Variable-length constrained-storage tree-structured vector quantization

Ulug Bayazit*, William A. Pearlman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


Constrained storage vector quantization, (CSVQ), introduced by Chan and Gersho [2]-[4], allows for the stagewise design of balanced tree-structured residual vector quantization codebooks with low encoding and storage complexities. On the other hand, it has been established in [9], [11], and [12] that variable-length tree-structured vector quantizer (VLTSVQ) yields better coding performance than a balanced tree-structured vector quantizer and may even outperform a full-search vector quantizer due to the nonuniform distribution of rate among the subsets of its input space. The variable-length constrained storage tree-structured vector quantization (VLCS-TSVQ) algorithm presented in this paper utilizes the codebook sharing by multiple vector sources concept as in CSVQ to greedily grow an unbalanced tree structured residual vector quantizer with constrained storage. It is demonstrated by simulations on test sets from various synthetic one-dimensional (1-D) sources and real-world images that the performance of VLCS-TSVQ, whose codebook storage complexity varies linearly with rate, can come very close to the performance of greedy growth VLTSVQ of [11] and [12]. The dramatically reduced size of the overall codebook allows the transmission of the codevector probabilities as side information for source adaptive entropy coding.

Original languageEnglish
Pages (from-to)321-331
Number of pages11
JournalIEEE Transactions on Image Processing
Issue number3
Publication statusPublished - 1999
Externally publishedYes


Manuscript received August 31, 1996; revised September 23, 1997. This material is based upon work supported by the National Science Foundation under Grant NCR-9523767. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Roland Wilson.

FundersFunder number
National Science FoundationNCR-9523767


    • Adaptive coding
    • Tree data structures
    • Vector quantization


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