Finding sparse representation of quantized frame coefficients using 0-1 integer programming

The use of overcomplete dictionaries, or frames, has received increased attention in low-bit-rate compression. Several vector selection algorithms, such as Matching Pursuit, Orthogonal Matching Pursuit and FOCUSS have been developed to get sparse representations of signals. In these algorithms, cont...

Full description

Saved in:
Bibliographic Details
Published in:ISPA 2001 : proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis : in conjunction with 23nd [sic] Int'l Conference on Information Technology Interfaces : Pula, Croatia, June 19-21, 2001 pp. 541 - 544
Main Authors: Ryen, T., Aase, S.O., Husoy, J.H.
Format: Conference Proceeding
Language:English
Published: Piscataway NJ IEEE 2001
IEEE Service Center
Subjects:
Applied sciences
Bit rate
Dictionaries
Distortion measurement
Exact sciences and technology
Information, signal and communications theory
Linear programming
Matching pursuit algorithms
Pursuit algorithms
Quantization
Signal analysis
Signal and communications theory
Signal representation. Spectral analysis
Signal, noise
Sparse matrices
Telecommunications and information theory
Vectors
Integer programming
Signal compression
Optimal solution
Signal processing
Low bit rate
Signal representation
Algorithm
ISBN:9539676940, 9789539676948
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The use of overcomplete dictionaries, or frames, has received increased attention in low-bit-rate compression. Several vector selection algorithms, such as Matching Pursuit, Orthogonal Matching Pursuit and FOCUSS have been developed to get sparse representations of signals. In these algorithms, continuous valued coefficients are found and subsequently quantized. The latter part can cause unwanted effects on the quality of the reconstructed signal. We propose an algorithm that merges the selection and quantization procedures by using 0-1 integer programming. The object is to minimize the distortion measured by the l/sub 1/-norm, subject to a bound on the number of "ones" in a binary representation of the frame coefficients. This bound is an indirect measure of the bit rate. Our new algorithm finds the globally optimal solution based on the abovementioned criteria.
ISBN:9539676940
9789539676948
DOI:10.1109/ISPA.2001.938688