Fast full search equivalent encoding algorithms for image compression using vector quantization

Fast planar-oriented ripple search algorithm for hyperspace VQ codebook

This paper presents a fast codebook search method for improving the quantization complexity of full-search vector quantization (VQ). The proposed method is built on the planar Voronoi diagram to label a ripple search domain. Then, the appropriate codeword can easily be found just by searching the local region instead of global exploration. In order to take a step further and obtain the close result full-search VQ would, we equip the proposed method with a duplication mechanism that helps to bring down the possible quantizing distortion to its lowest level. According to the experimental results, the proposed method is indeed capable of providing better outcome at a faster quantization speed than the existing partial-search methods. Moreover, the proposed method only requires a little extra storage for duplication.

Fast vector quantization algorithms based on nearest partition set search

A fast search method for vector quantization is proposed in this paper. It makes use of the fact that in the generalized Lloyd algorithm (GLA) a vector in a training sequence is either placed in the same minimum distance partition (MDP) as in the previous iteration or in a partition within a very small subset of partitions. The proposed method searches for the MDP for a training vector only in this subset of partitions plus the single previous MDP. As the size of this subset is much smaller than the total number of codevectors, the search process is speeded up significantly. The creation of the subset is essential, as it has a direct effect on the improvement in computation time of the proposed method. The schemes that create the subset efficiently have been proposed. The proposed method generates a codebook identical to that generated using the GLA. It is simple and requires only minor modification of the GLA and a modest amount of additional memory. The experimental results show that the computation time of codebook training was improved by factors from 6.6 to 50.7 and from 5.8 to 70.4 for two test data sets when codebooks of sizes from N = 16 to 2048 were trained. The proposed method was also combined with an earlier published method to further improve the computation time.

A data mining technique for discovering distinct patterns of hand signs: implications in user training and computer interface design

Hand signs are considered as one of the important ways to enter information into computers for certain tasks. Computers receive sensor data of hand signs for recognition. When using hand signs as computer inputs, we need to (1) train computer users in the sign language so that their hand signs can be easily recognized by computers, and (2) design the computer interface to avoid the use of confusing signs for improving user input performance and user satisfaction. For user training and computer interface design, it is important to have a knowledge of which signs can be easily recognized by computers and which signs are not distinguishable by computers. This paper presents a data mining technique to discover distinct patterns of hand signs from sensor data. Based on these patterns, we derive a group of indistinguishable signs by computers. Such information can in turn assist in user training and computer interface design.

An efficient encoding algorithm for vector quantization based on subvector technique

In this paper, a new and fast encoding algorithm for vector quantization is presented. This algorithm makes full use of two characteristics of a vector: the sum and the variance. A vector is separated into two subvectors: one is composed of the first half of vector components and the other consists of the remaining vector components. Three inequalities based on the sums and variances of a vector and its two subvectors components are introduced to reject those codewords that are impossible to be the nearest codeword, thereby saving a great deal of computational time, while introducing no extra distortion compared to the conventional full search algorithm. The simulation results show that the proposed algorithm is faster than the equal-average nearest neighbor search (ENNS), the improved ENNS, the equal-average equal-variance nearest neighbor search (EENNS) and the improved EENNS algorithms. Comparing with the improved EENNS algorithm, the proposed algorithm reduces the computational time and the number of distortion calculations by 2.4% to 6% and 20.5% to 26.8%, respectively. The average improvements of the computational time and the number of distortion calculations are 4% and 24.6% for the codebook sizes of 128 to 1024, respectively.

A fast search algorithm for vector quantization using L2-norm pyramid of codewords

Vector quantization for image compression requires expensive encoding time to find the closest codeword to the input vector. This paper presents a fast algorithm to speed up the closest codeword search process in vector quantization encoding. By using an appropriate topological structure of the codebook, we first derive a condition to eliminate unnecessary matching operations from the search procedure. Then, based on this elimination condition, a fast search algorithm is suggested. Simulation results show that with little preprocessing and memory cost, the proposed search algorithm significantly reduces the encoding complexity while maintaining the same encoding quality as that of the full search algorithm. It is also found that the proposed algorithm outperforms the existing search algorithms.

Color quantization and processing by Fibonacci lattices

Color quantization is sampling of three-dimensional (3-D) color spaces (such as RGB or Lab) which results in a discrete subset of colors known as a color codebook or palette. It is extensively used for display, transfer, and storage of natural images in Internet-based applications, computer graphics, and animation. We propose a sampling scheme which provides a uniform quantization of the Lab space. The idea is based on several results from number theory and phyllotaxy. The sampling algorithm is very much systematic and allows easy design of universal (image-independent) color codebooks for a given set of parameters. The codebook structure allows fast quantization and ordered dither of color images. The display quality of images quantized by the proposed color codebooks is comparable with that of image-dependent quantizers. Most importantly, the quantized images are more amenable to the type of processing used for grayscale ones. Methods for processing grayscale images cannot be simply extended to color images because they rely on the fact that each gray-level is described by a single number and the fact that a relation of full order can be easily established on the set of those numbers. Color spaces (such as RGB or Lab) are, on the other hand, 3-D. The proposed color quantization, i.e., color space sampling and numbering of sampled points, makes methods for processing grayscale images extendible to color images. We illustrate possible processing of color images by first introducing the basic average and difference operations and then implementing edge detection and compression of color quantized images.

Fast nearest neighbor search of entropy-constrained vector quantization

Entropy-constrained vector quantization (ECVQ) offers substantially improved image quality over vector quantization (VQ) at the cost of additional encoding complexity. We extend results in the literature for fast nearest neighbor search of VQ to ECVQ. We use a new, easily computed distance that successfully eliminates most codewords from consideration.

Fast search algorithms for vector quantization of images using multiple triangle inequalities and wavelet transform

The encoding of vector quantization (VQ) needs expensive computation for searching the closest codevector to the input vector. This paper presents several fast encoding algorithms based on multiple triangle inequalities and wavelet transform to overcome this problem. The multiple triangle inequalities confine a search range using the intersection of search areas generated from several control vectors. A systematic way for designing the control vectors is also presented. The wavelet transform combined with the partial distance elimination is used to reduce the computational complexity of the distance calculation of vectors. The proposed algorithms provide the same coding quality as the full search method. The experimental results indicate that the new algorithms perform more efficiently than existing algorithms.

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 fractal image block coding based on local variances

In fractal image block coding, most of the time is spent on finding a close match between a range block and a large pool of domain blocks. For a large image, this effect becomes aggravated as the domain pool increases exponentially. We propose using the local variances of domain blocks to reduce the search space. By sorting the contracted domain pool according to their local variances and defining an acceptance criterion for a close match, we can confine all the potential close matches to a relatively small sized window to limit the search space. The encoding time can hence be shortened with the decoded image quality as good as that using the full search method. The speedup can be over ten times depending on the complexity of encoded images.

Digital color imaging

This paper surveys current technology and research in the area of digital color imaging. In order to establish the background and lay down terminology, fundamental concepts of color perception and measurement are first presented using vector-space notation and terminology. Present-day color recording and reproduction systems are reviewed along with the common mathematical models used for representing these devices. Algorithms for processing color images for display and communication are surveyed, and a forecast of research trends is attempted. An extensive bibliography is provided.

A novel approach for coding color quantized images

An approach to the lossy compression of color images with limited palette that does not require color quantization of the decoded image is presented. The algorithm is particularly suited for coding images using an image-dependent palette. The technique restricts the pixels of the decoded image to take values only in the original palette. Thus, the decoded image can be readily displayed without having to be quantized. For comparable quality and bit rates, the technique significantly reduces the decoder computational complexity.