University of Maryland College Park. Digital Signal Processing: ENEE425. Fall Project#2: Image Compression. Ronak Shah & Franklin L Nouketcha
|
|
- Simon Barnett
- 5 years ago
- Views:
Transcription
1 University of Maryland College Park Digital Signal Processing: ENEE425 Fall 2012 Project#2: Image Compression Ronak Shah & Franklin L Nouketcha
2 I- Introduction Data compression is core in communication as it diminishes the size of files, and therefore facilitates their transmission. In this project we are compressing the images of Lena (Fig.1) and Baboon (Fig.2) respectively subject to a PSNR of 40 and 20. The PSNR indicates the quality of the image, and the compression ratio is the ratio in size of the compressed image and its original. Better compressions are achieved with large compression ratios; however, as the compression ratio increases, the PSNR decreases degrading the overall quality of the image. The problem is resumed to one question. How can we achieve a large compression ratio without destroying the quality of the image? Figure 1: original picture of Lena
3 Figure 2: Original Picture of baboon II- Approach Compressing an image is to represent the most important components of that image with more bits while allocating fewer bits to its less important components. One way to look at the most important components of a picture is to look at its DCT spectrum obtained by performing a two dimensional discrete cosine transform (2-D DCT). In this project we performed the DCT on each 8X8, 16X16 and 32X32 blocks of the original images, and by carefully analyzing the resulting DCT spectrums, we made the appropriate quantization table to compress the images. For the case where the image was divided into 8X8 blocks for instance, there were a total of 4096 blocks of such size as the original image had blocks (512X512).
4 The quantization table needed in this case is supposed to be 8X8 and made in a way that it would fairly quantize all of the 4096 blocks not just a specific block. We look at all the 4096 blocks at the same time and we took the variance of all the elements having the same position within each block, to obtain a total of 64 variances that we redistributed in a 8X8 table (Fig.3). Figure 3: Example of variance table (Baboon 8X8 blocks) The variance matrix shows the most important positions within a block that need to be represented with more bits and the less important positions within a block to be represented with fewer bits (even zero). Higher variances correspond to important positions, thereby needing more bits. To have better and fair allocation of the quantization table we defined some threshold values (Fig.4) in order to compare the range of all the variances. The most important blocks were to be assigned eight bits and denoted by 1= (2 0 ) in the quantization table (division by 2 n in binary shift to the right n times resulting in n less bits). The second less important blocks were to be assigned 7 or fewer bits (division by 2 resulting in seven bits) as depicted in Fig.5. And so on. In order to obtain a higher compression ratio we just adjusted our threshold values to achieve a quantization table that would get rid of more bits.
5 Figure 4: Threshold value use to make Variance table of baboon to obtain PSNR =40 Figure 5: An example of a quantization table for baboon 8X8 subject to PSNR=40. Once the DCT of the original image was quantized we found the inverse discrete cosine transform (IDCT) for each block to reconstruct the compressed image. We followed the same step for the 16X16 blocks (1024 blocks totals) and for the 32 by 32 blocks (256 blocks total). A- 8X8 blocks III- Lena For this case we subjected to a PSNR of with the following bit assignment for quantization levels for various variance ranges: Bits Allocated Variance Range 8 Greater than Less than 20
6 It had a compression ratio of Below (Fig. 6) is the resulting image produced after such compression: Figure 6: Reconstructed image of Lena for 8X8 PSNR=40. For this next case we subjected to a PSNR of with the following bit assignment for quantization levels for various variance ranges: Bits Allocated Variance Range Less than 90000
7 It had a compression ratio of 512 as a lot of the data is lost. Below (Fig. 7) is the resulting image produced after such compression: B- 16X16 blocks Figure 7: Reconstructed image of Lena for 8X8 PSNR~20. For this case we subjected to a PSNR of with the following bit assignment for quantization levels for various variance ranges: Bits Allocated Variance Range 8 Greater than Less than 15
8 It had a compression ratio of Below (Fig. 8) is the resulting image produced after such compression: Figure 8: Reconstructed image of Lena for 16X16 PSNR=40. For this next case we subjected to a PSNR of with the following bit assignment for quantization levels for various variance ranges: Bits Allocated Variance Range Less than It had a compression ratio of 2048 as a lot of the data is lost. Below (Fig. 9) is the resulting image produced after such compression:
9 C- 32X32 blocks Figure 9: Reconstructed image of Lena for 16X16 PSNR~20. For this case we subjected to a PSNR of with the following bit assignment for quantization levels for various variance ranges: Bits Allocated Variance Range 8 Greater than Less than 10 It had a compression ratio of Below (Fig. 10) is the resulting image produced after such compression:
10 Figure 10: Reconstructed image of Lena for 32X32 PSNR=40. For this next case we subjected to a PSNR of with the following bit assignment for quantization levels for various variance ranges: Bits Allocated Variance Range Less than It had a compression ratio of 8192 as a lot of the data is lost. Below (Fig. 11) is the resulting image produced after such compression:
11 Figure 11: Reconstructed image of Lena for 32X32 PSNR~20.
12 A- 8X8 blocks IV- Baboon For this case we subjected to a PSNR of with the following bit assignment for quantization levels for various variance ranges: Bits Allocated Variance Range 8 Greater than Less than 50 It had a compression ratio of 1.31 as a lot of the data is lost. Below (Fig. 12) is the resulting image produced after such compression: Figure 12: Reconstructed image of Baboon 8X8 subject to PSNR=40.
13 For this next case we subjected to a PSNR of with the following bit assignment for quantization levels for various variance ranges: Bits Allocated Variance Range 8 Greater than Less than It had a compression ratio of as a lot of the data is lost. Below (Fig. 13) is the resulting image produced after such compression: Figure 13: Reconstructed image of Baboon 8X8 subject to PSNR~20.
14 B- 16X16 blocks For this case we subjected to a PSNR of with the following bit assignment for quantization levels for various variance ranges: Bits Allocated Variance Range 8 Greater than Less than 50 It had a compression ratio of 1.43 as a lot of the data is lost. Below (Fig. 14) is the resulting image produced after such compression: Figure 14: Reconstructed image of Baboon 16X16 subject to PSNR=40 For this next case we subjected to a PSNR of with the following bit assignment for quantization levels for various variance ranges:
15 Bits Allocated Variance Range 8 Greater than Less than It had a compression ratio of 256 as a lot of the data is lost. Below (Fig. 15) is the resulting image produced after such compression: Figure 15: Reconstructed image of Baboon 16X16 subject to PSNR~20. C- 32X32 blocks For this case we subjected to a PSNR of with the following bit assignment for quantization levels for various variance ranges:
16 Bits Allocated Variance Range 8 Greater than Less than 50 It had a compression ratio of 1.39 as a lot of the data is lost. Below (Fig. 16) is the resulting image produced after such compression: Figure 16: Reconstructed image of Baboon 32X32 subject to PSNR=40. For this next case we subjected to a PSNR of with the following bit assignment for quantization levels for various variance ranges:
17 Bits Allocated Variance Range 8 Greater than Less than It had a compression ratio of as a lot of the data is lost. Below (Fig. 17) is the resulting image produced after such compression: Figure 17: Reconstructed image of Baboon 32X32 subject to PSNR~20.
18 V- Conclusion The table below summarizes our results that we obtained. Lena Baboon 8x8 16x16 32x32 PSNR Compression Ratio PSNR Compression Ratio PSNR Compression Ratio Our results are what we expected to obtain. We achieved good compression ratios for both figures subject to a PSNR of 40. Lena was able to be compressed more than Baboon. This is due to the way Baboon is compromised. Baboon has a lot of high frequency components. Therefore, Baboon has a lot of its data stored in the top left corner coefficients of a N*N block which leaves very little data in the lower right corner coefficients to be truncated. Therefore, you will not achieve a compression rate as high as Lena. While Lena, had a majority of its data stored in coefficients in the top left corner, it had more coefficients in the lower right corner than Baboon that could be truncated to achieve a quality with PSNR of 40; thereby having a higher compression ratio. The images lost a lot of quality when subject to a PSNR of 20, as expected. Therefore, compression ratios were very high as a lot of data had to be truncated to zero in order to achieve such a PSNR, including coefficients in the very top left corner. However, this is expected for an image being compressed subject to such a PSNR of 20. A large difficulty of the project is determining how to develop your quantization matrix that you will apply to each DCT N*N block based off the variances. Initially we had decided to go with a quantization matrix that would be compromised of zeros and ones that would then be multiplied to each N*N block. Determining where the ones and zeros would be placed, was based on an analysis of variance values. Positions with higher variances would be multiplied by a one to preserve its
19 corresponding coefficient and positions with lower variances would be multiplied by a zero to throw out its corresponding coefficient. Therefore, positions that were multiplied by one would need 8 bits to be represented and 0 bits for positions that were multiplied by a zero. However, we found that this method was too drastic and resulted in compression ratios that were not satisfactory. The method we implemented alleviated this issue because it allowed for finer quantization levels rather than such drastic levels (8 bits or 0 bits). The difficulty in this method is that you must develop threshold ranges for variances and based off these ranges, assign a proper amount of bits to represent that coefficient. Determining these ranges to achieve the proper PSNR and the best compression ratio proved to be a difficult process. However, after many attempts, our final results are the best combination. A key lesson in this project was determining which block size proved to be the best in terms of the best compression ratio subject to a specific PSNR. We learned from our results that the best results are obtained through 8X8 and 16X16. The finer the segmentation, the smaller the variance in a particular position in an N*N block. Taking an extreme case, if we break the image into 64X64 blocks, a single block would cover a larger part of the image, thereby allowing for a larger variance between blocks as each block assumes a larger part of the image. Therefore, we found that the finer the segmentation, 8X8, the higher the compression ratio you can achieve subject to a PSNR. However, in order to achieve this result, one must be careful in determining their quantization levels. As more than one set of quantization levels can result in the same PSNR, thereby giving various compression ratios, one could get results that show a 32X32 segmentation providing a better compression ratio. Therefore, one must optimize their quantization levels to achieve the highest compression ratio when subject to a PSNR.
CS4495/6495 Introduction to Computer Vision. 2C-L3 Aliasing
CS4495/6495 Introduction to Computer Vision 2C-L3 Aliasing Recall: Fourier Pairs (from Szeliski) Fourier Transform Sampling Pairs FT of an impulse train is an impulse train Sampling and Aliasing Sampling
More informationThe Scientist and Engineer's Guide to Digital Signal Processing By Steven W. Smith, Ph.D.
The Scientist and Engineer's Guide to Digital Signal Processing By Steven W. Smith, Ph.D. Home The Book by Chapters About the Book Steven W. Smith Blog Contact Book Search Download this chapter in PDF
More informationModule 6 STILL IMAGE COMPRESSION STANDARDS
Module 6 STILL IMAGE COMPRESSION STANDARDS Lesson 16 Still Image Compression Standards: JBIG and JPEG Instructional Objectives At the end of this lesson, the students should be able to: 1. Explain the
More informationA Novel Approach of Compressing Images and Assessment on Quality with Scaling Factor
A Novel Approach of Compressing Images and Assessment on Quality with Scaling Factor Umesh 1,Mr. Suraj Rana 2 1 M.Tech Student, 2 Associate Professor (ECE) Department of Electronic and Communication Engineering
More informationEfficient Image Compression Technique using JPEG2000 with Adaptive Threshold
Efficient Image Compression Technique using JPEG2000 with Adaptive Threshold Md. Masudur Rahman Mawlana Bhashani Science and Technology University Santosh, Tangail-1902 (Bangladesh) Mohammad Motiur Rahman
More informationEEL 6562 Image Processing and Computer Vision Image Restoration
DEPARTMENT OF ELECTRICAL & COMPUTER ENGINEERING EEL 6562 Image Processing and Computer Vision Image Restoration Rajesh Pydipati Introduction Image Processing is defined as the analysis, manipulation, storage,
More informationAN ERROR LIMITED AREA EFFICIENT TRUNCATED MULTIPLIER FOR IMAGE COMPRESSION
AN ERROR LIMITED AREA EFFICIENT TRUNCATED MULTIPLIER FOR IMAGE COMPRESSION K.Mahesh #1, M.Pushpalatha *2 #1 M.Phil.,(Scholar), Padmavani Arts and Science College. *2 Assistant Professor, Padmavani Arts
More informationMODIFIED DCT BASED SPEECH ENHANCEMENT IN VEHICULAR ENVIRONMENTS
MODIFIED DCT BASED SPEECH ENHANCEMENT IN VEHICULAR ENVIRONMENTS 1 S.PRASANNA VENKATESH, 2 NITIN NARAYAN, 3 K.SAILESH BHARATHWAAJ, 4 M.P.ACTLIN JEEVA, 5 P.VIJAYALAKSHMI 1,2,3,4,5 SSN College of Engineering,
More informationEnhanced DCT Interpolation for better 2D Image Up-sampling
Enhanced Interpolation for better 2D Image Up-sampling Aswathy S Raj MTech Student, Department of ECE Marian Engineering College, Kazhakuttam, Thiruvananthapuram, Kerala, India Reshmalakshmi C Assistant
More informationAudio and Speech Compression Using DCT and DWT Techniques
Audio and Speech Compression Using DCT and DWT Techniques M. V. Patil 1, Apoorva Gupta 2, Ankita Varma 3, Shikhar Salil 4 Asst. Professor, Dept.of Elex, Bharati Vidyapeeth Univ.Coll.of Engg, Pune, Maharashtra,
More informationA Modified Image Coder using HVS Characteristics
A Modified Image Coder using HVS Characteristics Mrs Shikha Tripathi, Prof R.C. Jain Birla Institute Of Technology & Science, Pilani, Rajasthan-333 031 shikha@bits-pilani.ac.in, rcjain@bits-pilani.ac.in
More informationIdentification of Bitmap Compression History: JPEG Detection and Quantizer Estimation
230 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 12, NO. 2, FEBRUARY 2003 Identification of Bitmap Compression History: JPEG Detection and Quantizer Estimation Zhigang Fan and Ricardo L. de Queiroz, Senior
More informationFong, WC; Chan, SC; Nallanathan, A; Ho, KL. Ieee Transactions On Image Processing, 2002, v. 11 n. 10, p
Title Integer lapped transforms their applications to image coding Author(s) Fong, WC; Chan, SC; Nallanathan, A; Ho, KL Citation Ieee Transactions On Image Processing, 2002, v. 11 n. 10, p. 1152-1159 Issue
More informationAssistant Lecturer Sama S. Samaan
MP3 Not only does MPEG define how video is compressed, but it also defines a standard for compressing audio. This standard can be used to compress the audio portion of a movie (in which case the MPEG standard
More informationNonuniform multi level crossing for signal reconstruction
6 Nonuniform multi level crossing for signal reconstruction 6.1 Introduction In recent years, there has been considerable interest in level crossing algorithms for sampling continuous time signals. Driven
More informationA SURVEY ON DICOM IMAGE COMPRESSION AND DECOMPRESSION TECHNIQUES
A SURVEY ON DICOM IMAGE COMPRESSION AND DECOMPRESSION TECHNIQUES Shreya A 1, Ajay B.N 2 M.Tech Scholar Department of Computer Science and Engineering 2 Assitant Professor, Department of Computer Science
More informationAPPLICATIONS OF DSP OBJECTIVES
APPLICATIONS OF DSP OBJECTIVES This lecture will discuss the following: Introduce analog and digital waveform coding Introduce Pulse Coded Modulation Consider speech-coding principles Introduce the channel
More informationImage Compression Technique Using Different Wavelet Function
Compression Technique Using Different Dr. Vineet Richariya Mrs. Shweta Shrivastava Naman Agrawal Professor Assistant Professor Research Scholar Dept. of Comp. Science & Engg. Dept. of Comp. Science & Engg.
More informationInternational Journal of Digital Application & Contemporary research Website: (Volume 1, Issue 7, February 2013)
Performance Analysis of OFDM under DWT, DCT based Image Processing Anshul Soni soni.anshulec14@gmail.com Ashok Chandra Tiwari Abstract In this paper, the performance of conventional discrete cosine transform
More informationComputing for Engineers in Python
Computing for Engineers in Python Lecture 10: Signal (Image) Processing Autumn 2011-12 Some slides incorporated from Benny Chor s course 1 Lecture 9: Highlights Sorting, searching and time complexity Preprocessing
More informationSpeech Coding in the Frequency Domain
Speech Coding in the Frequency Domain Speech Processing Advanced Topics Tom Bäckström Aalto University October 215 Introduction The speech production model can be used to efficiently encode speech signals.
More informationSatellite Image Compression using Discrete wavelet Transform
IOSR Journal of Engineering (IOSRJEN) ISSN (e): 2250-3021, ISSN (p): 2278-8719 Vol. 08, Issue 01 (January. 2018), V2 PP 53-59 www.iosrjen.org Satellite Image Compression using Discrete wavelet Transform
More informationCompression and Image Formats
Compression Compression and Image Formats Reduce amount of data used to represent an image/video Bit rate and quality requirements Necessary to facilitate transmission and storage Required quality is application
More informationA POSTPROCESSING TECHNIQUE FOR COMPRESSION ARTIFACT REMOVAL IN IMAGES
A POSTPROCESSING TECHNIQUE FOR COMPRESSION ARTIFACT REMOVAL IN IMAGES Nirmal Kaur Department of Computer Science,Punjabi University Campus,Maur(Bathinda),India Corresponding e-mail:- kaurnirmal88@gmail.com
More informationChapter 9 Image Compression Standards
Chapter 9 Image Compression Standards 9.1 The JPEG Standard 9.2 The JPEG2000 Standard 9.3 The JPEG-LS Standard 1IT342 Image Compression Standards The image standard specifies the codec, which defines how
More informationTime division multiplexing The block diagram for TDM is illustrated as shown in the figure
CHAPTER 2 Syllabus: 1) Pulse amplitude modulation 2) TDM 3) Wave form coding techniques 4) PCM 5) Quantization noise and SNR 6) Robust quantization Pulse amplitude modulation In pulse amplitude modulation,
More informationA COMPARATIVE ANALYSIS OF DCT AND DWT BASED FOR IMAGE COMPRESSION ON FPGA
International Journal of Applied Engineering Research and Development (IJAERD) ISSN:2250 1584 Vol.2, Issue 1 (2012) 13-21 TJPRC Pvt. Ltd., A COMPARATIVE ANALYSIS OF DCT AND DWT BASED FOR IMAGE COMPRESSION
More informationJPEG2000 Choices and Tradeoffs for Encoders
dsp tips & tricks Krishnaraj Varma and Amy Bell JPEG2000 Choices and Tradeoffs for Encoders Anew, and improved, image coding standard has been developed, and it s called JPEG2000. In this article we describe
More informationA Modified Image Template for FELICS Algorithm for Lossless Image Compression
Research Article International Journal of Current Engineering and Technology E-ISSN 2277 4106, P-ISSN 2347-5161 2014 INPRESSCO, All Rights Reserved Available at http://inpressco.com/category/ijcet A Modified
More informationDEVELOPMENT OF LOSSY COMMPRESSION TECHNIQUE FOR IMAGE
DEVELOPMENT OF LOSSY COMMPRESSION TECHNIQUE FOR IMAGE Asst.Prof.Deepti Mahadeshwar,*Prof. V.M.Misra Department of Instrumentation Engineering, Vidyavardhini s College of Engg. And Tech., Vasai Road, *Prof
More informationChapter 4 SPEECH ENHANCEMENT
44 Chapter 4 SPEECH ENHANCEMENT 4.1 INTRODUCTION: Enhancement is defined as improvement in the value or Quality of something. Speech enhancement is defined as the improvement in intelligibility and/or
More informationAudio Signal Compression using DCT and LPC Techniques
Audio Signal Compression using DCT and LPC Techniques P. Sandhya Rani#1, D.Nanaji#2, V.Ramesh#3,K.V.S. Kiran#4 #Student, Department of ECE, Lendi Institute Of Engineering And Technology, Vizianagaram,
More informationDiscrete Wavelet Transform For Image Compression And Quality Assessment Of Compressed Images
Research Paper Volume 2 Issue 9 May 2015 International Journal of Informative & Futuristic Research ISSN (Online): 2347-1697 Discrete Wavelet Transform For Image Compression And Quality Assessment Of Compressed
More informationNon Linear Image Enhancement
Non Linear Image Enhancement SAIYAM TAKKAR Jaypee University of information technology, 2013 SIMANDEEP SINGH Jaypee University of information technology, 2013 Abstract An image enhancement algorithm based
More informationComparing Multiresolution SVD with Other Methods for Image Compression
1 Comparing Multiresolution SVD with Other Methods for Image Compression Ryuichi Ashino (1), Akira Morimoto (2), Michihiro Nagase (3), and Rémi Vaillancourt (4) 1 Osaka Kyoiku University, Kashiwara, Japan
More informationA new quad-tree segmented 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 A new quad-tree segmented image compression scheme using histogram analysis and pattern
More informationDirection-Adaptive Partitioned Block Transform for Color Image Coding
Direction-Adaptive Partitioned Block Transform for Color Image Coding Mina Makar, Sam Tsai Final Project, EE 98, Stanford University Abstract - In this report, we investigate the application of Direction
More informationComputer Vision. Howie Choset Introduction to Robotics
Computer Vision Howie Choset http://www.cs.cmu.edu.edu/~choset Introduction to Robotics http://generalrobotics.org What is vision? What is computer vision? Edge Detection Edge Detection Interest points
More informationAn Enhanced Least Significant Bit Steganography Technique
An Enhanced Least Significant Bit Steganography Technique Mohit Abstract - Message transmission through internet as medium, is becoming increasingly popular. Hence issues like information security are
More informationDigital Image Processing 3/e
Laboratory Projects for Digital Image Processing 3/e by Gonzalez and Woods 2008 Prentice Hall Upper Saddle River, NJ 07458 USA www.imageprocessingplace.com The following sample laboratory projects are
More informationImage Compression Supported By Encryption Using Unitary Transform
Image Compression Supported By Encryption Using Unitary Transform Arathy Nair 1, Sreejith S 2 1 (M.Tech Scholar, Department of CSE, LBS Institute of Technology for Women, Thiruvananthapuram, India) 2 (Assistant
More informationCh. 3: Image Compression Multimedia Systems
4/24/213 Ch. 3: Image Compression Multimedia Systems Prof. Ben Lee (modified by Prof. Nguyen) Oregon State University School of Electrical Engineering and Computer Science Outline Introduction JPEG Standard
More informationA Low Power CMOS Imaging System with Smart Image Capture and Adaptive Complexity 2D-DCT Calculation
J. Low Power Electron. Appl. 213, 3, 267-278; doi:1.339/jlpea33267 Article Journal of Low Power Electronics and Applications ISSN 279-9268 www.mdpi.com/journal/jlpea A Low Power CMOS Imaging System with
More information2. REVIEW OF LITERATURE
2. REVIEW OF LITERATURE Digital image processing is the use of the algorithms and procedures for operations such as image enhancement, image compression, image analysis, mapping. Transmission of information
More informationISSN: ISO 9001:2008 Certified International Journal of Engineering and Innovative Technology (IJEIT) Volume 2, Issue 3, September 2012
A Tailored Anti-Forensic Approach for Digital Image Compression S.Manimurugan, Athira B.Kaimal Abstract- The influence of digital images on modern society is incredible; image processing has now become
More informationTemplates and Image Pyramids
Templates and Image Pyramids 09/06/11 Computational Photography Derek Hoiem, University of Illinois Project 1 Due Monday at 11:59pm Options for displaying results Web interface or redirect (http://www.pa.msu.edu/services/computing/faq/autoredirect.html)
More informationQuality-Aware Techniques for Reducing Power of JPEG Codecs
DOI 10.1007/s11265-012-0667-5 Quality-Aware Techniques for Reducing Power of JPEG Codecs Yunus Emre Chaitali Chakrabarti Received: 4 November 2011 / Revised: 30 January 2012 / Accepted: 8 February 2012
More informationENEE408G Multimedia Signal Processing
ENEE48G Multimedia Signal Processing Design Project on Image Processing and Digital Photography Goals:. Understand the fundamentals of digital image processing.. Learn how to enhance image quality and
More informationINSTITUTE OF AERONAUTICAL ENGINEERING Dundigal, Hyderabad
INSTITUTE OF AERONAUTICAL ENGINEERING Dundigal, Hyderabad - 500 043 ELECTRONICS AND COMMUNICATION ENGINEERING QUESTION BANK Course Title Course Code Class Branch DIGITAL IMAGE PROCESSING A70436 IV B. Tech.
More informationThe Strengths and Weaknesses of Different Image Compression Methods. Samuel Teare and Brady Jacobson
The Strengths and Weaknesses of Different Image Compression Methods Samuel Teare and Brady Jacobson Lossy vs Lossless Lossy compression reduces a file size by permanently removing parts of the data that
More informationPractical Content-Adaptive Subsampling for Image and Video Compression
Practical Content-Adaptive Subsampling for Image and Video Compression Alexander Wong Department of Electrical and Computer Eng. University of Waterloo Waterloo, Ontario, Canada, N2L 3G1 a28wong@engmail.uwaterloo.ca
More informationTemplates and Image Pyramids
Templates and Image Pyramids 09/07/17 Computational Photography Derek Hoiem, University of Illinois Why does a lower resolution image still make sense to us? What do we lose? Image: http://www.flickr.com/photos/igorms/136916757/
More information2.1. General Purpose Run Length Encoding Relative Encoding Tokanization or Pattern Substitution
2.1. General Purpose There are many popular general purpose lossless compression techniques, that can be applied to any type of data. 2.1.1. Run Length Encoding Run Length Encoding is a compression technique
More informationIntroduction to More Advanced Steganography. John Ortiz. Crucial Security Inc. San Antonio
Introduction to More Advanced Steganography John Ortiz Crucial Security Inc. San Antonio John.Ortiz@Harris.com 210 977-6615 11/17/2011 Advanced Steganography 1 Can YOU See the Difference? Which one of
More information8.2 IMAGE PROCESSING VERSUS IMAGE ANALYSIS Image processing: The collection of routines and
8.1 INTRODUCTION In this chapter, we will study and discuss some fundamental techniques for image processing and image analysis, with a few examples of routines developed for certain purposes. 8.2 IMAGE
More informationA Novel Image Steganography Based on Contourlet Transform and Hill Cipher
Journal of Information Hiding and Multimedia Signal Processing c 2015 ISSN 2073-4212 Ubiquitous International Volume 6, Number 5, September 2015 A Novel Image Steganography Based on Contourlet Transform
More informationLIST 04 Submission Date: 04/05/2017; Cut-off: 14/05/2017. Part 1 Theory. Figure 1: horizontal profile of the R, G and B components.
Universidade de Brasília (UnB) Faculdade de Tecnologia (FT) Departamento de Engenharia Elétrica (ENE) Course: Image Processing Prof. Mylène C.Q. de Farias Semester: 2017.1 LIST 04 Submission Date: 04/05/2017;
More informationDigital Watermarking Using Homogeneity in Image
Digital Watermarking Using Homogeneity in Image S. K. Mitra, M. K. Kundu, C. A. Murthy, B. B. Bhattacharya and T. Acharya Dhirubhai Ambani Institute of Information and Communication Technology Gandhinagar
More informationFourier Transform. Any signal can be expressed as a linear combination of a bunch of sine gratings of different frequency Amplitude Phase
Fourier Transform Fourier Transform Any signal can be expressed as a linear combination of a bunch of sine gratings of different frequency Amplitude Phase 2 1 3 3 3 1 sin 3 3 1 3 sin 3 1 sin 5 5 1 3 sin
More informationComparison of Image Compression and Enhancement Techniques for Image Quality in Medical Images.
Master Thesis Electrical Engineering February 2017 Master of Science in Electrical Engineering with Emphasis on Signal Processing Comparison of Image Compression and Enhancement Techniques for Image Quality
More informationImage Compression Using Haar Wavelet Transform
Image Compression Using Haar Wavelet Transform ABSTRACT Nidhi Sethi, Department of Computer Science Engineering Dehradun Institute of Technology, Dehradun Uttrakhand, India Email:nidhipankaj.sethi102@gmail.com
More informationCHANNEL MEASUREMENT. Channel measurement doesn t help for single bit transmission in flat Rayleigh fading.
CHANNEL MEASUREMENT Channel measurement doesn t help for single bit transmission in flat Rayleigh fading. It helps (as we soon see) in detection with multi-tap fading, multiple frequencies, multiple antennas,
More informationABSTRACT. We investigate joint source-channel coding for transmission of video over time-varying channels. We assume that the
Robust Video Compression for Time-Varying Wireless Channels Shankar L. Regunathan and Kenneth Rose Dept. of Electrical and Computer Engineering, University of California, Santa Barbara, CA 93106 ABSTRACT
More informationArtifacts and Antiforensic Noise Removal in JPEG Compression Bismitha N 1 Anup Chandrahasan 2 Prof. Ramayan Pratap Singh 3
IJSRD - International Journal for Scientific Research & Development Vol. 3, Issue 05, 2015 ISSN (online: 2321-0613 Artifacts and Antiforensic Noise Removal in JPEG Compression Bismitha N 1 Anup Chandrahasan
More informationEvaluation of Audio Compression Artifacts M. Herrera Martinez
Evaluation of Audio Compression Artifacts M. Herrera Martinez This paper deals with subjective evaluation of audio-coding systems. From this evaluation, it is found that, depending on the type of signal
More informationAudio Compression using the MLT and SPIHT
Audio Compression using the MLT and SPIHT Mohammed Raad, Alfred Mertins and Ian Burnett School of Electrical, Computer and Telecommunications Engineering University Of Wollongong Northfields Ave Wollongong
More informationObjective Evaluation of Edge Blur and Ringing Artefacts: Application to JPEG and JPEG 2000 Image Codecs
Objective Evaluation of Edge Blur and Artefacts: Application to JPEG and JPEG 2 Image Codecs G. A. D. Punchihewa, D. G. Bailey, and R. M. Hodgson Institute of Information Sciences and Technology, Massey
More informationEC 6501 DIGITAL COMMUNICATION UNIT - II PART A
EC 6501 DIGITAL COMMUNICATION 1.What is the need of prediction filtering? UNIT - II PART A [N/D-16] Prediction filtering is used mostly in audio signal processing and speech processing for representing
More informationA Compression Artifacts Reduction Method in Compressed Image
A Compression Artifacts Reduction Method in Compressed Image Jagjeet Singh Department of Computer Science & Engineering DAVIET, Jalandhar Harpreet Kaur Department of Computer Science & Engineering DAVIET,
More informationSpread Spectrum Watermarking Using HVS Model and Wavelets in JPEG 2000 Compression
Spread Spectrum Watermarking Using HVS Model and Wavelets in JPEG 2000 Compression Khaly TALL 1, Mamadou Lamine MBOUP 1, Sidi Mohamed FARSSI 1, Idy DIOP 1, Abdou Khadre DIOP 1, Grégoire SISSOKO 2 1. Laboratoire
More informationFPGA implementation of DWT for Audio Watermarking Application
FPGA implementation of DWT for Audio Watermarking Application Naveen.S.Hampannavar 1, Sajeevan Joseph 2, C.B.Bidhul 3, Arunachalam V 4 1, 2, 3 M.Tech VLSI Students, 4 Assistant Professor Selection Grade
More informationCompressive Optical MONTAGE Photography
Invited Paper Compressive Optical MONTAGE Photography David J. Brady a, Michael Feldman b, Nikos Pitsianis a, J. P. Guo a, Andrew Portnoy a, Michael Fiddy c a Fitzpatrick Center, Box 90291, Pratt School
More informationIMAGE PROCESSING: POINT PROCESSES
IMAGE PROCESSING: POINT PROCESSES N. C. State University CSC557 Multimedia Computing and Networking Fall 2001 Lecture # 11 IMAGE PROCESSING: POINT PROCESSES N. C. State University CSC557 Multimedia Computing
More informationTDI2131 Digital Image Processing
TDI2131 Digital Image Processing Image Enhancement in Spatial Domain Lecture 3 John See Faculty of Information Technology Multimedia University Some portions of content adapted from Zhu Liu, AT&T Labs.
More informationCompressed Image Transmission over AWGN Channel using DCT and Raised Cosine Filter
Compressed Image Transmission over AWGN Channel using DCT and Raised Cosine Filter Md. Khaliluzzaman* Dept. of Computer Science & Engineering (CSE) International Islamic University Chittagong (IIUC) Chittagong-4203,
More informationUNEQUAL POWER ALLOCATION FOR JPEG TRANSMISSION OVER MIMO SYSTEMS. Muhammad F. Sabir, Robert W. Heath Jr. and Alan C. Bovik
UNEQUAL POWER ALLOCATION FOR JPEG TRANSMISSION OVER MIMO SYSTEMS Muhammad F. Sabir, Robert W. Heath Jr. and Alan C. Bovik Department of Electrical and Computer Engineering, The University of Texas at Austin,
More informationRECENTLY, there has been an increasing interest in noisy
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II: EXPRESS BRIEFS, VOL. 52, NO. 9, SEPTEMBER 2005 535 Warped Discrete Cosine Transform-Based Noisy Speech Enhancement Joon-Hyuk Chang, Member, IEEE Abstract In
More informationSPEECH ENHANCEMENT USING A ROBUST KALMAN FILTER POST-PROCESSOR IN THE MODULATION DOMAIN. Yu Wang and Mike Brookes
SPEECH ENHANCEMENT USING A ROBUST KALMAN FILTER POST-PROCESSOR IN THE MODULATION DOMAIN Yu Wang and Mike Brookes Department of Electrical and Electronic Engineering, Exhibition Road, Imperial College London,
More informationMULTISPECTRAL IMAGE PROCESSING I
TM1 TM2 337 TM3 TM4 TM5 TM6 Dr. Robert A. Schowengerdt TM7 Landsat Thematic Mapper (TM) multispectral images of desert and agriculture near Yuma, Arizona MULTISPECTRAL IMAGE PROCESSING I SENSORS Multispectral
More informationEIE 441 Advanced Digital communications
EIE 441 Advanced Digital communications MACHED FILER 1. Consider the signal s ( ) shown in Fig. 1. 1 t (a) Determine the impulse response of a filter matched to this signal and sketch it as a function
More informationSignal Processing First Lab 20: Extracting Frequencies of Musical Tones
Signal Processing First Lab 20: Extracting Frequencies of Musical Tones Pre-Lab and Warm-Up: You should read at least the Pre-Lab and Warm-up sections of this lab assignment and go over all exercises in
More informationSensors & Transducers 2015 by IFSA Publishing, S. L.
Sensors & Transducers 5 by IFSA Publishing, S. L. http://www.sensorsportal.com Low Energy Lossless Image Compression Algorithm for Wireless Sensor Network (LE-LICA) Amr M. Kishk, Nagy W. Messiha, Nawal
More informationImage Compression and its implementation in real life
Image Compression and its implementation in real life Shreyansh Tripathi, Vedant Bonde, Yatharth Rai Roll No. 11741, 11743, 11745 Cluster Innovation Centre University of Delhi Delhi 117 1 Declaration by
More informationColor Bayer CFA Image Compression using Adaptive Lifting Scheme and SPIHT with Huffman Coding Shreykumar G. Bhavsar 1 Viraj M.
IJSRD - International Journal for Scientific Research & Development Vol. 2, Issue 12, 2015 ISSN (online): 2321-0613 Color Bayer CFA Image Compression using Adaptive Lifting Scheme and SPIHT with Coding
More informationPractical applications of digital filters
News & Analysis Practical applications of digital filters David Zaucha, Texas Instruments, Dallas, Texas, USA 2/20/2003 01:12 AM EST Post a comment Tweet Share 16 0 Practical applications of digital filters
More informationPRECISION FOR 2-D DISCRETE WAVELET TRANSFORM PROCESSORS
PRECISION FOR 2-D DISCRETE WAVELET TRANSFORM PROCESSORS Michael Weeks Department of Computer Science Georgia State University Atlanta, GA 30303 E-mail: mweeks@cs.gsu.edu Abstract: The 2-D Discrete Wavelet
More informationInternational Journal of Advancedd Research in Biology, Ecology, Science and Technology (IJARBEST)
Gaussian Blur Removal in Digital Images A.Elakkiya 1, S.V.Ramyaa 2 PG Scholars, M.E. VLSI Design, SSN College of Engineering, Rajiv Gandhi Salai, Kalavakkam 1,2 Abstract In many imaging systems, the observed
More informationDIGITAL COMMUNICATION
DEPARTMENT OF ELECTRICAL &ELECTRONICS ENGINEERING DIGITAL COMMUNICATION Spring 00 Yrd. Doç. Dr. Burak Kelleci OUTLINE Quantization Pulse-Code Modulation THE QUANTIZATION PROCESS A continuous signal has
More informationJournal of mathematics and computer science 11 (2014),
Journal of mathematics and computer science 11 (2014), 137-146 Application of Unsharp Mask in Augmenting the Quality of Extracted Watermark in Spatial Domain Watermarking Saeed Amirgholipour 1 *,Ahmad
More informationChapter 2 Distributed Consensus Estimation of Wireless Sensor Networks
Chapter 2 Distributed Consensus Estimation of Wireless Sensor Networks Recently, consensus based distributed estimation has attracted considerable attention from various fields to estimate deterministic
More informationQUESTION BANK. SUBJECT CODE / Name: EC2301 DIGITAL COMMUNICATION UNIT 2
QUESTION BANK DEPARTMENT: ECE SEMESTER: V SUBJECT CODE / Name: EC2301 DIGITAL COMMUNICATION UNIT 2 BASEBAND FORMATTING TECHNIQUES 1. Why prefilterring done before sampling [AUC NOV/DEC 2010] The signal
More informationCSE 166: Image Processing. Overview. What is an image? Representing an image. What is image processing? History. Today
CSE 166: Image Processing Overview Image Processing CSE 166 Today Course overview Logistics Some mathematics Lectures will be boardwork and slides CSE 166, Fall 2016 2 What is an image? Representing an
More informationTri-mode dual level 3-D image compression over medical MRI images
Research Article International Journal of Advanced Computer Research, Vol 7(28) ISSN (Print): 2249-7277 ISSN (Online): 2277-7970 http://dx.doi.org/10.19101/ijacr.2017.728007 Tri-mode dual level 3-D image
More informationA Novel Approach for MRI Image De-noising and Resolution Enhancement
A Novel Approach for MRI Image De-noising and Resolution Enhancement 1 Pravin P. Shetti, 2 Prof. A. P. Patil 1 PG Student, 2 Assistant Professor Department of Electronics Engineering, Dr. J. J. Magdum
More informationVU Signal and Image Processing. Torsten Möller + Hrvoje Bogunović + Raphael Sahann
052600 VU Signal and Image Processing Torsten Möller + Hrvoje Bogunović + Raphael Sahann torsten.moeller@univie.ac.at hrvoje.bogunovic@meduniwien.ac.at raphael.sahann@univie.ac.at vda.cs.univie.ac.at/teaching/sip/17s/
More informationDigital Media. Lecture 4: Bitmapped images: Compression & Convolution Georgia Gwinnett College School of Science and Technology Dr.
Digital Media Lecture 4: Bitmapped images: Compression & Convolution Georgia Gwinnett College School of Science and Technology Dr. Mark Iken Bitmapped image compression Consider this image: With no compression...
More informationDIGITAL IMAGE PROCESSING Quiz exercises preparation for the midterm exam
DIGITAL IMAGE PROCESSING Quiz exercises preparation for the midterm exam In the following set of questions, there are, possibly, multiple correct answers (1, 2, 3 or 4). Mark the answers you consider correct.
More informationProf. Feng Liu. Fall /04/2018
Prof. Feng Liu Fall 2018 http://www.cs.pdx.edu/~fliu/courses/cs447/ 10/04/2018 1 Last Time Image file formats Color quantization 2 Today Dithering Signal Processing Homework 1 due today in class Homework
More informationA Novel Color Image Compression Algorithm Using the Human Visual Contrast Sensitivity Characteristics
PHOTONIC SENSORS / Vol. 7, No. 1, 17: 72 81 A Novel Color Image Compression Algorithm Using the Human Visual Contrast Sensitivity Characteristics Juncai YAO 1,2 and Guizhong LIU 1* 1 School of Electronic
More informationDigital Image Processing Question Bank UNIT -I
Digital Image Processing Question Bank UNIT -I 1) Describe in detail the elements of digital image processing system. & write note on Sampling and Quantization? 2) Write the Hadamard transform matrix Hn
More information