Variable-length constrained-storage tree-structured vector quantization

Constrained storage vector quantization, (CSVQ), introduced by Chan anti Ge rsho [2]-[4], allows for the stagewise design of balanced tree-structured r esidual vector quantization codebooks with low encoding and storage complex ities, On the other hand, it has been established in [9], [11], and [12] th at variable-length tree-structured vector quantizer (VLTSVQ) yields better coding performance than a balanced tree-structured vector quantizer and mag even outperform a full-search vector quantizer due to the nonuniform distr ibution of rate among the subsets of its input space, The variable-length c onstrained storage tree-structured vector quantization (VLCS-TSVQ) algorith m presented in this paper utilizes the codebook sharing by multiple vector sources concept as in CSVQ to greedily grow an unbalanced tree structured r esidual vector quantizer with constrained storage. It is demonstrated by si mulations on test sets from various synthetic one-dimensional (I-D) sources and real-world images that the performance of VLCS-TSVQ, whose codebook st orage complexity varies linearly with rate, can come very close to the perf ormance of greedy growth VLTSVQ of [11] and [12], The dramatically reduced size of the overall codebook allows the transmission of the codevector prob abilities as side information for source adaptive entropy coding.