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A01=Kalyani R. Joshi
A01=Madhuri A. Joshi
A01=Mehul S. Raval
A01=Shilpa P. Metkar
A01=Yogesh H. Dandawate
advanced image compression techniques
arithmetic
Author_Kalyani R. Joshi
Author_Madhuri A. Joshi
Author_Mehul S. Raval
Author_Shilpa P. Metkar
Author_Yogesh H. Dandawate
bit
Bit Rate
Category=UYT
Codebook Designed
coding
Compressive Sensing
data storage optimisation
digital signal processing
discrete
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
fourier
graduate level resource
huffman
Huffman Coding
IEC JTC
Image Compression
image quality metrics
Initial Codebook
IQA
JPEG Compression
Lossless Image Compression
MATLAB programming
Measurement Matrix
MV
Psychovisual Redundancy
quantisation methods
rate
ratio
Run Length Coding
Signal Reconstruction
Sparse Signal
Sparse Signal Recovery
Training Vectors
transform
Ux Vy
Video Coding
Video Coding Standards
VQ
Watermarked Image
wavelet
Wavelet Function
Wavelet Transform

Product details

  • ISBN 9780367378165
  • Weight: 453g
  • Dimensions: 156 x 234mm
  • Publication Date: 19 Sep 2019
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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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® 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.

Joshi, Madhuri A.; Raval, Mehul S.; Dandawate, Yogesh H.; Joshi, Kalyani R.; Metkar, Shilpa P.

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