Mohammad Al-khrayshah Supervisors: Prof. J.J. Lukkien Eindhoven

### Megapixel Video for. Part 2 of 4. Brought to You by. Presented by Video Security Consultants

rought to You by 2009 Video Security Consultants Presented by Part 2 of 4 A1 Part 2 of 4 How to Avert a Compression Depression Illustration by Jerry King While bandwidth is widening, larger video systems

### Face Recognition for Different Facial Expressions Using Principal Component analysis

Face Recognition for Different Facial Expressions Using Principal Component analysis ASHISH SHRIVASTAVA *, SHEETESH SAD # # Department of Electronics & Communications, CIIT, Indore Dewas Bypass Road, Arandiya

### Image Processing (2) Point Operations and Local Spatial Operations

Intelligent Control Systems Image Processing (2) Point Operations and Local Spatial Operations Shingo Kagami Graduate School of Information Sciences, Tohoku University swk(at)ic.is.tohoku.ac.jp http://www.ic.is.tohoku.ac.jp/ja/swk/

### Motivation. Intensity Levels

Motivation Image Intensity and Point Operations Dr. Edmund Lam Department of Electrical and Electronic Engineering The University of Hong ong A digital image is a matrix of numbers, each corresponding

### Generation of Digital Watermarked Anaglyph 3D Image Using DWT

SSRG International Journal of Electronics and Communication Engineering (SSRG-IJECE) volume1 issue7 Sep 2014 Generation of Digital Anaglyph 3D Using DWT D.Usha 1, Y.Rakesh 2 1 MTech Student, 2 Assistant

### TKT-2431 SoC design. Introduction to exercises. SoC design / September 10

TKT-2431 SoC design Introduction to exercises Assistants: Exercises and the project work Juha Arvio juha.arvio@tut.fi, Otto Esko otto.esko@tut.fi In the project work, a simplified H.263 video encoder is

### Quality Access Control of a Compressed Gray Scale Image

Quality Access Control of a Compressed Gray Scale Image Amit Phadikar, Malay K. Kundu 2, Santi P. Maity 3 Department of Information Technology, MCKV Institute of Engineering, Liluah, Howrah 7204, India.

### AN OPTIMIZED LOSSLESS IMAGE COMPRESSION TECHNIQUE IN IMAGE PROCESSING

AN OPTIMIZED LOSSLESS IMAGE COMPRESSION TECHNIQUE IN IMAGE PROCESSING 1 MAHENDRA PRATAP PANIGRAHY, 2 NEERAJ KUMAR Associate Professor, Department of ECE, Institute of Technology Roorkee, Roorkee Associate

### High Quality Image Compression

Article ID: WMC001673 ISSN 2046-1690 High Quality Image Compression Corresponding Author: Dr. Rash B Dubey, Professor, ECE Dept, Hindu College of Engg, Sonepat, 121003 - India Submitting Author: Dr. Rash

### Multimedia Technology CHAPTER 4. Video and Animation

CHAPTER 4 Video and Animation - Both video and animation give us a sense of motion. They exploit some properties of human eye s ability of viewing pictures. - Motion video is the element of multimedia

### Texture Segmentation and Classification in Biomedical Image Processing

Texture Segmentation and Classification in Biomedical Image Processing Aleš Procházka and Andrea Gavlasová Department of Computing and Control Engineering Institute of Chemical Technology in Prague Technická

### Image Compression for Mobile Devices using Prediction and Direct Coding Approach

Image Compression for Mobile Devices using Prediction and Direct Coding Approach Joshua Rajah Devadason M.E. scholar, CIT Coimbatore, India Mr. T. Ramraj Assistant Professor, CIT Coimbatore, India Abstract

### DCT Based, Lossy Still Image Compression

DCT Based, Lossy Still Image Compression NOT a JPEG artifact! Lenna, Playboy Nov. 1972 Lena Soderberg, Boston, 1997 Nimrod Peleg Update: April. 2009 http://www.lenna.org/ Image Compression: List of Topics