Finite-state vector quantization by exploiting interband andintraband correlations for subband image coding

Subband coding (SBC) with vector quantization (VQ) has been shown to be an effective method for coding images at low bit rates. We split the image spectrum into seven nonuniform subbands. Threshold vector quantization (TVQ) and finite state vector quantization (FSVQ) methods are employed in coding the subband images by exploiting interband and intraband correlations. Our new SBC-FSVQ schemes have the advantages of the subband-VQ scheme while reducing the bit rate and improving the image quality. Experimental results are given and comparisons are made using our new scheme and some other coding techniques. In the experiments, it is found that SBC-FSVQ schemes achieve the best peak signal-to-noise ratio (PSNR) performance when compared to other methods at the same bit rate.     

Adaptive edge-based side-match finite-state classified vectorquantization with quadtree map

Vector quantization (VQ) is an effective image coding technique at low bit rate. The side-match finite-state vector quantizer (SMVQ) exploits the correlations between neighboring blocks (vectors) to avoid large gray level transition across block boundaries. A new adaptive edge-based side-match finite-state classified vector quantizer (classified FSVQ) with a quadtree map has been proposed. In classified FSVQ, blocks are arranged into two main classes, edge blocks and nonedge blocks, to avoid selecting a wrong state codebook for an input block. In order to improve the image quality, edge vectors are reclassified into 16 classes. Each class uses a master codebook that is different from the codebooks of other classes. In our experiments, results are given and comparisons are made between the new scheme and ordinary SMVQ and VQ coding techniques. As is shown, the improvement over ordinary SMVQ is up to 1.16 dB at nearly the same bit rate, moreover, the improvement over ordinary VQ can be up to 2.08 dB at the same bit rate for the image, Lena. Further, block boundaries and edge degradation are less visible because of the edge-vector classification. Hence, the perceptual image quality of classified FSVQ is better than that of ordinary SMVQ.     

Wavelet image coding using variable blocksize vector quantization with optimal quadtree segmentation

In this paper, we propose an image coding scheme by using the variable blocksize vector quantization (VBVQ) to compress wavelet coefficients of an image. The scheme is capable of finding an optimal quadtree segmentation of wavelet coefficients of an image for VBVQ subject to a given bit budget, such that the total distortion of quantized wavelet coefficients is minimal. From our simulation results, we can see that our proposed coding scheme has higher performance in PSNR than other wavelet/VQ or subband/VQ coding schemes.     

Subband coding of images using vector quantization

A novel two-dimensional subband coding technique is presented that can be applied to images as well as speech. A frequency-band decomposition of the image is carried out by means of 2D separable quadrature mirror filters, which split the image spectrum into 16 equal-rate subbands. These 16 parallel subband signals are regarded as a 16-dimensional vector source and coded as such using vector quantization. In the asymptotic case of high bit rates, a theoretical analysis yields that a lower bound to the gain is attainable by choosing this approach over scalar quantization of each subband with an optimal bit allocation. It is shown that vector quantization in this scheme has several advantages over coding the subbands separately. Experimental results are given, and it is shown the scheme has a performance that is comparable to that of more complex coding techniques     

Side match and overlap match vector quantizers for images

A class of vector quantizers with memory that are known as finite state vector quantizers (FSVQs) in the image coding framework is investigated. Two FSVQ designs, namely side match vector quantizers (SMVQs) and overlap match vector quantizers (OMVQs), are introduced. These designs take advantage of the 2-D spatial contiguity of pixel vectors as well as the high spatial correlation of pixels in typical gray-level images. SMVQ and OMVQ try to minimize the granular noise that causes visible pixel block boundaries in ordinary VQ. For 512 by 512 gray-level images, SMVQ and OMVQ can achieve communication quality reproduction at an average of 1/2 b/pixel per image frame, and acceptable quality reproduction. Because block boundaries are less visible, the perceived improvement in quality over ordinary VQ is even greater. Owing to the structure of SMVQ and OMVQ, simple variable length noiseless codes can achieve as much as 60% bit rate reduction over fixed-length noiseless codes.     

Regularized subband coding scheme

Two enhanced subband coding schemes using a regularized image restoration technique are proposed: the first controls the global regularity of the decompressed image; the second extends the first approach at each decomposition level. The quantization scheme incorporates scalar quantization (SQ) and pyramidal lattice vector quantization (VQ) with both optimal bit and quantizer allocation. Experimental results show that both the block effect due to VQ and the quantization noise are significantly reduced.     

基于BWT和FVQ的极低比特率图像编码算法

该文提出了一种基于双正交小波变换(BWT)和模糊矢量量化(FVQ)的极低比特率图像编码算法。该算法通过构造符合图像小波变换系数特征的跨频带矢量,充分利用了不同频带小波系数之间的相关性,有效地提高了图像的编码效率和重构质量。该算法采用非线性插补矢量量化(NLIVQ)的思想,从大维数矢量中提取小维数的特征矢量,并提出了一种新的模糊矢量量化方法一渐进构造模糊聚类(PCFC)算法用于特征矢量的量化,从而大大提高了矢量量化的速度和码书质量。实验结果证明,该算法在比特率为0．172bpp的条件下仍能获得PSNR＞30dB的高质量重构图像。     

A new finite-state vector quantizer with optimized state space andderailment-free operation

A new finite-state vector quantization (FSVQ) algorithm is developed based on state space optimization and the derailment prevention requirement. The proposed derailment-free FSVQ (DF-FSVQ) achieves good performance through (i) state space reduction, which allows a practical implementation of high-order FSVQ, and (ii) derailment free state transitions. Experimental results show that our approach outperforms other known FSVQ schemes in terms of performance, system complexity, and processing speed, especially at low bit rate image coding.     

Vector quantization for entropy coding of image subbands

Vector quantization for entropy coding of image subbands is investigated. Rate distortion curves are computed with mean square error as a distortion criterion. The authors show that full-search entropy-constrained vector quantization of image subbands results in the best performance, but is computationally expensive. Lattice quantizers yield a coding efficiency almost indistinguishable from optimum full-search entropy-constrained vector quantization. Orthogonal lattice quantizers were found to perform almost as well as lattice quantizers derived from dense sphere packings. An optimum bit allocation rule based on a Lagrange multiplier formulation is applied to subband coding. Coding results are shown for a still image.     

VQ-adaptive block transform coding of images

Two new design techniques for adaptive orthogonal block transforms based on vector quantization (VQ) codebooks are presented. Both techniques start from reference vectors that are adapted to the characteristics of the signal to be coded, while using different methods to create orthogonal bases. The resulting transforms represent a signal coding tool that stands between a pure VQ scheme on one extreme and signal-independent, fixed block transformation-like discrete cosine transform (DCT) on the other. The proposed technique has superior compaction performance as compared to DCT both in the rendition of details of the image and in the peak signal-to-noise ratio (PSNR) figures.     

Region adaptive subband image coding

We present a region adaptive subband image coding scheme using the statistical properties of image subbands for various subband decompositions. Motivated by analytical results obtained when the input signal to the subband decomposition is a unit step function, we analyze the energy packing properties toward the lower frequency subbands, edges, and the dependency of energy distribution on the orientation of the edges, in subband decomposed images. Based on these investigations and ideal analysis/synthesis filtering done in the frequency domain, the region adaptive subband image coding scheme extracts suitably shaped regions in each subband and then uses adaptive entropy-constrained quantizers for different regions under the assumption of a generalized Gaussian distribution for the image subbands. We also address the problem of determining an optimal subband decomposition among all possible decompositions. Experimental results show that visual degradations in the reconstructed image are negligible at a bit rate of 1.0 b/pel and reasonable quality images are obtainable at rates as low as 0.25 b/pel.     

用于图像编码的相关矢量量化研究

当相邻的图像块用矢量量化（ＶＱ）编码时可能出现编码地址相同的情况,尤其是在图像的平滑区。为了减少相邻块间编码地址的相关性,本文提出了一种相关矢量量化方案,采用相关码书与改进的自组织特征映射（ＩＳＯＦＭ）码书同时编码一个窗口内的四个邻域块,与无记忆类ＶＱ相比,对一幅典型的“Ｌｅｎｎａ”图象,编码过程中所需计算量减少一半,比特率减少４０％,由于在Ｋｏｈｏｎｅｎ自组织神经网络的训练过程中,对边缘类矢量采     

A fractal vector quantizer for image coding

We investigate the relation between VQ (vector quantization) and fractal image coding techniques, and propose a novel algorithm for still image coding, based on fractal vector quantization (FVQ). In FVQ, the source image is approximated coarsely by fixed basis blocks, and the codebook is self-trained from the coarsely approximated image, rather than from an outside training set or the source image itself. Therefore, FVQ is capable of eliminating the redundancy in the codebook without any side information, in addition to exploiting the self-similarity in real images effectively. The computer simulation results demonstrate that the proposed algorithm provides better peak signal-to-noise ratio (PSNR) performance than most other fractal-based coders.     

Wavelet tree classification and hybrid coding for image compression

A hybrid coding system that uses a combination of set partition in hierarchical trees (SPIHT) and vector quantisation (VQ) for image compression is presented. Here, the wavelet coefficients of the input image are rearranged to form the wavelet trees that are composed of the corresponding wavelet coefficients from all the subbands of the same orientation. A simple tree classifier has been proposed to group wavelet trees into two classes based on the amplitude distribution. Each class of wavelet trees is encoded using an appropriate procedure, specifically either SPIHT or VQ. Experimental results show that advantages obtained by combining the superior coding performance of VQ and efficient cross-subband prediction of SPIHT are appreciable for the compression task, especially for natural images with large portions of textures. For example, the proposed hybrid coding outperforms SPIHT by 0.38 dB in PSNR at 0.5 bpp for the Bridge image, and by 0.74 dB at 0.5 bpp for the Mandrill image.     

A robust joint source channel coding scheme for image transmission over the ionospheric channel

In this paper, we propose a joint source channel coding (JSCC) scheme to the transmission of fixed images for wireless communication applications. The ionospheric channel which presents some characteristics identical to those found on mobile radio channels, like fading, multipath and Doppler effect is our test channel. As this method based on a wavelet transform, a self-organising map (SOM) vector quantization (VQ) optimally mapped on a QAM digital modulation and an unequal error protection (UEP) strategy, this method is particularly well adapted to low bit-rate applications. The compression process consists in applying a SOM VQ on the discrete wavelet transform coefficients and computing several codebooks depending on the sub-images preserved. An UEP is achieved with a correcting code applied on the most significant data. The JSCC consists of an optimal mapping of the VQ codebook vectors on a high spectral efficiency digital modulation. This feature allows preserving the topological organization of the codebook along the transmission chain while keeping a reduced complexity system. This method applied on grey level images can be used for colour images as well. Several tests of transmission for different images have shown the robustness of this method even for high bit error rate (BER>10⁻²). In order to qualify the quality of the image after transmission, we use a PSNR% (peak signal-to-noise ratio) parameter which is the value of the difference of the PSNR after compression at the transmitter and after reception at the receiver. This parameter clearly shows that 95% of the PSNR is preserved when the BER is less than 10⁻².     

Design and Evaluation of an Entirely Psychovisual-Based Coding Scheme

In this paper a new psychovisual-based coding scheme is proposed. The analysis and the quantization stages, the two main functions which determine the performances of a coding scheme, are based on the human visual system properties. In the first stage, a filter bank decomposes images into subimages of perceptual significance when a contrast transformation is applied. Analytic cortex filters have been used because they provide an accurate modelization of visual receptive fields. The choice of subbands lies on psychovisual experiments led in the laboratory. It was found that visual information is processed through 17 channels. In the second stage the use of the local band-limited contrast yields very interesting properties concerning the quantization. A scalar and vector quantization have been considered. In this latter case the vector's construction methodology preserves the main properties of the human visual system about perception of quantization impairments and takes into account the masking effect due to interaction between subbands with the same radial frequency but with different orientations. The vector's components are the local band limited contrasts C_ij (m, n) defined as the ratio between the luminance L_ij at the point (m, n), which belongs to the radial subband i and angular sector j and the average luminance at this location. Hence the vector's dimension depends on the orientation selectivity of the chosen decomposition. The low pass subband, which is nondirectional is scalar quantized. A methodology for automatic subsampling matrix design was also developed. The performance have been evaluated on a set of images in terms of peak SNR, true bit rates, and visual quality. For the latter, no impairments are visible at a distance of four times the height of the used high quality TV monitor. The SNRs are about 6 to 8 dB under the ones of classical subband image coding schemes when producing the same visual quality. Another particularity of this approach, due to the use of the local band limited contrast, lies in the structure of the reconstruction image error which is found to be highly correlated to the structure of the original image.     

Wavelet-based image coding using nonlinear interpolative vectorquantization

We propose a reduced complexity wavelet-based image coding technique. Here, 64-D (for three stages of decomposition) vectors are formed by combining appropriate coefficients from the wavelet subimages, 16-D feature vectors are then extracted from the 64-D vectors on which vector quantization (VQ) is performed. At the decoder, 64-D vectors are reconstructed using a nonlinear interpolative technique. The proposed technique has a reduced complexity and has the potential to provide a superior coding performance when the codebook is generated using the training vectors drawn from similar images.     

Subband coding of image sequences at low bit rates

In this paper a low bit rate subband coding scheme for image sequences is described. Typically, the scheme is based on temporal DPCM in combination with an intraframe subband coder. In contrast to previous work, however, the subbands are divided into blocks onto which conditional replenishment is applied, while a bit allocation algorithm divides the bits among the blocks assigned for replenishment. A solution is given for the ‘dirty window’ effect by setting blocks to zero that were assigned to be replenished but received no bits. The effect of motion compensation and the extension to color images are discussed as well. Finally, several image sequence coding results are given for a bit rate of 300 kbit/s.     

Adaptive entropy coded subband coding of images

The authors describe a design approach, called 2-D entropy-constrained subband coding (ECSBC), based upon recently developed 2-D entropy-constrained vector quantization (ECVQ) schemes. The output indexes of the embedded quantizers are further compressed by use of noiseless entropy coding schemes, such as Huffman or arithmetic codes, resulting in variable-rate outputs. Depending upon the specific configurations of the ECVQ and the ECPVQ over the subbands, many different types of SBC schemes can be derived within the generic 2-D ECSBC framework. Among these, the authors concentrate on three representative types of 2-D ECSBC schemes and provide relative performance evaluations. They also describe an adaptive buffer instrumented version of 2-D ECSBC, called 2-D ECSBC/AEC, for use with fixed-rate channels which completely eliminates buffer overflow/underflow problems. This adaptive scheme achieves performance quite close to the corresponding ideal 2-D ECSBC system.     

An entropy-coded lattice vector quantizer for transform and subbandimage coding

A lattice-based vector quantizer (VQ) and noiseless code are proposed for transform and subband image coding. The quantization is simple to implement, and no vector codebooks need to be stored. The noiseless code enumerates lattice codevectors based on their (weighted) l(1) norm. A software implementation is able to handle lattice codebooks of size 2(256). The image coding performance is shown to be comparable or superior to the best encoding methods reported in the literature.