Journal ArticleDOI
Fast closest codeword search algorithms for vector quantisation
Chang-Hsing Lee,L.-H. Chen +1 more
- Vol. 141, Iss: 3, pp 143-148
TLDR
In this paper, the authors present a fast algorithm to search for the closest codeword in vector quantization, which uses two significant features of a vector, mean value and variance, to reject many unlikely codewords and save a great deal of computation time.Abstract:
One of the most serious problems for vector quantisation is the high computational complexity of searching for the closest codeword in the codebook design and encoding phases. The authors present a fast algorithm to search for the closest codeword. The proposed algorithm uses two significant features of a vector, mean value and variance, to reject many unlikely codewords and saves a great deal of computation time. Since the proposed algorithm rejects those codewords that are impossible to be the closest codeword, this algorithm introduces no extra distortion than conventional full search method. The results obtained confirm the effectiveness of the proposed algorithm.read more
Citations
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Journal ArticleDOI
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
TL;DR: In this paper, it was shown that given an integer k ≥ 1, (1 + ϵ)-approximation to the k nearest neighbors of q can be computed in additional O(kd log n) time.
Journal ArticleDOI
Quantization
Robert M. Gray,David L. Neuhoff +1 more
TL;DR: The key to a successful quantization is the selection of an error criterion – such as entropy and signal-to-noise ratio – and the development of optimal quantizers for this criterion.
Journal ArticleDOI
Vector quantization for license-plate location and image coding
Rodolfo Zunino,Stefano Rovetta +1 more
TL;DR: A novel method based on vector quantization (VQ) to process vehicle images that makes it possible to perform superior picture compression for archival purposes and to support effective location at the same time.
Journal ArticleDOI
An efficient encoding algorithm for vector quantization based on subvector technique
TL;DR: A new and fast encoding algorithm for vector quantization is presented that makes full use of two characteristics of a vector: the sum and the variance.
Journal ArticleDOI
A fast encoding algorithm for vector quantization
TL;DR: A fast encoding algorithm for vector quantization that uses two characteristics of a vector, mean, and variance simultaneously to save computation time all the more.
References
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Journal ArticleDOI
An Algorithm for Vector Quantizer Design
Y. Linde,A. Buzo,Robert M. Gray +2 more
TL;DR: An efficient and intuitive algorithm is presented for the design of vector quantizers based either on a known probabilistic model or on a long training sequence of data.
Journal ArticleDOI
An Improvement of the Minimum Distortion Encoding Algorithm for Vector Quantization
Chang-da Bei,Robert M. Gray +1 more
TL;DR: A very simple method is presented for improving the efficiency of minimum distortion encoding for vector quantization by reducing the number of multiplications in a full search vector quantizer with a large number of codewords.
Proceedings ArticleDOI
A fast nearest-neighbor search algorithm
TL;DR: A fast nearest-neighbor search algorithm is developed which incorporates prior information about input vectors in the form of a vector from the codebook which is known to be near the input vector, though it may not be the nearest codebook vector.
Journal ArticleDOI
Effect of ordering the codebook on the efficiency of the partial distance search algorithm for vector quantization
TL;DR: It is shown that the computational complexity of this algorithm can be reduced further by ordering the codevectors according to the sizes of their corresponding clusters.
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