Geometric Vector Quantization Research Articles

Using Multiscale Edge Detection for Low Bit-Rate Subband Video Coding

This paper introduces a video compression system for very low bit–rate video–conferencing and tele–monitoring applications. The video codec first performs a three–dimensional subband decomposition on a group of video frames, and then encode the subbands with different quantization methods. By using a cubic spline wavelet, the spatial filter bank acts as a multiscale edge detector. This property is used for efficient selection and coding of high frequency subbands with geometric vector quantization. For lowest tempo–spatial subband, a DPCM coding with an entropy coder was used. Results at several low bit rates (16,32, 64, 128 Kbps) are reported and compared with H.263, the standard for low bit rate video coding.

Radial compression function for vector quantizer of Laplacian source with high dynamic variance range

In this paper, our aim is to define a high dynamic range vector quantization model for an i.i.d. Laplacian source. In order to do this we use a geometric approach and lattice quantization. As a result, we achieve symmetry in distribution of code vectors and introduce the radial scalar compression function for lattice cell side determination. This enables derivation of a sophisticated creation for a condition which the radial compression function should satisfy in order to make the SQNR non-dependent on variance. It is interesting that the obtained solution represents the generalisation of solution in the case of high dynamic range scalar quantization. Because of that, in the second part of the paper, the well-known semilogarithmic A-law compression characteristic is utilized as radial scalar compression function of geometric vector quantizer. It is shown that such a model has very good performances over a very wide variance range. The proper choice of compression parameter A and dimension n enables the constant SQNR to sustain over much wider variance range than in case of log-scalar quantization. Particularly, the high dimension makes it possible to broaden the variance range over which SQNR remains constant by increasing the A value simultaneously holding the SQNR level unchanged. In comparison with the widely used log-scalar quantization, the presented solution for dimensions from 10 to 140 gives a quality improvement of 7–9 dB, while comparison of maximal SQNR value to that of optimal scalar quantization shows the SQNR growth of 3–5 dB. Furthermore, for dimension 40 the SQNR maximum is only for 0.5 dB lower than that of optimal vector quantization, while dynamic range characteristic broadening is considerable. The cited benefits of high dynamic range vector quantization make it possible to realize a high quality signal compression. Besides, in comparison with the adaptive quantization, our proposal has a smaller implementation complexity. Copyright © 2010 John Wiley & Sons, Ltd.

Contents of Volume 17

In this work, we present a coding scheme based on a rate-distortion optimum wavelet packets decomposition and on an adaptive coding procedure that exploits spatial non-stationarity within each subband. We show, by means of a generalization of the concept of coding gain to the case of non-stationary signals, that it may be convenient to perform subband decomposition optimization in conjunction with intraband optimal bit allocation. In our implementation, each subband is partitioned into blocks of coefficients that are coded using a geometric vector quantizer with a rate determined on the basis of spatially local statistical characteristics. The proposed scheme appears to be simpler than other wavelet packets-based schemes presented in the literature and achieves good results in terms of both compression and visual quality.

Low Complexity Image Compression Algorithm for Wireless Channel

Pyramid coding with absolute moment block truncation coding (PC-AMBTC) technique is proposed for wireless image communication. The digital European cordless telecommunications (DECT) system is used as wireless communication environment. The impact of Channel fading on the PC-AMBTC technique is investigated and its performance is compared with other algorithms. The image is decomposed into two images (decimated image and the difference image). The AMBTC is used for the decimated image and a modified geometric vector quantization (MGVQ) is used for the difference image. Since most of the signal power and information is located in the low frequency band of the image, special attention is given to this band to protect its information from Channel fading errors. Simulation results show that the proposed technique is very robust to Channel fading errors. It is found that the performance of PC-AMBTC algorithm is better than the existing algorithms at the same bit rate.

Wavelet packets and spatial adaptive intraband coding of images

Abstract In this work, we present a coding scheme based on a rate-distortion optimum wavelet packets decomposition and on an adaptive coding procedure that exploits spatial non-stationarity within each subband. We show, by means of a generalization of the concept of coding gain to the case of non-stationary signals, that it may be convenient to perform subband decomposition optimization in conjunction with intraband optimal bit allocation. In our implementation, each subband is partitioned into blocks of coefficients that are coded using a geometric vector quantizer with a rate determined on the basis of spatially local statistical characteristics. The proposed scheme appears to be simpler than other wavelet packets-based schemes presented in the literature and achieves good results in terms of both compression and visual quality.

An image coding scheme based on image projections and geometrical VQ

Abstract This paper deals with an image coding scheme based on overlapped projections and geometrical vector quantization. As far as still images are concerned, we present a simple coding algorithm, in which, the concept of projection, normally employed in tomographic applications, and geometric vector quantization are used to describe, in a compact way, circular overlapping regions in which the input image is subdivided. In this way it is possible to represent in a good way, contour and edge information without introducing significant blocking artefacts. The proposed coding techniques can also be used to represent images at progressive levels of quality. This aim can be simply obtained by increasing the number of projections used to describe each image region. Simulations carried out on several test images showed interesting results. An extension of this projection-based coding techniques is proposed for moving images too. In this case projections are used to describe in a compact way, the motion compensated luminance differences between the current image and the previous coded one.

Hybrid vector quantization for multiresolution image coding

In this correspondence, we propose a coding scheme that exploits the redundancy of the multiresolution representation of images, in that blocks in one subimage are predicted from blocks of the adjacent lower resolution subimage with the same orientation. The pool of blocks used for prediction of a given subband plays the role of a codebook that is built from vectors of coefficients inside the subband decomposition itself. Whenever the prediction procedure does not give satisfactory results with respect to a target quality, the block coefficients are quantized using a geometric vector quantizer for a Laplacian source.

Three-dimensional subband coding of video

We describe and show the results of video coding based on a three-dimensional (3-D) spatio-temporal subband decomposition. The results include a 1-Mbps coder based on a new adaptive differential pulse code modulation scheme (ADPCM) and adaptive bit allocation. This rate is useful for video storage on CD-ROM. Coding results are also shown for a 384-kbps rate that are based on ADPCM for the lowest frequency band and a new form of vector quantization (geometric vector quantization (GVQ)) for the data in the higher frequency bands. GVQ takes advantage of the inherent structure and sparseness of the data in the higher bands. Results are also shown for a 128-kbps coder that is based on an unbalanced tree-structured vector quantizer (UTSVQ) for the lowest frequency band and GVQ for the higher frequency bands. The results are competitive with traditional video coding techniques and provide the motivation for investigating the 3-D subband framework for different coding schemes and various applications.