The Transform and Data Compression Handbook
By K. Rao, P. Yip. 2000
412 citations recorded by the source corpus.
From the Publisher: The Transform and Data Compression Handbook serves as a 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 presentations of the ideas and concepts, as well as descriptions of the algorithms, the authors provide 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 information regarding different discrete transforms and demonstrates their power and practicality in data compression.
Subfields: Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/979885088067f48f8cbeaf368df12f2511e5d308
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Dongsun Lee
- J. Huh
- Darae Jeong
- Jaemin Shin
- Ana Yun
- Junseok Kim
- P. A. Lyakhov
- N. Chervyakov
Citations in the corpus
- Physical, mathematical, and numerical derivations of the Cahn–Hilliard equation (2014, methodology): “(38) can be computed in O(N lnN) operations with the 2D Fast Cosine Transform [89,91,92].”
- Application of the residue number system to reduce hardware costs of the convolutional neural network implementation (2020, background): “This value describes the difference between the two images, almost imperceptible to the viewer [24].”