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Image and Video Processing Jan Biemond Delft University of Technology Russell M.. Mersereau Georgia Institute of Technology 51 Image Processing Fundamentals Ian T.. Murat Tekalp Introduc

Image and Video

Processing

Jan Biemond

Delft University of Technology

Russell M Mersereau

Georgia Institute of Technology

51 Image Processing Fundamentals Ian T Young, Jan J Gerbrands, and Lucas J van Vliet

Introduction •Digital Image Definitions•Tools•Perception•Image Sampling•Noise•Cameras

•Displays•Algorithms•Techniques•Acknowledgments

52 Still Image Compression Tor A Ramstad

Introduction •Signal Decomposition•Quantization and Coding Strategies•Frequency Domain

Coders •Fractal Coding•Color Coding

53 Image and Video Restoration A Murat Tekalp

Introduction •Modeling•Model Parameter Estimation•Intra-Frame Restoration•Multiframe

Restoration and Superresolution •Conclusion

54 Video Scanning Format Conversion and Motion Estimation Gerard de Haan

Introduction •Conversion vs Standardization•Problems with Linear Sampling Rate Conversion

Applied to Video Signals •Alternatives for Sampling Rate Conversion Theory•Motion Estimation

•Motion Estimation and Scanning Format Conversion

55 Video Sequence Compression Osama Al-Shaykh, Ralph Neff, David Taubman, and

Avideh Zakhor

Introduction •Motion Compensated Video Coding•Desirable Features•Standards

56 Digital Television Kou-Hu Tzou

Introduction •EDTV/HDTV Standards•Hybrid Analog/Digital Systems•Error Protection and

Concealment •Terrestrial Broadcasting•Satellite Transmission•ATM Transmission of Video

57 Stereoscopic Image Processing Reginald L Lagendijk, Ruggero E.H Franich, and Emile

A Hendriks

Introduction •Acquisition and Display of Stereoscopic Images•Disparity Estimation•

Compres-sion of Stereoscopic Images •Intermediate Viewpoint Interpolation

58 A Survey of Image Processing Software and Image Databases Stanley J Reeves

Image Processing Software •Image Databases

59 VLSI Architectures for Image Communications P Pirsch and W Gehrke

Introduction •Recent Coding Schemes•Architectural Alternatives•Efficiency Estimation of

Al-ternative VLSI Implementations •Dedicated Architectures•Programmable Architectures•

Con-clusion

I MAGE AND VIDEO SIGNAL PROCESSING is quite different from other forms of signal

pro-cessing for a variety of reasons The most obvious difference lies in the fact that these signals are two or three dimensional This means that some familiar techniques used for processing one-dimensional signals, for example, those that require factorization of polynomials, have to be abandoned Other techniques for filtering, sampling, and transform computation have to be mod-ified Even more compromises have to be made, however, because of the signals’ size Images and sequences of images can be huge For example, processing sequences of color images each of which contains 780 rows and 1024 columns at a frame rate of 30 frames per second requires a data rate

of 72 megabytes per second Successful image processing techniques reward careful attention to problem requirements, algorithmic complexity, and machine architecture The past decade has been particularly exciting as each new wave of faster computing hardware has opened the door to new applications This is a trend that will likely continue for some time

The following chapters, written by experts in their fields, highlight the state-of-the-art in several aspects of image and video processing The range of topics is quite broad While it includes some discussions of techniques that go back more than a decade, the emphasis is on current practice There

is some danger in this, because the field is changing very rapidly, but, on the other hand, many of the concepts on which these current techniques are based should be around for some time

Chapter 51 is a very long and thorough discussion of image processing fundamentals For a novice to the field, this material is important for a complete understanding It discusses the basics

of how images differ from other types of signals and how the limitations of cameras, displays, and the human visual system affect the kinds of processing that can be done It also defines the basic theory of multidimensional digital signal processing, particularly with respect to how linear and nonlinear filtering, transform computation, and sampling are generalized from the one-dimensional case Other topics treated include statistical models for images, models for recording distortions, histogram-based methods for image processing, and image segmentation

Probably the most visible image processing is occurring in the development of standards for image and video compression JPEG, MPEG, and digital television are all highly visible success stories Chapter 52 looks at methods for still image compression including JPEG, wavelet, and fractal coders Image compression is successful because image samples are spatially correlated with their neighbors Operators such as the discrete cosine transform (DCT) largely remove this correlation and capture the essence of an image block in a few parameters that can be quantized and transmitted The transform domain also enables these coders to exploit limitations in the human visual system Chapters 55 and

56 extend these approaches to video and television compression, respectively Video compression achieves significant additional compression gains by exploiting the temporal redundancy that is present in video sequences This is done by using simple models for modeling object motion within a scene, using these models to predict the current frame, and then encoding only the model parameters and the quantized prediction errors

Images are often distorted when they are recorded This might be caused by out-of-focus optics, motion blur, camera noise, or coding errors Chapter 53 looks at methods for image and video restoration This is the most mathematically based area of image processing, and it is also one of the areas with the longest history It has applications in the analysis of astronomical images, in forensic

imaging, and in the production of high-quality stills from video sequences.

Chapter 54 looks at methods for motion estimation and video scan conversion Motion estimation

is a key technique for removing temporal redundancy in image sequences and, as a result, it is a key component in all of the video compression standards It is also, however, a highly time-consuming numerically ill-posed operation As a result it continues to be highly studied, particularly with respect to more sophisticated motion models A related problem is the problem of scanning format conversion This is a major issue in television systems where both interlaced and progressively scanned images are encountered

Chapter 57 explores stereoscopic and multiview image processing Traditional image processing assumes that only one camera is present As a result depth information in a three-dimensional scene

is lost When explicit depth information is needed, multiple cameras can be used Differences in the displacement of objects in the left and right images can be converted to depth measurements Mam-mals do this naturally with their two eyes Stereoscopic image processing techniques are becoming increasingly used in problems of computer vision and computer graphics This chapter discusses the state-of-the-art in this emerging area

The final two chapters in this section, Chapters 58 and 59, look at software and hardware systems for doing image processing Chapter 58 provides an overview of a representative set of image software packages that embody the core capabilities required by many image processing applications It also provides a list of Internet addresses for a number of image databases Chapter 59 provides an overview

of VLSI architectures for implementing many of the video compression standards