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.     

Smooth side-match weighted vector quantiser with variable block size for image coding

Although side-match vector quantisation (SMVQ) reduces the bit rate, the quality of image coding using SMVQ generally degenerates as the grey level transition across the boundaries of neighbouring blocks increases or decreases. The author proposes a smooth side-match weighted method to yield a state codebook according to the smoothness of the grey levels between neighbouring blocks. When a block is encoded, a corresponding weight is assigned to each neighbouring block to represent its relative importance. This smooth side-match weighted vector quantisation (SSMWVQ) achieves a higher PSNR than SMVQ at the same bit rate. Also, each block can be pre-encoded in an image, allowing each encoded block to use all neighbouring blocks to yield the state codebook in SSMWVQ, rather than using only two neighbouring blocks, as in SMVQ. Moreover, SSMWVQ selects many high-detail blocks as basic blocks to enhance the coding quality, and merges many low-detail blocks into a larger one to reduce further the bit rate. Experimental results reveal that SSMWVQ has a higher PSNR and lower bit rate than other methods.     

A new image coding algorithm using variable-rate side-matchfinite-state vector quantization

A new side-match vector quantizer, NewSMVQ, is presented in this paper. Three techniques are incorporated to improve the image quality, encoding speed, and bit rate for compressing images. The experimental result shows: i) the encoding time of NewSMVQ is almost 7 times faster than that of SMVQ (ordinary fixed-rate side-match vector quantizer) and CSMVQ (variable-rate SMVQ) and ii) NewSMVQ outperforms SMVQ and CSMVQ in terms of bit rate versus image quality tradeoffs.     

Image coding using variable-rate side-match finite-state vectorquantization

Future B-ISDN (broadband integrated services digital network) users will be able to send various kinds of information, such as voice, data, and image, over the same network and send information only when necessary. It has been recognized that variable-rate encoding techniques are more suitable than fixed-rate techniques for encoding images in a B-ISDN environment. A new variable-rate side-match finite-state vector quantization with a block classifier (CSMVQ) algorithm is described. In an ordinary fixed-rate SMVQ, the size of the state codebook is fixed. In the CSMVQ algorithm presented, the size of the state codebook is changed according to the characteristics of the current vector which can be predicted by a block classifier. In experiments, the improvement over SMVQ was up to 1.761 dB at a lower bit rate. Moreover, the improvement over VQ can be up to 3 dB at nearly the same bit rate.     

Fast Image Coding Algorithm Using Indirect-Index Codebook Based on SMVQ

Side-match vector quantization (SMVQ) achieves better compression performance than vector quantization (VQ) in image coding due to its exploration of the dependence of adjacent pixels. However, SMVQ has the disadvantage of requiring excessive time during the process of coding. Therefore, this paper proposes a fast image coding algorithm using indirect-index codebook based on SMVQ (ⅡC-SMVQ) to reduce the coding time. Two codebooks, named indirect-index codebook (Ⅱ-codebook) and entire-state codebook (ES-codebook), are trained and utilized. The Ⅱ-codebook is trained by using the Linde-Buzo-Gray (LBG) algorithm from side-match information, while the ES-codebook is generated from the clustered residual blocks on the basis of the Ⅱ-codebook. According to the relationship between these two codebooks, the codeword in the Ⅱ-codebook can be regarded as an indicator to construct a fast search path, which guides in quickly determining the state codebook from the ES-codebook to encode the to-be-encoded block. The experimental results confirm that the coding time of the proposed scheme is shorter than that of the previous SMVQ.     

Side-match tree-structured vector quantiser for image progressive coding

The balanced tree-structured vector quantiser is the traditional method of achieving image progressive coding. During image progressive coding, an image is decoded step-by-step in a decoder. The author proposes an unbalanced tree-structured vector quantiser to perform image progressive coding for a given series of rate thresholds. Side-match vector quantisation and its variants have been proposed to reduce the bit rate in image coding. The tree-structured vector quantiser and the side-match vector quantiser are combined to perform image progressive coding, achieving a better coding quality than that obtained using only the tree-structured vector quantiser at the same bit rate.     

一种改进的相关图像矢量量化编码算法

本文提出了一种改进的图像矢量量化编码算法。该算法通过采用增加预测块，以及根据相邻块状态进行控制信息的条件熵编码等改进方法，进一步提高了算法的编码效率，为了便于算法实现，本文对图像的边缘引进的一致的处理方法。测试结果表明，相对于无记忆适量量化编码算法，比特率约下降４０％，相对于文献［５］算法，比特率约下降１５％。     

Novel image compression method using edge-oriented classifier andnovel predictive noiseless coding method

A new image compression approach is proposed in which variable block size technique is adopted, using quadtree decomposition, for coding images at low bit rates. In the proposed approach, low-activity regions, which usually occupy large areas in an image, were coded with a larger block size and the block mean is used to represent each pixel in the block. To preserve edge integrity, the classified vector quantisation (CVQ) technique is used to code high-activity regions. A new edge-oriented classifier without employing any thresholds is proposed for edge classification. A novel predictive noiseless coding (NPNC) method which exploits the redundancy between neighbouring blocks is also presented to efficiently code the mean values of low-activity blocks and the addresses of edge blocks. The bit rates required for coding the mean values and addresses can be significantly reduced by the proposed NPNC method. Experimental results show that excellent reconstructed images and higher PSNR were obtained     

A fuzzy classified vector quantizer for image coding

Vector quantization of images raises problems of complexity in codebook search and subjective quality of images. The family of image vector quantization algorithms proposed in this paper addresses both of those problems. The fuzzy classified vector quantizer (FCVQ) is based on fuzzy set theory and consists basically in a method of extracting a subcodebook from the original codebook, biased by the features of the block to be coded. The incidence of each feature on the blocks is represented by a fuzzy set that captures its (possibly subjective) nature. Unlike the classified vector quantizer (CVQ), in the FCVQ a specific subcodebook is extracted for each block to be coded, allowing a better adaptation to the block. The CVQ may be regarded as a special case of the FCVQ. In order to explore the possible correlation between blocks, an estimator for the degree of incidence of features on the block to be coded is included. The estimate is based on previously coded blocks and is obtained by maximizing a possibility; a distribution that intends to represent the subjective knowledge on the feature's possibility of occurrence conditioned to the coded blocks is used. Some examples of the application of a FCVQ coder to two test images are presented. A slight improvement on the subjective quality of the coded images is obtained, together with a significant reduction on the codebook search complexity and, when applying the estimator, a reduction of the bit rate     

Image coding using robust quantization for noisy digitaltransmission

A robust quantizer is developed for encoding memoryless sources and transmission over the binary symmetric channel (BSC). The system combines channel optimized scalar quantization (COSQ) with all-pass filtering, the latter performed using a binary phase-scrambling/descrambling method. Applied to a broad class of sources, the robust quantizer achieves the same performance as the Gaussian COSQ for the memoryless Gaussian source. This quantizer is used in image coding for transmission over a BSC. The peak signal-to-noise ratio (PSNR) performance degrades gracefully as the channel bit error rate increases.     

基于特征值苑配的变比特率相关矢量量化图像编码算法

该文提出了一种用于图像编码的新颖的变比特率相关矢量量化器。在编码之前,首先计算各码字的四个特征值,然后根据各特征值的升序排列得到相应的四个排序码书。在对当前输入矢量(当前处理图像块)进行编码的过程中,充分考虑当前处理图像块与其相邻图像块之间的相关性以及各码字特征值与该输入矢量特征值之间的匹配性。测试结果表明,该算法与传统矢量量化(VQ)器相比,虽然在编码质量上有少许下降,但降低了比特率并加快了编码速度。     

A generalized interpolative vector quantization method for jointlyoptimal quantization, interpolation, and binarization of textimages

This paper presents an approach for the effective combination of interpolation with binarization of gray level text images to reconstruct a high resolution binary image from a lower resolution gray level one. We study two nonlinear interpolative techniques for text image interpolation. These nonlinear interpolation methods map quantized low dimensional 2x2 image blocks to higher dimensional 4x4 (possibly binary) blocks using a table lookup operation. The first method performs interpolation of text images using context-based, nonlinear, interpolative, vector quantization (NLIVQ). This system has a simple training procedure and has performance (for gray-level high resolution images) that is comparable to our more sophisticated generalized interpolative VQ (GIVQ) approach, which is the second method. In it, we jointly optimize the quantizer and interpolator to find matched codebooks for the low and high resolution images. Then, to obtain the binary codebook that incorporates binarization with interpolation, we introduce a binary constrained optimization method using GIVQ. In order to incorporate the nearest neighbor constraint on the quantizer while minimizing the distortion in the interpolated image, a deterministic-annealing-based optimization technique is applied. With a few interpolation examples, we demonstrate the superior performance of this method over the NLIVQ method (especially for binary outputs) and other standard techniques e.g., bilinear interpolation and pixel replication.     

A very low bit rate video coder based on vector quantization

Describes a video coder based on a hybrid DPCM-vector quantization algorithm that is suited for bit rates ranging from 8-16 kb/s. The proposed approach involves segmenting difference images into variable-size and variable-shape blocks and performing segmentation and motion compensation simultaneously. The purpose of obtaining motion vectors for variable-size and variable-shape blocks is to improve the quality of motion estimation, particularly in those areas where the edges of moving objects are situated. For the larger blocks, decimation takes place in order to simplify vector quantization. For very active blocks, which are always of small dimension, a specific vector quantizer has been applied, the fuzzy classified vector quantizer (FCVQ). The coding algorithm described displays good performance in the compression of test sequences at the rates of 8 and 16 kb/s; the signal-to-noise ratios obtained are good in both cases. The complexity of the coder implementation is comparable to that of conventional hybrid coders, while the decoder is much simpler in this proposal.     

改进的基于快速相关矢量量化的高效信息隐藏方法

为了改善信息隐藏后的图像质量,减少失真,并提高嵌入信息的容量,本文提出了一种新的改进的基于快速相关矢量量化（MFCVQ,modified fast correlation VQ）的信息隐藏方法。由于图像本身的相关性,快速相关矢量量化利用了当前索引的相邻矢量进行替代编码,计算编码时使用的相邻矢量所产生的失真与事先设定的门限做比较,当失真小于门限时可以进行替代编码,一步完成了编码和嵌入信息;当失真大于门限时不能进行替代编码,从而控制了当前索引的嵌入和最后生成图像的视觉质量。同时,增加了相邻矢量的数目,提高了嵌入信息的容量。实验结果表明改进后的算法能够显著改善图像失真,在门限为18时对不同复杂程度的图像其PSNR分别提高了0.018dB-2.125dB,并且有效地提高了嵌入容量,进而大幅度的提高了嵌入效率。改进算法的嵌入效率达到了Yang算法的1.967-4.683倍。      

A differential index assignment scheme for tree-structured vectorquantization

A differential index (DI) assignment scheme is proposed for the image encoding system in which a variable-length tree-structured vector quantizer (VLTSVQ) is adopted. Each source vector is quantized into a terminal node of VLTSVQ and each terminal node is represented as a unique binary vector. The proposed index assignment scheme utilizes the correlation between interblocks of the image to increase the compression ratio with the image quality maintained. Simulation results show that the proposed scheme achieves a much higher compression ratio than the conventional one does and that the amount of the bit rate reduction of the proposed scheme becomes large as the correlation of the image becomes large. The proposed encoding scheme can be effectively used to encode MR images whose pixel values are, in general, highly correlated with those of the neighbor pixels.     

Space-frequency localized image compression

Subband and wavelet-based image compression can be viewed as frequency oriented techniques because each subimage in the decomposition is essentially a band-pass version of the original image. The authors suggest a space-frequency partition scheme to fully exploit the excellent localization properties of wavelets in both the spatial and frequency domains. Due to the relatively large number of blocks in this partition compared with traditional subband coders, the rate required for communicating the quantizer configurations must be taken into account. An iterative bit allocation algorithm is suggested that minimizes the mean square error given the overall rate for specifying the quantization configuration and for quantizing the wavelet coefficients. Images encoded using scalar quantization under this scheme show improvements in PSNR versus rate over traditional subband and wavelet-based methods.     

Edge-based motion compensated classified DCT with quadtree for image sequence coding

The motion compensated discrete cosine transform coding (MCDCT) is an efficient image sequence coding technique. In order to further reduce the bit-rate for the quantizied DCT coefficients and keep the visual quality, we propose an adaptive edge-based quadtree motion compensated discrete cosine transform coding (EQDCT). In our proposed algorithm, the overhead moving information is encoded by a quadtree structure and the nonedge blocks will be encoded at lower bit-rate but the edge blocks will be encoded at higher bit-rate. The edge blocks will be further classified into four different classes according to the orientations and locations of the edges. Each class of edge blocks selects the different set of the DCT coefficients to be encoded. By this method, we can just preserve and encode a few DCT coefficients, but still maintain the visual quality of the images. In the proposed EQDCT image sequence coding scheme, the average bit-rate of each frame is reduced to 0.072 bit/pixel and the average PSNR value is 32.11 dB.     

Design of vector quantizer for image compression using self-organizing feature map and surface fitting

We propose a new scheme of designing a vector quantizer for image compression. First, a set of codevectors is generated using the self-organizing feature map algorithm. Then, the set of blocks associated with each code vector is modeled by a cubic surface for better perceptual fidelity of the reconstructed images. Mean-removed vectors from a set of training images is used for the construction of a generic codebook. Further, Huffman coding of the indices generated by the encoder and the difference-coded mean values of the blocks are used to achieve better compression ratio. We proposed two indices for quantitative assessment of the psychovisual quality (blocking effect) of the reconstructed image. Our experiments on several training and test images demonstrate that the proposed scheme can produce reconstructed images of good quality while achieving compression at low bit rates. Index Terms-Cubic surface fitting, generic codebook, image compression, self-organizing feature map, vector quantization.     

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.     

A new dynamic finite-state vector quantization algorithm for imagecompression

The picture quality of conventional memory vector quantization techniques is limited by their supercodebooks. This paper presents a new dynamic finite-state vector quantization (DFSVQ) algorithm which provides better quality than the best quality that the supercodebook can offer. The new DFSVQ exploits the global interblock correlation of image blocks instead of local correlation in conventional DFSVQs. For an input block, we search the closest block from the previously encoded data using the side-match technique. The closest block is then used as the prediction of the input block, or used to generate a dynamic codebook. The input block is encoded by the closest block, dynamic codebook or supercodebook. Searching for the closest block from the previously encoded data is equivalent to expand the codevector space; thus the picture quality achieved is not limited by the supercodebook. Experimental results reveal that the new DFSVQ reduces bit rate significantly and provides better visual quality, as compared to the basic VQ and other DFSVQs.