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Image and video signals require large transmission bandwidth and
storage, leading to high costs. The data must be compressed without
a loss or with a small loss of quality. Thus, efficient image and
video compression algorithms play a significant role in the storage
and transmission of data. Image and Video Compression:
Fundamentals, Techniques, and Applications explains the major
techniques for image and video compression and demonstrates their
practical implementation using MATLAB (R) programs. Designed for
students, researchers, and practicing engineers, the book presents
both basic principles and real practical applications. In an
accessible way, the book covers basic schemes for image and video
compression, including lossless techniques and wavelet- and vector
quantization-based image compression and digital video compression.
The MATLAB programs enable readers to gain hands-on experience with
the techniques. The authors provide quality metrics used to
evaluate the performance of the compression algorithms. They also
introduce the modern technique of compressed sensing, which retains
the most important part of the signal while it is being sensed.
Image and video signals require large transmission bandwidth and
storage, leading to high costs. The data must be compressed without
a loss or with a small loss of quality. Thus, efficient image and
video compression algorithms play a significant role in the storage
and transmission of data. Image and Video Compression:
Fundamentals, Techniques, and Applications explains the major
techniques for image and video compression and demonstrates their
practical implementation using MATLAB (R) programs. Designed for
students, researchers, and practicing engineers, the book presents
both basic principles and real practical applications. In an
accessible way, the book covers basic schemes for image and video
compression, including lossless techniques and wavelet- and vector
quantization-based image compression and digital video compression.
The MATLAB programs enable readers to gain hands-on experience with
the techniques. The authors provide quality metrics used to
evaluate the performance of the compression algorithms. They also
introduce the modern technique of compressed sensing, which retains
the most important part of the signal while it is being sensed.
This introduction to the fundamental concepts and methodologies of
image processing is suitable for first-year postgraduate and senior
undergraduate students in almost every engineering discipline, and
in particular meets the requirement of the prescribed courses in
the streams: Electronics and Communication, Computer Science and
Engineering, Information Technology, and Computer Applications. The
book, now in its second edition, continues to offer a balanced
exposition of the basic principles and applications of image
processing. It lays considerable emphasis on the algorithmic
approach in order to teach students how to write good practical
programs for problem solving. Major topics covered in the book
include Image fundamentals, Different image transforms, Image
enhancement in the spatial and frequency domains, Restoration,
Image analysis, Image description, Image compression, Image
reconstruction from projections, and Applications of image
processing in the areas of biometrics, speaker recognition,
satellite imaging, medical imaging, and many more. The style of
presentation is comprehensive and application oriented, comprising
examples, diagrams, image results, case studies of applications,
and review questions-making it easy for students to understand key
ideas, their practical relevance and applications.
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