ALGORITHMS AND ARCHITECTURES FOR ALGORITHMS AND ARCHITECTURES FOR DISCRETE WAVELET TRANSFORM BASED VIDEO

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  • ALGORITHMS AND ARCHITECTURES FOR

    DISCRETE WAVELET TRANSFORM BASED

    VIDEO ENCODER

    Thesis Submitted in partial fulfillment for the

    Award of Degree

    DOCTOR OF PHILOSOPHY

    in

    Electrical and Electronics Engineering

    by

    SHRIRAM PARAMESHWAR HEGDE

    VINAYAKA MISSIONS UNIVERSITY SALEM, TAMILNADU, INDIA

    DECEMBER 2015

  • VINAYAKA MISSIONS UNIVERSITY

    Declaration

    I, Shriram Parameshwar Hegdedeclare that the thesis entitled

    “Algorithms and Architectures for Discrete Wavelet Transform Based

    Video Encoder’’ submitted by me for the Degree of Doctor of Philosophy

    is the record of work carried out by me during the period

    from January 2008 to December 2015 under the guidance of

    Dr S. Ramachandran and has not formed the basis for the award of any

    degree, diploma, associate ship, fellowship, titles in this or any other

    University or other similar institutions of higher learning.

    (SHRIRAM PARAMESHWAR HEGDE)

    Place: Bangalore

    Date: 21-06-2016

  • VINAYAKA MISSIONS UNIVERSITY

    Certificate by the Guide

    I, Dr S. Ramachandran certify that the thesis entitled

    “Algorithms and Architectures for Discrete Wavelet

    Transform based Video Encoder” submitted for the Degree

    Doctor of Philosophy by Mr. Shriram Parameshwar Hegde. The

    record of research work carried out by him during the period from

    January 2008 to December 2015 under my guidance and supervision

    and this work has not formed the basis for the award of any degree,

    diploma, associate-ship, fellowship or other titles in this University

    or any other university or Institution of higher learning.

    (Dr. S. Ramachandran)

    Place: Bangalore

    Date:21-06-2016

  • i

    ACKNOWLEDGEMENTS

    I would like to express my heartfelt thanks to the Chancellor

    and Dean (Research) of Vinayaka Missions University, Salem for

    their constant support and encouragement.

    I would like to express my heartfelt thanks to my Guide

    Dr S. Ramachandran for continuous and efficient mentoring. I would

    like to thank him for encouraging my research and for allowing me to

    grow as a researcher. His advice on both research as well as on my

    career has been priceless.

    A special thanks to Almighty and my family members. Words

    cannot express how grateful I am to all for their sacrifices made on my

    behalf. I would also like to thank all my friends and well-wishers who

    supported me and motivated me to strive towards my goal.

    I thank the Management, Principal, and my fellow colleagues at

    SDMIT, UJIRE and I feel fortunate to have used the R&D facilities of

    SJBIT, Bangalore and executing this work in such a rich intellectual

    climate comprising many brilliant Professionals. My special thanks are

    due to Mr Shailesh who had been a great source of Inspiration and

    Technical help, without whom this work could not have been completed

    to perfection.

    Shriram Parameshwar Hegde

  • ii

    ABSTRACT

    Image compression is of incredible significance in multimedia

    frameworks and applications on the grounds that it radically decreases

    bandwidth necessities for transmission and memory prerequisites for

    capacity. Albeit prior gauges for image compression were taking into

    account the Discrete Cosine Transform. Of late, Discrete Wavelet

    Transform has been observed to be more proficient for image coding than

    the DCT.

    In spite of enhancements in compression proficiency, wavelet image

    coders altogether expand memory utilization and many-sided quality when

    contrasted to DCT-based coders. A noteworthy explanation behind the

    high memory necessities is that the algorithm to wavelet transform requires

    the whole image to be in memory. Albeit a few proposition lessen the

    memory utilization, they show issues that thwart their implementation.

    Moreover, some wavelet image coders as SPIHT (which has turned into a

    benchmark for wavelet coding), constantly need to hold the whole image in

    memory. SPIHT can be considered very perplexing on the grounds that it

    performs bit-plane coding with different image checks.

  • iii

    In this work, we intend to diminish memory use and unpredictability

    in wavelet-based image and feature coding, while protecting pressure

    effectiveness. To this end, a 5/3 2D-DWT technique for the implementation

    has been realized to pack digital image for lessening the equipment

    prerequisite. Likewise, a novel SPIHT algorithm alongside DWT has also

    been realized for the image compression to lessen the space necessity

    and postponement time. At long last, a construction modeling for the

    feature compression utilizing DWT is introduced, which is perfect for the

    ongoing execution.

  • iv

    LIST OF ABBREVIATIONS

    MPEG Moving Picture Experts Group JPEG Joint Photographic Experts Group

    SPIHT Set Partitioning in Hierarchical Trees ISPIHT Inverse Set Partitioning in Hierarchical Trees ROI Region of Interest DCT Discrete Cosine Transform DWT Discrete Wavelet Transform IDWT Inverse Discrete Wavelet Transform WT Wavelet Transform CWT Continuous Wavelet Transform LIS List of Insignificant Sets LIP List of Insignificant Pixels LSP List of Significant Pixels PSNR Peak Signal to Noise Ratio MSE Mean SquaredError CR Compression Ratio FPGA Field Programmable Gate Array CODEC Compression/Decompression MATLAB Matrix Laboratory PNG Portable Network Graphics CALIC Context Based Adaptive Loss Less Image Codec GIF Graphic Interchange Format STFT Short Time Fourier Transform CDF Cohen-Daubechies-Feauveau EZT Embedded Zero Tree WCQT Wavelet Coded Quantization Transform VM Verification Model EBCOT Embedded Block Coding With Optimal Truncation

    RCT Reversible Colour Transform SIPO Serial In Parallel Out

    PISO Parallel In Serial Out

    VLSI Very Large Scale Integration

    BP Bit Parallel

    OBMC Over Lapping Block Motion Compensation

  • v

    EEWITA Energy Efficient Wavelet Image TransformAlgorithm

    SDVC Scalable Distributed Video Coding

    AVC Advanced Video Coding

    JSVM Joint Scalable Video Model

    BMA British Medical Association

    BMME Block Matching Motion Estimation

    TSS Three Step Search

    FSS Four Step Search

    NTSS New Three Step Search

    BBGDS Block Based GradientDescent Search

    DS Diamond Search

    CDS Cross Diamond Search

    VCL Video Coding Layer

    ITU International Telecommunication Union

    VCEG Video Coding Expert Group

    ISO/IEC International Organisation for Standardization/International Electro -Technical Commission

    SOM Self Organizing Map

    BPC Bit Plane Coder

    EDP Exchange DeliveryPoint

    APT Automatic Picture Transmission

    SAR Storage Aspect Ratio

    ETS Error Tolerance Scheme

    KLT Karhunen - Loeve Transform

    BWFBs Bi orthogonal Wavelet Filter Banks

    FIFO First In First Out

    EZW Embedded Zero Wavelet

    PIT Progressive Image Transmission

    VHDL Very high speed integrated circuit hardware description language

    HDTV High Definition Television

    NTSC National Television Sytem(s) Committee

    PAL Phase Alternation Line

    SECAM Sequential Colour And Memory

    FIR First Information Report

    ASCII American Standard Code for Information Interchange

  • vi

    LIST OF FIGURES

    Figure

    No

    Figure Name Page

    No

    1.1

    1.2

    1.

    Basic flow of Image Compression Technique

    Example of Mother Wavelet

    2

    1.2 Example of Mother Wavelet 11

    1.3 Example of Scaled Baby Wavelet 11

    1.4 Example of Translated Baby Wavelet

    12

    1.5 Dyadic Sampling

    16

    1.6 Subband Decomposition without Scaling Function

    16

    1.7 Subband Decomposition with Scaling Function 17

    1.8 Haar Family Wavelet 17

    1.9 DWT Analysis of Signal using Two-Channel Subband

    Coding

    17

    1.10

    Multiple Level DWT Analysis of Signal using Two-Channel subband coding

    18

  • vii

    Figure

    No

    Figure Name Page

    No

    1.11 DWT Synthesis of Signal using Two-Channel Subband

    Coding

    18

    1.12 CDF 5/3 analysis Wavelet 20

    1.13 CDF 5/3 Synthesis Wavelet 21

    1.14 CDF 7/9 analysis Wavelet 22

    1.15 CDF 7/9Synthesis Wavelet 22

    1.16 JPEG2000 block diagram

    25

    3.1 Result of Three Level 2D Wavelet Transform Operation on an Image

    76

    3.2 DWT Analysis and Synthesis Coding

    79

    3.3 The 2D-DWT analysis filter bank 80

    3.4 Proposed 1D-DWT Architecture 84

    3.5 Proposed 2D-DWT Architecture 85

    3.6 Image Output of 1D-DWT Block 87

    3.7 Image Output of