Smooth side-match classified vector quantizer with variable blocksize

Although the side-match vector quantizer (SMVQ) reduces the bit rate, the image coding quality by SMVQ generally degenerates as the gray level transition across the boundaries of the neighboring blocks is increasing or decreasing. This study presents a smooth side-match method to select a state codebook according to the smoothness of the gray levels between neighboring blocks. This method achieves a higher PSNR and better visual perception than SMVQ does for the same bit rate. Moreover, to design codebooks, a genetic clustering algorithm that automatically finds the appropriate number of clusters is proposed. The proposed smooth side-match classified vector quantizer (SSM-CVQ) is thus a combination of three techniques: the classified vector quantization, the variable block size segmentation and the smooth side-match method. Experimental results indicate that SSM-CVQ has a higher PSNR and a lower bit rate than other methods. Furthermore, the Lena image can be coded by SSM-CVQ with 0.172 bpp and 32.49 dB in PSNR.     

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.     

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.     

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.     

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.     

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.     

Transform and Hybrid Transform/DPCM Coding of Images Using Pyramid Vector Quantization

The transform and hybrid transform/DPCM methods of image coding are generalized to allow pyramid vector quantization of the transform coefficients. An asymptotic mean-squared error performance expression is derived for the pyramid vector quantizer and used to determine the optimum rate assignment for encoding the various transform coefficients. Coding simulations for two images at average rates of 0.5-1 bit/pixel demonstrate a 1-3 dB improvement in signal-to-noise ratio for the vector quantization approach in the hybrid coding, with more modest improvements in signal-to-noise ratio in the transform coding. However, this improvement is quite noticeable in image quality, particularly in reducing "blockiness" in the low bit rate encoded images.     

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.     

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 VLSI architecture for real-time image coding using a vectorquantization based algorithm

Digital image coding using vector quantization (VQ) based techniques provides low-bit rates and high quality coded images, at the expense of intensive computational demands. The computational requirement due to the encoding search process, had hindered application of VQ to real-time high-quality coding of color TV images. Reduction of the encoding search complexity through partitioning of a large codebook into the on-chip memories of a concurrent VLSI chip set is proposed. A real-time vector quantizer architecture for encoding color images is developed. The architecture maps the mean/quantized residual vector quantizer (MQRVQ) (an extension of mean/residual VQ) onto a VLSI/LSI chip set. The MQRVQ contributes to the feasibility of the VLSI architecture through the use of a simple multiplication free distortion measure and reduction of the required memory per code vector. Running at a clock rate of 25 MHz the proposed hardware implementation of this architecture is capable of real-time processing of 480×768 pixels per frame with a refreshing rate of 30 frames/s. The result is a real-time high-quality composite color image coder operating at a fixed rate of 1.12 b per pixel     

Image compression using address-vector quantization

A novel vector quantization scheme, called the address-vector quantizer (A-VQ), is proposed. It is based on exploiting the interblock correlation by encoding a group of blocks together using an address-codebook. The address-codebook consists of a set of address-codevectors where each codevector represents a combination of addresses (indexes). Each element of this codevector is an address of an entry in the LBG-codebook, representing a vector quantized block. The address-codebook consists of two regions: one is the active (addressable) region, and the other is the inactive (nonaddressable) region. During the encoding process the codevectors in the address-codebook are reordered adaptively in order to bring the most probable address-codevectors into the active region. When encoding an address-codevector, the active region of the address-codebook is checked, and if such an address combination exist its index is transmitted to the receiver. Otherwise, the address of each block is transmitted individually. The quality (SNR value) of the images encoded by the proposed A-VQ method is the same as that of a memoryless vector quantizer, but the bit rate would be reduced by a factor of approximately two when compared to a memoryless vector quantizer     

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.     

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.     

Sample-adaptive product quantization: asymptotic analysis andexamples

Vector quantization (VQ) is an efficient data compression technique for low bit rate applications. However the major disadvantage of VQ is that its encoding complexity increases dramatically with bit rate and vector dimension. Even though one can use a modified VQ, such as the tree-structured VQ, to reduce the encoding complexity, it is practically infeasible to implement such a VQ at a high bit rate or for large vector dimensions because of the huge memory requirement for its codebook and for the very large training sequence requirement. To overcome this difficulty, a structurally constrained VQ called the sample-adaptive product quantizer (SAPQ) has recently been proposed. We extensively study the SAPQ that is based on scalar quantizers in order to exploit the simplicity of scalar quantization. Through an asymptotic distortion result, we discuss the achievable performance and the relationship between distortion and encoding complexity. We illustrate that even when SAPQ is based on scalar quantizers, it can provide VQ-level performance. We also provide numerical results that show a 2-3 dB improvement over the Lloyd-Max (1982, 1960) quantizers for data rates above 4 b/point     

IMPLEMENTING THE ADAPTIVE VECTOR QUANTIZER USING CARPENTER/GROSSBERG NET

AVQ(Adaptive Vector Quantizer)overcomes some shortcomings of traditional vectorquantizer with a fixed codebook trained and generated by the LBG or other algorithms by applyinga variab|e codebook.In this paper,we describe an effective and efficient implementation of AVQby modifying the CCN(Carpenter/Grossberg Net).The encoding process of AVQ is very similarto the learning process of the CGN.We study several different encoding schemes,includingwaveform AVQ,analysed parameter AVQ and so on,implemented by the CGN.And we simulatethe encoding performance of each scheme for encoding Gaussian process source,first order Gauss-Markov process source and practical speech signal.Our simulation results show that good qualityboth in subjective and objective tests can be obtained in a low or middle bit rate range.     

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％。     

Interframe DPCM with Adaptive Quantization and Entropy Coding

The object of this work has been to study encoding of monochrome pictures with a rate of about 1 bit per picture element (pel). Differential pulse code modulation, DPCM, has been chosen to make the system reasonably simple. This motivates the choice of a fixed three-dimensional predictor made separable in time and space. It essentially amounts to encoding the frame difference using a two dimensional predictor. To improve performance, the quantizer is made adaptive and the encoding is combined with delayed decision. A three-level quantizer is being used with an adaptive scheme that works with either forward or backward estimation. The output from the DPCM unit is redundant and we have adopted entropy coding to further reduce the bit rate. The entropy measure is also used to adjust the quantizer parameters to achieve the desired bit rate of 1 bit/pel. Encoding experiments were carried out on a complex videophone scene by means of computer simulations.     

A cascading MRVQ-DCT scheme for the compression of sequence images

In this correspondence, a mean residual vector quantizer (MRVQ) is superimposed on the motion estimator for improving the intraframe coding quality of MPEG. The proposed scheme performs an MRVQ before executing the discrete cosine transformation (DCT) for the purpose of curing the DCT intrinsic trouble of blocking effect at low bit rate.     

Soft-decoding vector quantizer using reliability information fromturbo-codes

The optimum soft-decoding vector quantizer using the reliability information from turbo-codes is derived for combined source-channel coding. The encoder and decoder of the quantizer are optimized iteratively. For a four-dimensional vector quantizer having a rate of 1 bit/sample transmitted through a noisy channel, the soft-decoding channel-optimized quantizer can achieve about 3-3.7 dB performance improvement over conventional source-optimized quantizer