.png){kind=icon}
Return
Complexity-constrained tree-structured vector quantizers
.png){kind=icon}
PRE
AI
DOI：10.1016/S0165-1684(02)00253-0.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We present a new algorithm for complexity-distortion optimization in tree-structured vector quantizers (TSVQ). The algorithm allows the user to specify the average rate and computational complexity budgets R and C, measured in bits and multiplications per sample, respectively. The output is an optimal-in a sense to be specified-TSVQ satisfying the constraints. The complexity budget is lower-bounded by the complexity of a binary TSVQ and upper-bounded by the complexity of a full-search entropy-constrained vector quantizer. Experimental results for synthetic and natural sources are given. (C) 2002 Elsevier Science B.V. All rights reserved.
Keywords:
tree-structured vector quantizer
complexity
optimization
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.6
Papers:
9.9K
Citations:
1.7W
Organization
No organization information available
Cited Papers
Wildfire activity and land use drove 20th‐century changes in forest cover in the Colorado front range.png)
Ecosphere
OAAI
EcosphereIF0
OAAIShareSaveHighly Selective Continuous‐Flow Synthesis of Potentially Bioactive Deuterated Chalcone DerivativesChemPlusChem
ChemPlusChemIF0
PREAI
ShareSaveShareSave
ShareSaveShareSaveShareSave no more
no more