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Updated: Jul 7, 2026
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Quasi-light Storage for Optical Data Packets
Published on: February 6, 2014
Variable-length constrained-storage tree-structured vector quantization
1Toshiba American Electronic Components, Inc., San Jose, CA 95131, USA.
Summary
This study introduces Variable-Length Constrained Storage Tree-Structured Vector Quantization (VLCS-TSVQ), an algorithm that achieves coding performance close to existing methods while significantly reducing codebook size. This enables efficient transmission of code vector probabilities for adaptive entropy coding.
Area of Science:
- Digital signal processing
- Information theory
- Data compression
Background:
- Constrained Storage Vector Quantization (CSVQ) offers low complexity but balanced codebooks.
- Variable-Length Tree-Structured Vector Quantization (VLTSVQ) provides superior coding performance due to nonuniform rate distribution.
Purpose of the Study:
- To develop a Variable-Length Constrained Storage Tree-Structured Vector Quantization (VLCS-TSVQ) algorithm.
- To achieve high coding performance with reduced codebook storage complexity.
Main Methods:
- Utilizes codebook sharing from CSVQ.
- Greedily grows an unbalanced tree-structured residual vector quantizer.
- Applies constrained storage principles.
Main Results:
- VLCS-TSVQ performance closely approaches greedy growth VLTSVQ.
- Codebook storage complexity scales linearly with the rate.
- Demonstrated effectiveness on 1-D synthetic sources and real-world images.
Conclusions:
- VLCS-TSVQ offers a practical approach to high-performance vector quantization with reduced storage.
- The reduced codebook size facilitates efficient side information transmission for adaptive entropy coding.
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