Fast Vector Quantization Algorithm Research Articles

A fast VQ codebook generation algorithm via pattern reduction

In this paper, we present a simple but fast codebook generation algorithm, called PREGLA (Pattern Reduction Enhanced GLA). The proposed algorithm is fundamentally different from the previous approaches in that the previous approaches focus on reducing the size of the codebook whereas the proposed algorithm focuses on using pattern reduction to reduce the computation time. The proposed algorithm is motivated by the observation that input vectors that are “static” during the training process can be considered as part of the final solutions and thus can be compressed and removed to eliminate the redundant computations at the later iterations of the training process. To evaluate the performance of the proposed algorithm, we compare the proposed algorithm with “efficient” state-of-the-art GLA or GLA-based algorithms such as Codeword Displacement, Nearest Partition Set Search, Fast Vector Quantization Algorithm, Law of Cosines, and standard GLA. Our experimental results indicate that the proposed algorithm can reduce the computation time from 29.45% up to about 77.98% compared to those of standard GLA and other fast GLA-based algorithms alone.

Hyperspectral data compression using a fast vector quantization algorithm

A fast vector quantization algorithm for data compression of hyperspectral imagery is proposed in this paper. It makes use of the fact that in the full search of the generalized Lloyd algorithm (GLA) a training vector does not require a search to find the minimum distance partition if its distance to the partition is improved in the current iteration compared to that of the previous iteration. The proposed method has the advantage of being simple, producing a large computation time saving and yielding compression fidelity as good as the GLA. Four hyperspectral data cubes covering a wide variety of scene types were tested. The loss of spectral information due to compression was evaluated using the spectral angle mapper and a remote sensing application.

Fast vector quantization image coding by mean value predictive algorithm

Vector quantization (VQ) is an effective technology for signal compression. In traditional VQ, most of the computation concentrates on searching the nearest codeword in the codebook for each input vector. We propose a fast VQ algorithm to reduce the encoding time. There are two main parts in our proposed algorithm. One is the preprocessing process and the other is the practical encoding process. In preprocessing, we will generate some tables that we need to employ for practical encoding. Because those tables are used for all the images, the time to generate these tables does not increase any time in the practical encoding process. On the second part, the practical encoding process, we use the tables generated previously and other techniques to speed up the encoding time. This paper provides an effective algorithm to accelerate the encoding time. The proposed algorithm demonstrates the outstanding performance in terms of time saving and arithmetic operations. Compared to a full search algorithm, it saves more than 95% searching time.

Fast feature-based vector quantization algorithm of image coding

A fast vector quantization algorithm is presented that exploits the spatial redundancy between neighboring vectors of pixels in an image to improve the performance of the triangle inequality elimination ruler, and employs the integral projection technique, a dimension-reduction method, to reduce the computational complexity of calculating distortion measure. Application of dimension reduction in distortion measures may result in some degradation of objective image quality. But a significant complexity reduction of over 90% in comparison with the conventional full-search method can be achieved. The degradation of image quality is only less than 0.2 dB in peak signal-to-noise ratio (PSNR). Acceptable image quality should be obtained successfully.