MPEG Video Transcoding in Compress Domain E-Book

Ankit Garg

• Herausgeber: BookRix
• Kategorie: Bildung
• Sprache: Englisch

Veröffentlichungsjahr: 2019

Beschreibung

Ankit Garg is Assistant Professor in Amity University, Haryana. He did M.tech (CSE) and pursuing P.hD from Uttarakhand Technical University dehradun. He has authored/co-authored more than 22 quality research publications in international journal and conferences. Beside this he has been part of more than 20 organisational bodies.

Leseprobe

Ankit Garg

MPEG Video Transcoding in Compress Domain

By Ankit Garg

Ankit Garg is Assistant Professor in Amity University, Haryana. He did M.tech (CSE) and pursuing P.hD from Uttarakhand Technical University dehradun. He has authored/co-authored more than 22 quality research publications in international journal and conferences. Beside this he has been part of more than 20 organisational bodies.BookRix GmbH & Co. KG81371 Munich

ABSTRACT

ABSTRACT 

The goal of transcoding is to process one standards-compliant video stream into another standards-compliant video stream that has properties better suited for a particular application. Most of still images and videos are stored in compressed domain in digital media. Various transcoding operations like splicing, downscaling and filtering can be easily performed in a spatial domain via de-compression and re-compression. But in general, transcoding of a compressed image or video in a compressed domain is much faster, more efficient and practical than that in spatial domain.

In my book I have purposed a novel approach to perform the transcoding operations by developing a number of transcoding algorithms in the Discrete Cosine Transform (DCT) domain, which exploits various properties of DCT, Discrete Fourier Transform (DFT) and their relationships. When an image or video is given in compressed domain, its transcoded image or video is also obtained in compressed domain. The purposed approach is computationally fast and has less space complexity, thus achieving high performance.

TABLE OF CONTENTS

CHAPTER 1 Introduction

          1.1 Introduction to Transcoding

          1.2 Organization of Book

CHAPTER 2 Problem Statement and MPEG Compression

            2.1 MPEG Transcoding Problem Statement

            2.2 Introduction to MPEG Video Compression

                        2.2.1 Filters

                        2.2.2 Color Space Conversion

                        2.2.3 Digitization

                        2.2.4 Scaling

                        2.2.5 MPEG Frames

                        2.2.6 Transforms

                        2.2.7 Quantization

                        2.2.8 Compaction Coding

            2.3 Transform Coding Algorithms

                        2.3.1 Intraframe Algorithms

                        2.3.2 InterFrame Algorithms

            2.4 Summary

CHAPTER 3 Super-Resolution

3.1 Introduction

            3.2 Problem Definition

            3.3 Initial Approach

                        3.3.1 Relation with Discrete Fourier Transform (DFT)

                                    3.3.1.1 1-Dimension Case

                              3.3.1.2 Extension to 2-Dimension

                        3.3.2 Experimental Results

                                    3.3.2.1 Image Reconstruction

                                    3.3.2.2 Comparison with Interpolation

            3.4 Improvement in DFT Approach

          3.5 Super-Resolution using Discrete Cosine Transform (DCT)

                        3.5.1 Relationships between DFT and DCT

                                    3.5.1.1 1-Dimension Case

                              3.5.1.2 Extension to 2-Dimension

            3.6 Improvement in DCT Approach

                        3.6.1 1-Dimension Case

                    3.6.2 Extension to 2-Dimension

                        3.6.3 Experimental Results

                                    3.6.3.1 Image Reconstruction

            3.7 L/M-fold Resizing of an Image.

            3.8 Summary

CHAPTER 4 Mapping Spatial Shifting into DCT Domain

            4.1 Motivation

            4.2 Existing Approach

            4.3 Approach Suggested

                        4.3.1 First Principles

                        4.3.2 Putting it all together.

                        4.3.3 Results for 8 x 8 Block.

            4.4 Generalization

                        4.4.1 For 2-Dimension Input

                        4.4.2 Experimental Results

            4.5 Summary

CHAPTER 5 Embedded Coring in MPEG Video Compression

            5.1 Introduction

            5.2 Coring Approach

            5.3 Experimental Results.

            5.4 Extension to MPEG

            5.5 Summary

CHAPTER 6 Future Scope

            6.1 Temporal Mode Conversion

            6.2 Splicing

            6.3 Reverse Play

            6.4 SummaryREFERENCES 

APPENDICES

Appendix A

Appendix B

Appendix C.

Appendix D.    

TABLE OF FIGURES

TABLE OF FIGURES

CHAPTER 1

            Fig 1.1 Transcoding - Media streaming over packets networks 

CHAPTER 2

             Fig 2.1 MPEG Transcoding: the naive solution (top) and the compressed-domain solution (bottom)

            Fig 2.2 MPEG Structures

            Fig 2.3.MPEG Frames: Display Order (top) and Coding Order (bottom)

 

CHAPTER 3

             Fig 3.1 Super-Resolution

            Fig 3.2 Original Sequence and two sub-sampled sequences

            Fig 3.3 Original Image

            Fig 3.4 4-Sub-sampled images

            Fig 3.5 Image Reconstruction using DFT

            Fig 3.6 Comparison with Interpolation

            Fig 3.7 Improved Approach

            Fig 3.8 Time domain approach for DCT to DFT conversion

            Fig 3.9 Original Image with 208 X 222

            Fig 3.10 Sub-sampled images with 104 X 111

            Fig 3.11 Image Reconstruction using DCT

 

CHAPTER 4

            Fig. 4.1 Original Image

            Fig. 4.2 Output Image

 

CHAPTER 5

            Fig 5.1 Coring functions g(.) where Z(w) is DCT coefficient

            Fig 5.2 (a) Original Image, (b) Corrupted Image

            Fig 5.3 (a) Soft-Thresholding, (b) Hard-Thresholding

 

CHAPTER 6

             Fig 6.1 Splicing: the naive solution (top) and our approach (bottom).

            Fig 6.2 Splicing: Proposed algorithm

            Fig 6.3 Reverse Play: the naive solution (top) and our approach (bottom)

            Fig 6.4 Reverse Play: Proposed algorithm

  

LIST OF ABBRIVIATIONS

Transcoding                            process of converting a file form one format to another

MPEG                                     Audio and video compression standards from ISO

Compression                           storing data in a format requiring less space

Resolution                              sharpness and clarity of an image

JPEG                                       lossy compression standard for images

Lossy                                      the signal after compression is different from the original signal due to lost information

DFT                                         Discrete Fourier Transform

CTFT                                      Continuous Time Fourier Transform

DTFT                                      Discrete Time Fourier Transform

DCT                                        Discrete Cosine Transform

Frame                                      one picture or "still" out of a video stream

Down-sampling                      having less number of points either in time or spatial domain

Super-resolution                     improving the temporal or spatial content

Sampling period                     regular time duration after which signal value is taken

Transform                               change from one form or medium to another

Filtering                                  removing specific frequency components in the signal

Interpolation                           filling in unknown values in a sequence by examining known values

Image-resizing                                    increasing or decreasing the resolution or no of pixels

Coring                                     removing noise from the image

Threshold                                a region making a boundary

Splicing                                  adding two video sequences

CHAPTER 1 INTRODUCTION

1.1 Introduction to Transcoding

With the expansion of digital media, digital images and videos are widely available for use and editing. Video compression algorithms are being used to compress digital video for a wide variety of applications, including video delivery over the internet, advanced television broadcasting, as well as video storage and editing. The performance of modern compression algorithms such as MPEG is quite impressive -- raw video data rates often can be reduced by factors of 15-80 without considerable loss in reconstructed video quality. However, the use of these compression algorithms often makes other processing tasks quite difficult. For example, many operations once considered simple, such as splicing and downscaling, are much more complicated when applied to compressed video streams.

The goal of transcoding is to process one standards-compliant video stream into another standards-compliant video stream that has properties better suited for a particular application. This is useful for a number of applications. For example, a video server transmitting video over the internet may be restricted by stringent bandwidth requirements. In this scenario, a high-quality compressed bit-stream may need to be transcoded to a lower-rate compressed bit-stream prior to transmission; this can be achieved by lowering the spatial or temporal resolution of the video or by re-quantizing the MPEG data. Another important problem that arises in visual communications is the need to create an enhanced-resolution video image sequence from a lower resolution input video stream (Fig 1.1).

                                                           Fig 1.1 Transcoding - Media streaming over packets networks

Some other application may require MPEG video streams to be transcoded into streams that facilitate video editing functionalities such as splicing or fast-forward and reverse play; this may involve removing the temporal dependencies in the coded data stream. Finally, in a video communication system, the transmitted video stream may be subject to harsh channel conditions resulting in data loss; in this instance it may be useful to create a standards-compliant video stream that is more robust to channel errors.

A simple method for transcoding is to perform all the operations in the spatial domain via de-compression and re-compression of original image. However, due to high computational cost and storage capacity, there have been great efforts in recent years to develop fast algorithms that perform various transcoding operations directly in the transform domain, thereby avoiding the need for de-compression and re-compression.