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111
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 ..."
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Cited by 700 (12 self)
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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 ..."
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Cited by 86 (11 self)
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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 Rev
, 1999
"... Abstract. Each discrete cosine transform (DCT) uses N real basis vectors whose components are cosines. In the DCT4, for example, the jth component of vk is cos(j+ 12)(k+ 1 ..."
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Cited by 78 (2 self)
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Abstract. Each discrete cosine transform (DCT) uses N real basis vectors whose components are cosines. In the DCT4, for example, the jth component of vk is cos(j+ 12)(k+ 1
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 ..."
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Cited by 76 (1 self)
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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
 IEEE Journal of Solid State Circuits
, 1996
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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 ..."
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Cited by 41 (1 self)
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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 ...
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 ..."
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Cited by 38 (3 self)
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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
Fast algorithms for discrete polynomial transforms
 Math. Comput
, 1998
"... Abstract. Consider the Vandermondelike matrix P: = (Pk(cos jπ ..."
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Cited by 32 (7 self)
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Abstract. Consider the Vandermondelike matrix P: = (Pk(cos jπ
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 ..."
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Cited by 29 (4 self)
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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...