Learning Tree-Structured Vector Quantization for Image Compression
نویسنده
چکیده
Kohonen's self-organizing feature map (KSOFM) is an adaptive vector quantization (VQ) scheme for progressive code vector update. However, KSOFM approach belongs to unconstrained vector quantization, which suuers from exponential growth of the codebook. In this paper, a learning tree-structured vector quantization (LTSVQ) is presented for overcoming this drawback, which is based on competitive learning (CL) algorithm. LTSVQ algorithm is computationally very eecient, easy to implement and provides performance comparable to that of the LBG (Linde, Buzo and Gray) algorithm.
منابع مشابه
Pii: S0893-6080(01)00020-x
This paper introduces S-TREE (Self-Organizing Tree), a family of models that use unsupervised learning to construct hierarchical representations of data and online tree-structured vector quantizers. The S-TREE1 model, which features a new tree-building algorithm, can be implemented with various cost functions. An alternative implementation, S-TREE2, which uses a new double-path search procedure...
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