Image and Video Compression Fundamentals, Techniques, and Applications
Auteurs : Joshi Madhuri A., Raval Mehul S., Dandawate Yogesh H., Joshi Kalyani R., Metkar Shilpa P.
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.
Preface. Introduction to image compression. Lossless image compression. Image Transforms. Wavelet Based Image Compression. Image Compression Using Vector Quantization. Digital Video Compression. Image Quality Assessment. Compressive Sensing. Index
Date de parution : 11-2014
15.6x23.4 cm
Date de parution : 09-2019
15.6x23.4 cm
Thème d’Image and Video Compression :
Mots-clés :
Image Compression; Watermarked Image; Compressive Sensing; Huffman Coding; Wavelet Transform; JPEG Compression; Run Length Coding; Bit Rate; IEC JTC; IQA; Codebook Designed; Lossless Image Compression; Video Coding; Video Coding Standards; Initial Codebook; Measurement Matrix; Sparse Signal; Ux Vy; Training Vectors; VQ; MV; Wavelet Function; Signal Reconstruction; Sparse Signal Recovery; Psychovisual Redundancy