VIDEO DISTORTION MEASUREMENT USING PSNR IN WAVELET … Distortion Measurement Using PSNR In...Satu...

VIDEO DISTORTION MEASUREMENT USING PSNR IN WAVELET DOMAIN

MOK YUNG LENG

Bachelor of Engineering with Honors (Electronics & Computer Engineering)

2009/2010

UNIVERSITI MALAYSIA SARAWAK

R13a

BORANG PENGESAHAN STATUS TESIS Judul: VIDEO DISTORTION MEASUREMENT USING PSNR IN WAVELET DOMAIN

SESI PENGAJIAN: 2009/2010 Saya MOK YUNG LENG

(HURUF BESAR)

mengaku membenarkan tesis * ini disimpan di Pusat Khidmat Maklumat Akademik, Universiti Malaysia Sarawak dengan syarat-syarat kegunaan seperti berikut:

1. Tesis adalah hakmilik Universiti Malaysia Sarawak. 2. Pusat Khidmat Maklumat Akademik, Universiti Malaysia Sarawak dibenarkan membuat salinan untuk

tujuan pengajian sahaja. 3. Membuat pendigitan untuk membangunkan Pangkalan Data Kandungan Tempatan. 4. Pusat Khidmat Maklumat Akademik, Universiti Malaysia Sarawak dibenarkan membuat salinan tesis ini

sebagai bahan pertukaran antara institusi pengajian tinggi. 5. ** Sila tandakan ( ) di kotak yang berkenaan

SULIT (Mengandungi maklumat yang berdarjah keselamatan atau kepentingan Malaysia seperti yang termaktub di dalam AKTA RAHSIA RASMI 1972). TERHAD (Mengandungi maklumat TERHAD yang telah ditentukan oleh organisasi/ badan di mana penyelidikan dijalankan). TIDAK TERHAD Disahkan oleh (TANDATANGAN PENULIS) (TANDATANGAN PENYELIA) Alamat tetap: 5, JLN ANGGERIK VANILLA 31/98Q, KOTA KEMUNING, 40460 SHAH ALAM, SELANGOR IR. DAVID BONG BOON LIANG Nama Penyelia

Tarikh: 10 APRIL 2010 Tarikh: 12 APRIL 2010

CATATAN * Tesis dimaksudkan sebagai tesis bagi Ijazah Doktor Falsafah, Sarjana dan Sarjana Muda. ** Jika tesis ini SULIT atau TERHAD, sila lampirkan surat daripada pihak berkuasa/organisasi

berkenaan dengan menyatakan sekali sebab dan tempoh tesis ini perlu dikelaskan sebagai SULIT dan TERHAD.

This Final Year Project attached here:

Title : Video Distortion Measurement Using PSNR In Wavelet

Domain

Student Name : Mok Yung Leng

Matric No : 16744

has been read and approved by:

__________________ _________________

Ir. David Bong Boon Liang Date

(Supervisor)

Video Distortion Measurement Using PSNR In Wavelet Domain

MOK YUNG LENG

This project is submitted in partial fulfillment of The requirements for the degree of Bachelor of Engineering with Honors

(Electronics and Computer Engineering)

Faculty of Engineering UNIVERSITI MALAYSIA SARAWAK

2009/2010

Dedicated to Mom, my friends and my family

ACKNOWLEDGEMENT

I would like take the opportunity to thank my supervisor, Ir. David Bong for his

encouragement and support, as well as his comments, suggestions and advice on the

course of developing this project. With his involvement in this project, I am able to

complete the project within the scheduled time.

I would also like to give credit to my beloved friends and family, who gave

support to me throughout the years in the University, both financially and in the form

of moral support. Without them, it would not be easy to go through my rough times

along the four years in my university life.

I am also grateful to UNIMAS and the Engineering Faculty for giving me the

chance to receive my tertiary education here.

Finally, I would also like to express my gratitude to in the individuals who

directly or indirectly helped me in the development of this project.

i

ABSTRAK

Pada era digital, teknologi imej digital semakin maju. Oleh itu, algoritma

pengekodan video yang berprestasi baik penting untuk menghasilkan video yang

berkualiti tinggi. Analisis kualiti video objektif dapat menambahbaikan algoritma

pengekodan video. Satu cara ukuran herotan video yang baru akan dicadangkan

dalam tesis ini. Ukuran herotan ini adalah ditujukan kepada video dalam domain

wavelet. Video yang diujikan dalam projek ini adalah daripada video yang diperolehi

dalam pangkalan data video “Laboratory for Image and Video Engineering” (LIVE).

Wavelet Cohen-Daubechies-Feauveau (CDF) 9/7 dalam 2D akan diapplikasikan

dalam semua imej dalam semua video yang akan diuji. Ukuran objektif yang

digunakan dalam projek ini adalah “Peak Signal-to-Noise Ratio” (PSNR). Projek ini

akan membuat taksiran dengan mengunakan PSNR sebagai ukuran objektif dalam

perbezaan antara video rujukan dan video yang mempunyai herotan dalam domain

wavelet. Satu skor keseluruhan untuk video yang mempunyai herotan akan

ditentukan daripada analisis ini. Dalam analsis ini, nilai-nilai PSNR video dalam

domain wavelet juga akan dibandingkan dengan nilai-nilai PSNR video dalam

domain ruangan. Prestasi, keutuhan, dan ketekalan skema ukuran herotan yang

dicadangkan ini juga dianalisa dengan membuat perbandingan dengan nilai-nilai

PSNR video dalam domain ruangan. Perisian MATLAB digunakan untuk

mendapatkan nilai-nilai PSNR. Perisian Microsoft Excel digunakan untuk analisis

nilai-nilai PSNR.

ii

ABSTRACT

With the advancement of digital imaging, video coding algorithms that has

good performance is important for producing videos in high quality. Objective video

quality analysis can improve the video coding algorithms. In this thesis, a new

objective method for distortion measurement of videos is proposed. The distortion

measurement is based on videos in wavelet domain. The test videos used in the

project are test videos provided by Laboratory for Image and Video Engineering

(LIVE) video database. 2D Cohen-Daubechies-Feauveau (CDF) 9/7 wavelet is

applied to the video frames. The objective measurement used in this project is Peak

Signal-to-Noise Ratio (PSNR). The project calculates the differences of the reference

video and the distorted video in wavelet domain, by implementing PSNR values as

the objective measurement. An overall PSNR score for a distorted video is also

determined from the analysis. PSNR values of the video in wavelet domain are

compared to the PSNR values of the videos in spatial domain. Performance,

reliability, and consistency of the proposed video distortion measurement scheme are

also analysed in this thesis by comparison to the PSNR values of videos in spatial

domain. The PSNR values are calculated using MATLAB and the values are

exported to Microsoft Excel to perform analysis.

iii

TABLE OF CONTENTS

CONTENT PAGES

ACKNOWLEDGEMENT

ABSTRAK i

ABSTRACT ii

TABLE OF CONTENTS iii

LIST OF TABLES ix

LIST OF FIGURES x

ABBREVIATIONS xii

CHAPTER 1 INTRODUCTION

1.1 Introduction 1

1.2 Problem Statement 3

1.3 Project Objectives 4

1.4 Project Scope 5

1.5 Project Outline 5

CHAPTER 2 LITERATURE REVIEW

2.1 Overview 7

2.2 Types of distortions in digital videos 7

iv

2.2.1 Blocking effect 8

2.2.2 Blurring 9

2.2.3 Colour bleeding 10

2.2.4 Posterisation 11

2.2.5 Ringing effect 12

2.2.6 Mosquito noise 13

2.2.7 Ghosting 13

2.2.8 Random noise 14

2.2.9 Unstableness 15

2.2.10 Jerkiness 16

2.3 Full-reference (FR), no-reference (NR), and reduced-reference (RR)

video quality assessment 17

2.4 MOS, MSE and PSNR 18

2.5 Wavelet transform 21

2.5.1 CDF 9/7 wavelet transform 23

2.5.2 Construction of CDF 9/7 wavelets 26

CHAPTER 3 METHODOLOGY

3.1 Overview 29

3.2 Video distortion measurement in wavelet domain 31

3.3 Video distortion measurement in spatial domain 35

3.4 MATLAB 37

v

CHAPTER 4 RESULTS, ANALYSIS AND DISCUSSIONS

4.1 Overview 40

4.2 Analysis on PSNR values of videos in wavelet domain 41

4.2.1 Discussion on the analysis on PSNR values of videos

in wavelet domain 46

4.3 Analysis on PSNR values of videos in spatial domain 47

4.3.1 Discussion on the analysis on PSNR values of videos

in spatial domain 53

4.4 Discussion 54

CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS

5.1 Overview 56

5.2 Conclusion 56

5.3 Recommendations 58

REFERENCES 59

APPENDIX A: MATLAB source codes 62

APPENDIX B: PSNR values of video frames of bs2 to bs16


APPENDIX C: PSNR values of video frames of mc2 to mc16


APPENDIX D: PSNR values of video frames of pa2 to pa16


vi

APPENDIX E: PSNR values of video frames of pr2 to pr16


APPENDIX F: PSNR values of video frames of rb2 to rb16


APPENDIX G: PSNR values of video frames of bs2 to bs16


APPENDIX H: PSNR values of video frames of mc2 to mc16


APPENDIX I: PSNR values of video frames of pa2 to pa16


APPENDIX J: PSNR values of video frames of pr2 to pr16


APPENDIX K: PSNR values of video frames of rbr2 to rb16


APPENDIX L: Line graph of PSNR values of video frames in

bs2 to bs16 in wavelet domain 123

APPENDIX M: Line graph of PSNR values of video frames in

mc2 to mc16 in wavelet domain 124

APPENDIX N: Line graph of PSNR values of video frames in

pa2 to pa16 in wavelet domain 125

APPENDIX O: Line graph of PSNR values of video frames in

pr2 to pr16 in wavelet domain 126

APPENDIX P: Line graph of PSNR values of video frames in

pr2 to pr16 in wavelet domain 127

vii

APPENDIX Q: Line graph of PSNR values of video frames in

bs2 to bs16 in spatial domain 128

APPENDIX R: Line graph of PSNR values of video frames in

mc2 to mc16 in spatial domain 129

APPENDIX S: Line graph of PSNR values of video frames in

pa2 to pa16 in spatial domain 130

APPENDIX T: Line graph of PSNR values of video frames in

pr2 to pr16 in spatial domain 131

APPENDIX U: Line graph of PSNR values of video frames in

rb2 to rb16 in spatial domain 132

APPENDIX V: Comparison of PSNR values of video frames in

bs2 to bs16 in wavelet domain to PSNR values of

video frames in bs2 to bs 16 in spatial domain 133

APPENDIX W: Difference of PSNR values of video frames in

bs2 to bs16 in wavelet domain to PSNR values of

video frames in bs2 to bs 16 in spatial domain

and the percentage of difference 148

APPENDIX X: Correlation of mean PSNR, median PSNR,

RMS PSNR and DMOS in video sequence “bs” 156

APPENDIX Y: Correlation of mean PSNR, median PSNR,

RMS PSNR and DMOS in video sequence “mc” 157

APPENDIX Z: Correlation of mean PSNR, median PSNR,

RMS PSNR and DMOS in video sequence “pa” 159

APPENDIX AA: Correlation of mean PSNR, median PSNR,

RMS PSNR and DMOS in video sequence “pr” 160

viii

APPENDIX AB: Correlation of mean PSNR, median PSNR,

RMS PSNR and DMOS in video sequence “rb” 162

APPENDIX AC: Spatial RMS vs. Wavelet RMS of PSNR

values of bs2 – bs16 164

APPENDIX AD: Spatial RMS vs. Wavelet RMS of PSNR

values of mc2 – mc16 164

APPENDIX AE: Spatial RMS vs. Wavelet RMS of PSNR

values of pa2 – pa16 165

APPENDIX AF: Spatial RMS vs. Wavelet RMS of PSNR

values of pr2 – pr16 165

APPENDIX AG: Spatial RMS vs. Wavelet RMS of PSNR

values of rb2 – rb16 166

ix

LIST OF TABLES

TABLE PAGES

3.1 Number of frames and frame rate of the test videos 31

4.1 DMOS, Mean PSNR, RMS PSNR, and Median PSNR of bs2 – bs16 43

4.2 Spatial RMS vs. Wavelet RMS of PSNR values of bs2 – bs16 51

x

LIST OF FIGURES

FIGURE PAGE

2.1 Lena reference image 8

2.2 Blocking effect 8

2.3 Blurring 9

2.4 Lena reference image in colour 10

2.5 Colour bleeding 10

2.6 Posterisation 11

2.7 Cropped Lena reference image 12

2.8 Ringing effect 12

2.9 Random noise 14

2.10 Unstableness 15

2.11 Jerkiness 16

2.12 Two level 2D wavelet transform 23

2.13 Lifting algorithm for forward wavelet transform 24

2.14 Lifting algorithm for inverse wavelet transform 25

3.1 Flow chart of the video distortion measurement in wavelet domain 32

3.2 Three-scale wavelet decomposition 34

3.3 Flow chart of the video distortion measurement in spatial domain 36

3.4 Layout of the M-file editor 39

xi

4.1 PSNR values of video frames of bs1 vs. bs2-bs16 in

wavelet domain 42

4.2 Correlation of mean, median and RMS PSNR in

video sequence “bs” 44

4.3 Correlation of DMOS to mean, median and RMS PSNR

in video sequence “bs” 45

4.4 PSNR values of video frames of bs1 vs. bs2-bs16 in

spatial domain 48

4.5 Wavelet PSNR vs. spatial PSNR (bs2) 49

4.6 Difference and percentage of difference of wavelet PSNR

values to spatial PSNR values. 50

4.7 Spatial RMS vs. Wavelet RMS of PSNR values of bs2 – bs16 52

xii

ABBREVIATIONS

LIST OF NOTATIONS PSNR LIVE

Peak signal-to-noise ratio Laboratory for Image and Video Engineering

MPEG Motion Picture Experts Group FR Full-reference NR No-reference RR Reduced-reference MOS Mean opinion score MSE DMOS CDF

Mean squared error Difference Mean Opinion Score Cohen-Daubechies-Feauveau

IP AVI

Internet Protocol Audio Video Interleave

JPEG RGB YCbCr RMS

Joint Photographic Experts Group Red, Green, Blue Luminance, Blue difference, Red difference Root Mean Square

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CHAPTER 1

INTRODUCTION

1.1 Introduction

Video is the technology of capturing and recording a sequence of still images

representing scenes in motion using electronic devices like digital camera and

camcorders. Video also involves in processing, storing, transmitting, and

reconstructing such sequences of images [1].

Videos are prone to distortions. Distortions reduce the quality of a video. The

first type of distortion is introduced at the video acquisition stage. This is due to the

limitations of camera devices. Such distortions are introduced by camera optics,

sensor noise, colour calibration, exposure control etc [2]. The second type of

distortion is caused by video processing and transmission. Raw video occupies large

bandwidth, thus it must be compressed using different video compression schemes

before storage or transmission for better efficiency [3]. The compressed video

generally has a certain degree of distortions or loss of quality compared to the raw

2

video. When a video is transmitted over a channel, bit errors will occur and this also

introduce distortions to the transmitted video.

With the technology advancement in electronics and digital imaging, many

digital video coding techniques are implemented in different digital video coding

products. These products, which cover a broad range of applications, have different

quality and bandwidth requirements. Thus, it has become eventually more important

to develop video quality/distortion measurement techniques that can help to evaluate,

to compare and to improve the video coding techniques and products that provide

effective and high quality digital video services. [4]

To measure the quality of a video, researchers use two major methods of video

quality analysis. The first method is called the subjective video quality assessment.

This method evaluates the quality of a video by seeking opinion from human

observers [5]. However, this method is not practical in application because there are

a lot of videos in the real world and cannot be evaluated one by one. The other

reason is that researchers want to incorporate such quality measurement techniques

into algorithms that can be used to process videos, thus further enhance the

efficiency of the process to achieve a better quality of video with a given set of

resources [2].

As an alternative, researchers look into a more efficient method of video quality

analysis, namely objective video quality assessment. The purpose of objective video

quality analysis is to develop quantitative measures that can predict apparent video

quality by using a computer or other electronic devices [2]. Due to this property,

objective video quality assessment can be incorporated into different video

3

processing algorithms; that can improve the output of a processed video if such a

technique is used.

1.2 Problem Statement

Objective video assessment scheme is useful as it can be incorporated into

different video processing algorithms to improve the output of a processed video. By

implementing distortion measurement in a video processing algorithm, a processed

video can be further enhanced to give a higher quality output of videos. Hence, an

objective video assessment scheme is very much needed in the field of digital

imaging.

As of today, there is no standardised objective assessment scheme accepted for

measuring distortion. Many researchers had studied and proposed many different

objective assessment schemes, but none is accepted as a standard.

The aim of this project is to develop an objective assessment scheme to measure

distortion in videos in wavelet domain. This method utilizes full-reference method

and will use an unprocessed video as a reference to measure the degree of distortion

of the distorted videos. Wavelet transform is a popular method used for image/video

compression and analysis, and is used in JPEG2000 compression standard.

4

1.3 Project Objectives

The objectives of the project are:

I. To evaluate and to compare different existing video distortion measurement

techniques.

There are many types of video distortion measurement techniques available

currently. For this project, two different techniques are studied and compared

thoroughly. A suitable technique is used to apply in the distortion measurement of

the video.

II. To develop an assessment scheme and apply the techniques in 2-D wavelet

domain.

Distortion measurement in 2-D wavelet domain is chosen as the suitable

technique for the video distortion measurement. An assessment scheme or a

methodology is developed to implement such technique into the measurement.

III. To implement statistical analysis in the distortion measurement of the video.

Peak signal-to-noise ration (PSNR) is used as a statistical analysis for this

project. PSNR is a popular and widely accepted objective measurement due to their

easy-to-calculate features.

5

1.4 Project Scope

This project is focused on the distortion measurement of videos by measuring

the distortion introduced in the processed video in wavelet domain. The distortions

measured are distortions that were introduced in the compression and transmission

process of the videos. Wavelet transform is a popular method used for video analysis

and compression. Full-reference method is also used to make direct comparison

between a reference video and a processed video. PSNR is used as statistical analysis

for the project. MATLAB is used as a programming tool for the computation of

algorithms required for the distortion measurement by implementing the suitable

toolboxes available in MATLAB. Five test videos from Laboratory for Image and

Video Engineering (LIVE) Video Quality Database, provided by University of Texas

are used for analysis in this project.

1.5 Project Outlines

This thesis covers the details of processes involved in developing an assessment

scheme for measuring distortion in videos. The thesis is divided into five main

chapters; introduction, literature review, methodology, results, analysis and

discussions, conclusion and recommendations. Brief description for each chapter is

as below:

Chapter 1: Introduction

This chapter briefly describes the background of the project title, problem

statements and the objectives of the project, as well as the scope of the project.

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Chapter 2: Literature Review

This chapter is basically a summary of the researches done to gain knowledge

and information for the purpose of developing the project. Literature review from

different sources such as journals, books, internet sources and conference papers are

compiled and summarized in this chapter.

Chapter 3: Methodology

This chapter focuses on the proposed methodology for this. The methodology

gives an overall idea on how the assessment scheme is implemented using PSNR in

wavelet domain.

Chapter 4: Results, Analysis and Discussion

This chapter contains the experiment results, analysis and discussion of the

result. This chapter also discusses problems that occurred along the development of

the project.

Chapter 5: Conclusion and Recommendations

This chapter is the summary of the overall findings of the project. Future

implementations and suggestions for further improvement for the project are also

covered in this chapter.

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