Codebook Generation Algorithm Research Articles

Image compression by vector quantization with noniterative derivation of a codebook: applications to video and confocal images

We demonstrate an image-compression technique that uses what we believe is a new noniterative codebook generation algorithm for vector quantization. The technique supports rapid decompression and is equally applicable to individual images or to a set of images without the need for interframe processing. Compression with a single-image codebook is tested on (1) ten confocal images of the hindbrain of a mouse embryo, (2) video images of a polystyrene microsphere that is manipulated by a focused laser light, and (3) five fluorescence images of the embryo eye lens taken at different magnifications. The reconstructions are assessed with the normalized mean-squared error and with Linfoot's criteria of fidelity, structural content, and correlation quality. Experimental results with single-image compression show that the technique produces fewer local artifacts than JPEG compression, especially with noisy images. Results with video and confocal image series indicate that single-image codebook generation is sufficient at practical compression ratios for producing acceptable reconstructions for mouse embryo analysis and for viewing optically trapped microspheres. Experiments with the magnified images also reveal that the compression scheme is robust to scaling.

Fast Search Algorithms for VQ Codebook Generation

In this paper, we propose two fast codebook generation algorithms by making use of the information in the iterative process. Comparing to the conventional full search method (the LBG algorithm), the Fast Codebook Generation Algorithm (FCGA) reduces the CPU time by a factor of 2.49 to 6.16 for real images. Compared to FCGA the Maximum Set Algorithm (MSA), which is designed for the large codebook, gives the worse performance in general for our simulations. However, the Maximum Set Algorithm (MSA) still gives a better performance than that of the LBG algorithm and reduces the CPU time by a factor of 1.63 to 2.67. Both methods presented in this paper outperform the DSBS method developed by Huang and Harris. Compared to the LBG algorithm, the DSBS method reduced the codebook generation time by a factor of 1.5–2. It is noted that both FCGA and MSA give the same codebook as that produced by the LBG algorithm. Finally, some experimental results for several training sets with different types and sizes are listed.

Image compression by vector quantization: a review focused on codebook generation

Recently, vector quantization (VQ) has received considerable attention, and has become an effective tool for image compression. It provides a high compression ratio and a simple decoding process. However, studies on the practical implementation of VQ have revealed some major difficulties such as edge integrity and codebook design efficiency. After reviewing the state-of-the-art in the field of vector quantization, we focus on iterative and non-iterative codebook generation algorithms.

A comparison of several vector quantization codebook generation approaches

A review and a performance comparison of several often-used vector quantization (VQ) codebook generation algorithms are presented. The codebook generation algorithms discussed include the Linde-Buzo-Gray (LBG) binary-splitting algorithm, the pairwise nearest-neighbor algorithm, the simulated annealing algorithm, and the fuzzy c-means clustering analysis algorithm. A new directed-search binary-splitting method which reduces the complexity of the LBG algorithm, is presented. Also, a new initial codebook selection method which can obtain a good initial codebook is presented. By using this initial codebook selection algorithm, the overall LBG codebook generation time can be reduced by a factor of 1.5-2.

Fast codebook generation algorithm for vector quantization of images

A partial search partial distortion (PSPD) algorithm is presented for reducing the computational complexity of the codebook generation in vector quantization (VQ) of images. A partial codebook is built up using the feature of a training vector. The partial distortion technique is employed to find the best match codevector from the partial codebook. The new algorithm results in a considerable reduction of computational complexity when compared to the conventional LBG algorithm.