Results 1  10
of
80
Quantization
 IEEE TRANS. INFORM. THEORY
, 1998
"... The history of the theory and practice of quantization dates to 1948, although similar ideas had appeared in the literature as long ago as 1898. The fundamental role of quantization in modulation and analogtodigital conversion was first recognized during the early development of pulsecode modula ..."
Abstract

Cited by 639 (11 self)
 Add to MetaCart
The history of the theory and practice of quantization dates to 1948, although similar ideas had appeared in the literature as long ago as 1898. The fundamental role of quantization in modulation and analogtodigital conversion was first recognized during the early development of pulsecode modulation systems, especially in the 1948 paper of Oliver, Pierce, and Shannon. Also in 1948, Bennett published the first highresolution analysis of quantization and an exact analysis of quantization noise for Gaussian processes, and Shannon published the beginnings of rate distortion theory, which would provide a theory for quantization as analogtodigital conversion and as data compression. Beginning with these three papers of fifty years ago, we trace the history of quantization from its origins through this decade, and we survey the fundamentals of the theory and many of the popular and promising techniques for quantization.
SPL: A Language and Compiler for DSP Algorithms
, 2001
"... We discuss the design and implementation of a compiler that translates formulas representing signal processing transforms into ecient C or Fortran programs. The formulas are represented in a language that we call SPL, an acronym from Signal Processing Language. The compiler is a component of the SPI ..."
Abstract

Cited by 82 (11 self)
 Add to MetaCart
We discuss the design and implementation of a compiler that translates formulas representing signal processing transforms into ecient C or Fortran programs. The formulas are represented in a language that we call SPL, an acronym from Signal Processing Language. The compiler is a component of the SPIRAL system which makes use of formula transformations and intelligent search strategies to automatically generate optimized digital signal processing (DSP) libraries. After a discussion of the translation and optimization techniques implemented in the compiler, we use SPL formulations of the fast Fourier transform (FFT) to evaluate the compiler. Our results show that SPIRAL, which can be used to implement many classes of algorithms, produces programs that perform as well as \hardwired" systems like FFTW.
The Discrete Cosine Transform
 SIAM Review
, 1999
"... Each Discrete Cosine Transform uses N real basis vectors whose components are cosines. In the DCT4, for example, the jth component of v k is cos(j + 1 2 )(k + 1 2 ) ß N . These basis vectors are orthogonal and the transform is extremely useful in image processing. If the vector x gives the inten ..."
Abstract

Cited by 73 (2 self)
 Add to MetaCart
Each Discrete Cosine Transform uses N real basis vectors whose components are cosines. In the DCT4, for example, the jth component of v k is cos(j + 1 2 )(k + 1 2 ) ß N . These basis vectors are orthogonal and the transform is extremely useful in image processing. If the vector x gives the intensities along a row of pixels, its cosine series P c k v k has the coefficients c k = (x; v k )=N . They are quickly computed from an FFT. But a direct proof of orthogonality, by calculating inner products, does not reveal how natural these cosine vectors are. We prove orthogonality in a different way. Each DCT basis contains the eigenvectors of a symmetric "second difference" matrix. By varying the boundary conditions we get the established transforms DCT1 through DCT4. Other combinations lead to four additional cosine transforms. The type of boundary condition (Dirichlet or Neumann, centered at a meshpoint or a midpoint) determines the applications that are appropriate for each transfor...
Lossy Source Coding
 IEEE Trans. Inform. Theory
, 1998
"... Lossy coding of speech, highquality audio, still images, and video is commonplace today. However, in 1948, few lossy compression systems were in service. Shannon introduced and developed the theory of source coding with a fidelity criterion, also called ratedistortion theory. For the first 25 year ..."
Abstract

Cited by 71 (1 self)
 Add to MetaCart
Lossy coding of speech, highquality audio, still images, and video is commonplace today. However, in 1948, few lossy compression systems were in service. Shannon introduced and developed the theory of source coding with a fidelity criterion, also called ratedistortion theory. For the first 25 years of its existence, ratedistortion theory had relatively little impact on the methods and systems actually used to compress real sources. Today, however, ratedistortion theoretic concepts are an important component of many lossy compression techniques and standards. We chronicle the development of ratedistortion theory and provide an overview of its influence on the practice of lossy source coding. Index TermsData compression, image coding, speech coding, rate distortion theory, signal coding, source coding with a fidelity criterion, video coding. I.
Modeling and Analysis of Substrate Coupling in Integrated Circuits
, 1995
"... Modeling and Analysis of Substrate Coupling in Integrated Circuits by Ranjit Gharpurey Doctor of Philosophy in Engineering Electrical Engineering and Computer Sciences University of California at Berkeley Professor Robert G. Meyer, Chair Substrate coupling in integrated circuits is the process w ..."
Abstract

Cited by 67 (0 self)
 Add to MetaCart
Modeling and Analysis of Substrate Coupling in Integrated Circuits by Ranjit Gharpurey Doctor of Philosophy in Engineering Electrical Engineering and Computer Sciences University of California at Berkeley Professor Robert G. Meyer, Chair Substrate coupling in integrated circuits is the process whereby parasitic current flow in the substrate electrically couples devices in different parts of the circuit. Higher levels of integration and higher frequencies of operation make the coupling more pronounced in modern circuit realizations. High levels of integration are desirable in several applications for reducing the overall power dissipation, reducing the number of components and lowering costs. Portable radioreceivers are an example of such an application. Electrical coupling in the substrate leads to undesirable interaction between devices which can degrade circuit performance. The degradation can manifest itself in several ways. In mixed analogdigital circuits, for example, the switchingnoise generated by digital circuits can be coupled to the sensitive analog circuits through the substrate. Performance degradation due to substrate coupling can be addressed at the circuitdesign stage by including accurate substrate models in circuit simulations. An efficient and elegant technique to model substrate coupling is presented in this dissertation. The technique uses a combination of the classical Green function approach and the Fast Fourier Transform. The speed of this technique makes it suitable for optimization of circuit layout for minimization of substrate coupling related effects. The nature of substrate coupling in different types of substrates has been analyzed. The effectiveness of isolation schemes, such as guard rings, in different types of substrates has als...
Waveletbased image coding: An overview
 Applied and Computational Control, Signals, and Circuits
, 1998
"... ABSTRACT This paper presents an overview of waveletbased image coding. We develop the basics of image coding with a discussion of vector quantization. We motivate the use of transform coding in practical settings,and describe the properties of various decorrelating transforms. We motivate the use o ..."
Abstract

Cited by 35 (3 self)
 Add to MetaCart
ABSTRACT This paper presents an overview of waveletbased image coding. We develop the basics of image coding with a discussion of vector quantization. We motivate the use of transform coding in practical settings,and describe the properties of various decorrelating transforms. We motivate the use of the wavelet transform in coding using ratedistortion considerations as well as approximationtheoretic considerations. Finally,we give an overview of current coders in the literature. 1
HighLevel Synthesis Techniques for Reducing the Activity of Functional Units
, 1995
"... Decisions taken at the earliest steps of the design process may have a significant impact on the characteristics of the final implementation. This paper illustrates how power consumption issues can be tackled during highlevel synthesis (highlevel transformations, scheduling and binding). Several t ..."
Abstract

Cited by 34 (1 self)
 Add to MetaCart
Decisions taken at the earliest steps of the design process may have a significant impact on the characteristics of the final implementation. This paper illustrates how power consumption issues can be tackled during highlevel synthesis (highlevel transformations, scheduling and binding). Several techniques pursuing low power are proposed and the potential benefits evaluated. The common idea behind these techniques is to reduce the activity of the functional units (e.g. adders, multipliers) by minimizing the changes of their input operands. Preliminary evaluations obtained from switchlevel simulations show that significant improvements can be achieved. 1 Introduction Power consumption can be taken into account at different levels [5]: technological, topological, architectural and algorithmic level. Highlevel synthesis (HLS) comprises techniques at the architectural and algorithmic level. Traditionally, HLS has been applied to obtain small and fast designs. But little has been done ...
Learning to Predict Performance from Formula Modeling and Training Data
 In Proceedings of the Seventeenth International Conference on Machine Learning
, 2000
"... This paper reports on our work and results framing signal processing algorithm optimization as a machine learning task. A single signal processing algorithm can be represented by many different but mathematically equivalent formulas. When these formulas are implemented in actual code, they hav ..."
Abstract

Cited by 27 (4 self)
 Add to MetaCart
This paper reports on our work and results framing signal processing algorithm optimization as a machine learning task. A single signal processing algorithm can be represented by many different but mathematically equivalent formulas. When these formulas are implemented in actual code, they have very different running times. Signal processing optimization is concerned with finding a formula that implements the algorithm as efficiently as possible. Unfortunately, a correct mapping between a mathematical formula and its running time is unknown. However empirical performance data can be gathered for a variety of formulas. This data offers an interesting opportunity to learn to predict running time performance. In this paper we present two major results along this direction: (1) Different sets of features are identified for mathematical formulas that distinguish them into partitions with significantly different running times, and (2) A function approximator can learn t...
Fast algorithms for discrete polynomial transforms
 Math. Comput
, 1998
"... Abstract. Consider the Vandermondelike matrix P: = (Pk(cos jπ ..."
Abstract

Cited by 27 (7 self)
 Add to MetaCart
Abstract. Consider the Vandermondelike matrix P: = (Pk(cos jπ