Lossy Video Compression Using Well Known Mathematical Functions and a Limited Set of Reference Values

Total Page:16

File Type:pdf, Size:1020Kb

Lossy Video Compression Using Well Known Mathematical Functions and a Limited Set of Reference Values Popular and Democratic Republic of Algeria Ministry of Higher Education and Scientific Research Colonel Ahmed Draia University of Adrar Faculty of Sciences and Technology Department of Mathematics and Computer Science A Thesis Presented to Fulfill the Partial Requirement for Master’s Degree in Computer Science Option: Networks and Intelligent Systems Title: Lossy Video compression using well known mathematical functions and a limited set of reference values Prepared by Miss. Leyla OULAD BENSAID Supervised by Dr. Mohammed OMARI Academic year 2016/2017 ABSTRACT Nowadays the amount of digital video applications is rapidly increasing. The amount of raw video data is very large which makes storing, processing, and transmitting video sequences very complex tasks. Furthermore, while the demand for enhanced user experience is growing, the sizes of devices capable of performing video processing operations are getting smaller. This further increases the practical limitations encountered when handling these large amounts of data, and makes research on video compression systems and standards very important. For this reason, many compression techniques have been proposed; some of these have been effective in some areas and failed in others. In this thesis we propose a new lossy video compression technique using well known mathematical functions and a limited set of reference values. Our approach is based on finding a compressed value whose corresponding pair of the corresponding mathematical function and reference value, aiming to produce shorter expressions compared to the regular form. In addition, we integrated an approach that utilizes binary search algorithm in order to efficiently find a better compressed value with shorter reduced form that does not harm the original image quality. The preliminary results of our method show promising results compared to other peer techniques. Keywords: Lossy video compression, video codec, mathematical function compression, compression ratios, PSNR, Video codec standards. ii Acknowledgements First, I thank Allah the Almighty for giving me courage, strength and patience to complete this modest work. I wish to express my profound gratitude to Dr. OMARI Mohammed, my Respected supervisor, for his excellent guidance, caring, patience, I ask Allah to bless him. My respect and gratitude to the jury members who gave me the honor of judging this work through their availability, observations and reports that have enabled me to enhance my scientific contribution. Thanks to all the teachers of our faculty of Sciences and Technology. I thank all those who participate. iii Dedicates I would like to dedicate this work To the spring that never stops giving… To my mother To the big heart... My dear father To the people who paved our way of science and knowledge All our distinguished teachers To every person who supported me in my studies. iv TABLE OF CONTENTS ABSTRACT ............................................................................................................................... ii ACKNOLEDGEMENTS .......................................................................................................... iii DEDICATION .......................................................................................................................... iv TABLE OF CONTENTS ........................................................................................................... v LIST OF FIGURES ................................................................................................................. viii LIST OF TABLES ..................................................................................................................... x GLOSSARY …………… ......................................................................................................... xi INTRODUCTION ...................................................................................................................... 1 Chapter1 : Introduction to video compression ............................................................... 3 1.1.Introduction .......................................................................................................................... 3 1.2.History of video compression: ............................................................................................. 3 1.3.Concepts and Definitions ..................................................................................................... 5 1.3.1.Frame rate ...................................................................................................................... 5 1.3.2.Frame dimensions .......................................................................................................... 6 1.3.3.Bit Rate (BR) ................................................................................................................. 6 1.3.4.Natural video scenes ...................................................................................................... 7 1.4.Capture ................................................................................................................................. 8 1.4.1.Spatial Sampling ............................................................................................................ 8 1.4.2.Temporal Sampling ....................................................................................................... 9 1.4.3.Frames and Fields ........................................................................................................ 11 1.5.Representation of colors: .................................................................................................... 12 1.5.1.RGB coding ................................................................................................................. 13 1.5.2.YUV Coding ................................................................................................................ 14 1.5.3.YIQ Color Space ........................................................................................................ 15 1.6.Video formats ..................................................................................................................... 17 1.7.Video standards .................................................................................................................. 19 1.8.Video compression: ............................................................................................................ 19 1.8.1.Lossy and lossless compression .................................................................................. 20 1.8.2.Why is video compression used? ................................................................................. 21 v 1.8.3.Image and Video Compression Standards ................................................................... 21 1.9.Video Quality Measure ...................................................................................................... 22 1.10.Explanation of File Formats ............................................................................................. 23 1.11.Conclusion ........................................................................................................................ 24 Chapter 2: Video compression techniques ..................................................................... 25 2.1.Introduction ........................................................................................................................ 25 2.2.Video Compression Techniques ......................................................................................... 25 2.3.Two basic standards: JPEG and MPEG ............................................................................. 26 2.4.The next step: H.264 .......................................................................................................... 27 2.5.An overview of video compression techniques .................................................................. 27 2.5.1.JPEG ............................................................................................................................ 27 2.5.2.Motion JPEG ............................................................................................................... 28 2.5.3.JPEG 2000 ................................................................................................................... 28 2.5.4.Motion JPEG 2000 ...................................................................................................... 29 2.5.5.H.261/ H.263 ............................................................................................................... 29 2.5.6.MPEG1 ........................................................................................................................ 30 2.5.7.MPEG-2 ....................................................................................................................... 31 2.5.8.MPEG-3 ....................................................................................................................... 32 2.5.9.MPEG-4 ....................................................................................................................... 32 2.5.10.H.264 ......................................................................................................................... 33 2.5.11.MPE G-7 .................................................................................................................... 33 2.5.12.MPE G-21 .................................................................................................................
Recommended publications
  • A Survey Paper on Different Speech Compression Techniques
    Vol-2 Issue-5 2016 IJARIIE-ISSN (O)-2395-4396 A Survey Paper on Different Speech Compression Techniques Kanawade Pramila.R1, Prof. Gundal Shital.S2 1 M.E. Electronics, Department of Electronics Engineering, Amrutvahini College of Engineering, Sangamner, Maharashtra, India. 2 HOD in Electronics Department, Department of Electronics Engineering , Amrutvahini College of Engineering, Sangamner, Maharashtra, India. ABSTRACT This paper describes the different types of speech compression techniques. Speech compression can be divided into two main types such as lossless and lossy compression. This survey paper has been written with the help of different types of Waveform-based speech compression, Parametric-based speech compression, Hybrid based speech compression etc. Compression is nothing but reducing size of data with considering memory size. Speech compression means voiced signal compress for different application such as high quality database of speech signals, multimedia applications, music database and internet applications. Today speech compression is very useful in our life. The main purpose or aim of speech compression is to compress any type of audio that is transfer over the communication channel, because of the limited channel bandwidth and data storage capacity and low bit rate. The use of lossless and lossy techniques for speech compression means that reduced the numbers of bits in the original information. By the use of lossless data compression there is no loss in the original information but while using lossy data compression technique some numbers of bits are loss. Keyword: - Bit rate, Compression, Waveform-based speech compression, Parametric-based speech compression, Hybrid based speech compression. 1. INTRODUCTION -1 Speech compression is use in the encoding system.
    [Show full text]
  • Completed Projects / Projets Terminés
    Completed Projects / Projets terminés New Standards — New Editions — Special Publications Please note that the following standards were developed by the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC), and have been adopted by the Canadian Standards Association. These standards are available in PDF format only. CAN/CSA-ISO/IEC 2593:02, 4th edition Information Technology–Telecommunications and Information Exchange Between Systems–34-Pole DTE/DCE Interface Connector Mateability Dimensions and Contact Number Assignments (Adopted ISO/IEC 2593:2000).................................... $85 CAN/CSA-ISO/IEC 7811-2:02, 3rd edition Identification Cards–Recording Technique–Part 2: Magnetic Stripe–Low Coercivity (Adopted ISO/IEC 7811-2:2001) .................................................................................... $95 CAN/CSA-ISO/IEC 8208:02, 4th edition Information Technology–Data Communications–X.25 Packet Layer Protocol for Data Terminal Equipment (Adopted ISO/IEC 8208:2000) ............................................ $220 CAN/CSA-ISO/IEC 8802-3:02, 2nd edition Information Technology–Telecommunications and Information Exchange Between Systems–Local and Metropolitan Area Networks–Specific Requirements–Part 3: Carrier Sense Multiple Access with Collision Detection (CSMA/CD) Access Method and Physical Layer (Adopted ISO/IEC 8802-3:2000/IEEE Std 802.3, 2000) ................. $460 CAN/CSA-ISO/IEC 9798-1:02, 2nd edition Information Technology–Security Techniques–Entity Authentication–Part
    [Show full text]
  • Quality Assessment for HEVC Encoded Videos: Study of Transmission and Encoding Errors
    Master Thesis Electrical Engineering November 2016 Quality Assessment for HEVC Encoded Videos: Study of Transmission and Encoding Errors Sohaib Ahmed Siddiqui Yousuf Hameed Ansari Published : Blekinge Tekniska Högskola URL: http//www.bth.se This thesis is submitted to Blekinge Institute of Technology in partial fullfillment of the requirements for the degree of Masters of Science in Electrical Engieering. Contact Information: Author(s): Yousuf Hameed Ansari E-mail: [email protected] Sohaib Ahmed Siddiqui E-mail: [email protected] Supervisor: Benny Lövström TISB, Blekinge Institute of Technology Muhammad Shahid TISB, Blekinge Institute of Technology External Supervisor: Muhammad Arslan Usman WENS, Kumoh National Institute of Technology Gumi, South Korea Examiner: Dr. Sven Johansson TISB, Blekinge Institute of Technology Published : Blekinge Tekniska Högskola URL: http//www.bth.se i Quality Assessment for HEVC Encoded Videos: Study of Transmission and Encoding Errors Abstract There is a demand for video quality measurements in modern video applications specifically in wireless and mobile communication. In real time video streaming it is experienced that the quality of video becomes low due to different factors such as encoder and transmission errors. HEVC/H.265 is considered as one of the promising codecs for compression of ultra- high definition videos. In this research, full reference based video quality assessment is performed. The raw format reference videos have been taken from Texas database to make test videos data set. The videos are encoded using HM9 reference software in HEVC format. Encoding errors has been set during the encoding process by adjusting the QP values. To introduce packet loss in the video, the real-time environment has been created.
    [Show full text]
  • Arxiv:2004.10531V1 [Cs.OH] 8 Apr 2020
    ROOT I/O compression improvements for HEP analysis Oksana Shadura1;∗ Brian Paul Bockelman2;∗∗ Philippe Canal3;∗∗∗ Danilo Piparo4;∗∗∗∗ and Zhe Zhang1;y 1University of Nebraska-Lincoln, 1400 R St, Lincoln, NE 68588, United States 2Morgridge Institute for Research, 330 N Orchard St, Madison, WI 53715, United States 3Fermilab, Kirk Road and Pine St, Batavia, IL 60510, United States 4CERN, Meyrin 1211, Geneve, Switzerland Abstract. We overview recent changes in the ROOT I/O system, increasing per- formance and enhancing it and improving its interaction with other data analy- sis ecosystems. Both the newly introduced compression algorithms, the much faster bulk I/O data path, and a few additional techniques have the potential to significantly to improve experiment’s software performance. The need for efficient lossless data compression has grown significantly as the amount of HEP data collected, transmitted, and stored has dramatically in- creased during the LHC era. While compression reduces storage space and, potentially, I/O bandwidth usage, it should not be applied blindly: there are sig- nificant trade-offs between the increased CPU cost for reading and writing files and the reduce storage space. 1 Introduction In the past years LHC experiments are commissioned and now manages about an exabyte of storage for analysis purposes, approximately half of which is used for archival purposes, and half is used for traditional disk storage. Meanwhile for HL-LHC storage requirements per year are expected to be increased by factor 10 [1]. arXiv:2004.10531v1 [cs.OH] 8 Apr 2020 Looking at these predictions, we would like to state that storage will remain one of the major cost drivers and at the same time the bottlenecks for HEP computing.
    [Show full text]
  • (L3) - Audio/Picture Coding
    Committee: (L3) - Audio/Picture Coding National Designation Title (Click here to purchase standards) ISO/IEC Document L3 INCITS/ISO/IEC 9281-1:1990:[R2013] Information technology - Picture Coding Methods - Part 1: Identification IS 9281-1:1990 INCITS/ISO/IEC 9281-2:1990:[R2013] Information technology - Picture Coding Methods - Part 2: Procedure for Registration IS 9281-2:1990 INCITS/ISO/IEC 9282-1:1988:[R2013] Information technology - Coded Representation of Computer Graphics Images - Part IS 9282-1:1988 1: Encoding principles for picture representation in a 7-bit or 8-bit environment :[] Information technology - Coding of Multimedia and Hypermedia Information - Part 7: IS 13522-7:2001 Interoperability and conformance testing for ISO/IEC 13522-5 (MHEG-7) :[] Information technology - Coding of Multimedia and Hypermedia Information - Part 5: IS 13522-5:1997 Support for Base-Level Interactive Applications (MHEG-5) :[] Information technology - Coding of Multimedia and Hypermedia Information - Part 3: IS 13522-3:1997 MHEG script interchange representation (MHEG-3) :[] Information technology - Coding of Multimedia and Hypermedia Information - Part 6: IS 13522-6:1998 Support for enhanced interactive applications (MHEG-6) :[] Information technology - Coding of Multimedia and Hypermedia Information - Part 8: IS 13522-8:2001 XML notation for ISO/IEC 13522-5 (MHEG-8) Created: 11/16/2014 Page 1 of 44 Committee: (L3) - Audio/Picture Coding National Designation Title (Click here to purchase standards) ISO/IEC Document :[] Information technology - Coding
    [Show full text]
  • JPEG and JPEG 2000
    JPEG and JPEG 2000 Past, present, and future Richard Clark Elysium Ltd, Crowborough, UK [email protected] Planned presentation Brief introduction JPEG – 25 years of standards… Shortfalls and issues Why JPEG 2000? JPEG 2000 – imaging architecture JPEG 2000 – what it is (should be!) Current activities New and continuing work… +44 1892 667411 - [email protected] Introductions Richard Clark – Working in technical standardisation since early 70’s – Fax, email, character coding (8859-1 is basis of HTML), image coding, multimedia – Elysium, set up in ’91 as SME innovator on the Web – Currently looks after JPEG web site, historical archive, some PR, some standards as editor (extensions to JPEG, JPEG-LS, MIME type RFC and software reference for JPEG 2000), HD Photo in JPEG, and the UK MPEG and JPEG committees – Plus some work that is actually funded……. +44 1892 667411 - [email protected] Elysium in Europe ACTS project – SPEAR – advanced JPEG tools ESPRIT project – Eurostill – consensus building on JPEG 2000 IST – Migrator 2000 – tool migration and feature exploitation of JPEG 2000 – 2KAN – JPEG 2000 advanced networking Plus some other involvement through CEN in cultural heritage and medical imaging, Interreg and others +44 1892 667411 - [email protected] 25 years of standards JPEG – Joint Photographic Experts Group, joint venture between ISO and CCITT (now ITU-T) Evolved from photo-videotex, character coding First meeting March 83 – JPEG proper started in July 86. 42nd meeting in Lausanne, next week… Attendance through national
    [Show full text]
  • Lossless Compression of Audio Data
    CHAPTER 12 Lossless Compression of Audio Data ROBERT C. MAHER OVERVIEW Lossless data compression of digital audio signals is useful when it is necessary to minimize the storage space or transmission bandwidth of audio data while still maintaining archival quality. Available techniques for lossless audio compression, or lossless audio packing, generally employ an adaptive waveform predictor with a variable-rate entropy coding of the residual, such as Huffman or Golomb-Rice coding. The amount of data compression can vary considerably from one audio waveform to another, but ratios of less than 3 are typical. Several freeware, shareware, and proprietary commercial lossless audio packing programs are available. 12.1 INTRODUCTION The Internet is increasingly being used as a means to deliver audio content to end-users for en­ tertainment, education, and commerce. It is clearly advantageous to minimize the time required to download an audio data file and the storage capacity required to hold it. Moreover, the expec­ tations of end-users with regard to signal quality, number of audio channels, meta-data such as song lyrics, and similar additional features provide incentives to compress the audio data. 12.1.1 Background In the past decade there have been significant breakthroughs in audio data compression using lossy perceptual coding [1]. These techniques lower the bit rate required to represent the signal by establishing perceptual error criteria, meaning that a model of human hearing perception is Copyright 2003. Elsevier Science (USA). 255 AU rights reserved. 256 PART III / APPLICATIONS used to guide the elimination of excess bits that can be either reconstructed (redundancy in the signal) orignored (inaudible components in the signal).
    [Show full text]
  • The H.264 Advanced Video Coding (AVC) Standard
    Whitepaper: The H.264 Advanced Video Coding (AVC) Standard What It Means to Web Camera Performance Introduction A new generation of webcams is hitting the market that makes video conferencing a more lifelike experience for users, thanks to adoption of the breakthrough H.264 standard. This white paper explains some of the key benefits of H.264 encoding and why cameras with this technology should be on the shopping list of every business. The Need for Compression Today, Internet connection rates average in the range of a few megabits per second. While VGA video requires 147 megabits per second (Mbps) of data, full high definition (HD) 1080p video requires almost one gigabit per second of data, as illustrated in Table 1. Table 1. Display Resolution Format Comparison Format Horizontal Pixels Vertical Lines Pixels Megabits per second (Mbps) QVGA 320 240 76,800 37 VGA 640 480 307,200 147 720p 1280 720 921,600 442 1080p 1920 1080 2,073,600 995 Video Compression Techniques Digital video streams, especially at high definition (HD) resolution, represent huge amounts of data. In order to achieve real-time HD resolution over typical Internet connection bandwidths, video compression is required. The amount of compression required to transmit 1080p video over a three megabits per second link is 332:1! Video compression techniques use mathematical algorithms to reduce the amount of data needed to transmit or store video. Lossless Compression Lossless compression changes how data is stored without resulting in any loss of information. Zip files are losslessly compressed so that when they are unzipped, the original files are recovered.
    [Show full text]
  • Understanding Compression of Geospatial Raster Imagery
    Understanding Compression of Geospatial Raster Imagery Document Overview This document was created for the North Carolina Geographic Information and Coordinating Council (GICC), http://ncgicc.com, by the GIS Technical Advisory Committee (TAC). Its purpose is to serve as a best practice or guidance document for GIS professionals that are compressing raster images. This document only addresses compressing geospatial raster data and specifically aerial or orthorectified imagery. It does not address compressing LiDAR data. Compression Overview Compression is the process of making data more compact so it occupies less disk storage space. The primary benefit of compressing raster data is reduction in file size. An added benefit is greatly improved performance over a network, because the user is transferring less data from a server to an application; however, compressed data must be decompressed to display in GIS software. The result may be slower raster display in GIS software than data that is not compressed. Compressed data can also increase CPU requirements on the server or desktop. Glossary of Common Terms Raster is a spatial data model made of rows and columns of cells. Each cell contains an attribute value identifying its color and location coordinate. Geospatial raster data like satellite images and aerial photographs are typically larger on average than vector data (predominately points, lines, or polygons). Compression is the process of making a (raster) file smaller while preserving all or most of the data it contains. Imagery compression enables storage of more data (image files) on a disk than if they were uncompressed. Compression ratio is the amount or degree of reduction of an image's file size.
    [Show full text]
  • File Format Guidelines for Management and Long-Term Retention of Electronic Records
    FILE FORMAT GUIDELINES FOR MANAGEMENT AND LONG-TERM RETENTION OF ELECTRONIC RECORDS 9/10/2012 State Archives of North Carolina File Format Guidelines for Management and Long-Term Retention of Electronic records Table of Contents 1. GUIDELINES AND RECOMMENDATIONS .................................................................................. 3 2. DESCRIPTION OF FORMATS RECOMMENDED FOR LONG-TERM RETENTION ......................... 7 2.1 Word Processing Documents ...................................................................................................................... 7 2.1.1 PDF/A-1a (.pdf) (ISO 19005-1 compliant PDF/A) ........................................................................ 7 2.1.2 OpenDocument Text (.odt) ................................................................................................................... 3 2.1.3 Special Note on Google Docs™ .......................................................................................................... 4 2.2 Plain Text Documents ................................................................................................................................... 5 2.2.1 Plain Text (.txt) US-ASCII or UTF-8 encoding ................................................................................... 6 2.2.2 Comma-separated file (.csv) US-ASCII or UTF-8 encoding ........................................................... 7 2.2.3 Tab-delimited file (.txt) US-ASCII or UTF-8 encoding .................................................................... 8 2.3
    [Show full text]
  • Lossy Audio Compression Identification
    2018 26th European Signal Processing Conference (EUSIPCO) Lossy Audio Compression Identification Bongjun Kim Zafar Rafii Northwestern University Gracenote Evanston, USA Emeryville, USA [email protected] zafar.rafi[email protected] Abstract—We propose a system which can estimate from an compression parameters from an audio signal, based on AAC, audio recording that has previously undergone lossy compression was presented in [3]. The first implementation of that work, the parameters used for the encoding, and therefore identify the based on MP3, was then proposed in [4]. The idea was to corresponding lossy coding format. The system analyzes the audio signal and searches for the compression parameters and framing search for the compression parameters and framing conditions conditions which match those used for the encoding. In particular, which match those used for the encoding, by measuring traces we propose a new metric for measuring traces of compression of compression in the audio signal, which typically correspond which is robust to variations in the audio content and a new to time-frequency coefficients quantized to zero. method for combining the estimates from multiple audio blocks The first work to investigate alterations, such as deletion, in- which can refine the results. We evaluated this system with audio excerpts from songs and movies, compressed into various coding sertion, or substitution, in audio signals which have undergone formats, using different bit rates, and captured digitally as well lossy compression, namely MP3, was presented in [5]. The as through analog transfer. Results showed that our system can idea was to measure traces of compression in the signal along identify the correct format in almost all cases, even at high bit time and detect discontinuities in the estimated framing.
    [Show full text]
  • Comparison of Image Compressions: Analog Transformations P
    Proceedings Comparison of Image Compressions: Analog † Transformations P Jose Balsa P CITIC Research Center, Universidade da Coruña (University of A Coruña), 15071 A Coruña, Spain; [email protected] † Presented at the 3rd XoveTIC Conference, A Coruña, Spain, 8–9 October 2020. Published: 21 August 2020 Abstract: A comparison between the four most used transforms, the discrete Fourier transform (DFT), discrete cosine transform (DCT), the Walsh–Hadamard transform (WHT) and the Haar- wavelet transform (DWT), for the transmission of analog images, varying their compression and comparing their quality, is presented. Additionally, performance tests are done for different levels of white Gaussian additive noise. Keywords: analog image transformation; analog image compression; analog image quality 1. Introduction Digitized image coding systems employ reversible mathematical transformations. These transformations change values and function domains in order to rearrange information in a way that condenses information important to human vision [1]. In the new domain, it is possible to filter out relevant information and discard information that is irrelevant or of lesser importance for image quality [2]. Both digital and analog systems use the same transformations in source coding. Some examples of digital systems that employ these transformations are JPEG, M-JPEG, JPEG2000, MPEG- 1, 2, 3 and 4, DV and HDV, among others. Although digital systems after transformation and filtering make use of digital lossless compression techniques, such as Huffman. In this work, we aim to make a comparison of the most commonly used transformations in state- of-the-art image compression systems. Typically, the transformations used to compress analog images work either on the entire image or on regions of the image.
    [Show full text]