Product Description
Data compression is one of the main contributing factors in the explosive growth in information technology. Without it, a number of consumer and commercial products, such as DVD, videophone, digital camera, MP3, video-streaming and wireless PCS, would have been virtually impossible. Transforming the data to a frequency or other domain enables even more efficient compression. By illustrating this intimate link, The Transform and Data Compression Handbook serves as a much-needed handbook for a wide range of researchers and engineers.The authors describe various discrete transforms and their applications in different disciplines. They cover techniques, such as adaptive quantization and entropy coding, that result in significant reduction in bit rates when applied to the transform coefficients. With clear and concise presentations of the ideas and concepts, as well as detailed descriptions of the algorithms, the authors provide important insight into the applications and their limitations. Data compression is an essential step towards the efficient storage and transmission of information. The Transform and Data Compression Handbook provides a wealth of information regarding different discrete transforms and demonstrates their power and practicality in data compression.
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First Sentence:
The goal of image compression is to store an image in a more compact form, i.e., a representation that requires fewer bits for encoding than the original image. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
decoded image quality, statistical context modeling, first vertical subband, int blksize, minimum conditional entropy, other discrete sinusoidal, significant transform values, context quantizer, large range blocks, matching domain block, new significant values, thresholded transform, bit stream buffer, conditional entropy coding, emit bits, program following value, bit plane coding, projective flow, context quantization, projective coordinate transformation, affine fit, range block size, diagonal subbands, projective fit, embedded bit stream Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, Special Permission of Playboy, Academic Press, Englewood Cliffs, Eye Tap, John Wiley, Kluwer Academic Publishers, Optical Engineering, San Diego, Boca Raton, Data Compression Conference, Joint Photographic Experts Group, Local Variables, San Francisco, Van Nostrand Reinhold, Fast Discrete Cosine, Image Understanding Workshop, International Conference, Odd Ones, Picture Transfer Protocol, The Even Bases, Audio Electroacoust, Electronics Letters, Netlib Repository, San Jose New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
The goal of image compression is to store an image in a more compact form, i.e., a representation that requires fewer bits for encoding than the original image. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
decoded image quality, statistical context modeling, first vertical subband, int blksize, minimum conditional entropy, other discrete sinusoidal, significant transform values, context quantizer, large range blocks, matching domain block, new significant values, thresholded transform, bit stream buffer, conditional entropy coding, emit bits, program following value, bit plane coding, projective flow, context quantization, projective coordinate transformation, affine fit, range block size, diagonal subbands, projective fit, embedded bit stream Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, Special Permission of Playboy, Academic Press, Englewood Cliffs, Eye Tap, John Wiley, Kluwer Academic Publishers, Optical Engineering, San Diego, Boca Raton, Data Compression Conference, Joint Photographic Experts Group, Local Variables, San Francisco, Van Nostrand Reinhold, Fast Discrete Cosine, Image Understanding Workshop, International Conference, Odd Ones, Picture Transfer Protocol, The Even Bases, Audio Electroacoust, Electronics Letters, Netlib Repository, San Jose New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 65
books:
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15
books
cite this book:
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- The Digital Signal Processing Handbook, Second Edition - 3 Volume Set (Electrical Engineering Handbook Series) by Vijay K. Madisetti on 4 pages
- Discrete Cosine Transform: Algorithms, Advantages, Applications by K. Ramamohan Rao on page 36, page 76, and page 263
- Transform Coding of Images (Microelectronics and Signal Processing) by R. J. Clarke on page 35, page 194, and page 261
- Vector Quantization and Signal Compression (The Springer International Series in Engineering and Computer Science) by Allen Gersho on page 35, page 344, and page 376
- Multirate Systems And Filter Banks (Prentice Hall Signal Processing Series) by P. P. Vaidyanathan on page 77, page 265, and page 310
See all 65 books this book cites
- Signal Processing Noise (Electrical Engineering & Applied Signal Processing Series) by Vyacheslav P. Tuzlukov in Front Matter
- Handbook of Neural Network Signal Processing (Electrical Engineering & Applied Signal Processing) by Yu Hen Hu in Front Matter
- Multimedia Signals and Systems (The Springer International Series in Engineering and Computer Science) by Mrinal Kr. Mandal on page 118
- Digital Color Imaging Handbook (Electrical Engineering & Applied Signal Processing Series) by Gaurav Sharma on page 610
- Nonlinear Signal and Image Processing: Theory, Methods, and Applications (Electrical Engineering & Applied Signal Processing Series) by Kenneth E. Barner in Front Matter
See all 15 books citing this book
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