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.     

BTC Image Coding Using Vector Quantization

This paper describes source encoding of the outputs of a block truncation coder (BTC), namely, the overhead statistical information and the truncated block. The statistical overhead and the truncated block exhibit properties which can be effectively used for their quantization as vectors. Vector quantization of these BTC outputs results into reduction of the bit rate of the coder. The bit rate reduces up to 1.5 bits/ pel if vector quantization is used on one of the outputs; i.e., either the overhead information or the truncated block. By vector quantizing both the BTC outputs the bit rate can he reduced up to 1.0 bits/pel without introducing many perceivable errors in the reconstructed output.     

Two interpolative BTC image coding schemes

Two interpolative BTC (block truncation coding) image coding schemes with optimal stack filters (in the minimum MAE sense) as the interpolator are suggested. Compared to the standard BTC, the new schemes yield >25% and >37.5% reductions in bit rate, respectively, with little performance degradation. Compared to a previous interpolative BTC coding method, the quality of coded images has been greatly improved with the new schemes (at the same or even a lower rate).<>     

High performance reversible data hiding for block truncation coding compressed images

Reversible data hiding has been a hot research topic because it can recover both the host media and hidden data without distortion. Because most digital images are stored and transmitted in compressed forms, such as JPEG, vector quantization, and block truncation coding (BTC), the reversible data hiding schemes in compressed domains have been paid more and more attention. Compared with transform coding, BTC has a significantly low complexity and less memory requirement, it therefore becomes an ideal data hiding domain. Traditional data hiding schemes in the BTC domain modify the BTC encoding stage or BTC-compressed data according to the secret bits, and they have a relatively low efficiency and meanwhile may reduce the image quality. This paper presents a novel reversible data hiding scheme based on the joint neighbor coding technique for BTC-compressed images by further losslessly encoding the BTC-compressed data according to the secret bits. First, BTC is performed on the original image to obtain the BTC-compressed data that can be represented by a high mean table, a low mean table, and a bitplane sequence. Then, the secret data are losslessly embedded in both the high mean and low mean tables. Our hiding scheme is a lossless method based on the relation among the current value and the neighboring ones in mean tables. In addition, it can averagely embed 2 bits in each mean value, which increases the capacity and efficiency. Experimental results show that our scheme outperforms three existing BTC-based data hiding works, in terms of the bit rate, capacity, and efficiency.     

Adaptive block transform coding of speech based on LPC vectorquantization

The authors describe several adaptive block transform speech coding systems based on vector quantization of linear predictive coding (LPC) parameters. Specifically, the authors vector quantize the LPC parameters (LPCVQ) associated with each speech block and transmit the index of the code vector as overhead information. This code vector will determine the short-term spectrum of the block and, in turn, can be used for optimal bit allocation among the transform coefficients. In order to get a better estimate of the speech spectrum, the authors also consider the possibility of incorporating pitch information in the coder. In addition, entropy-coded zero-memory quantization of the transform coefficients is considered as an alternative to Lloyd-Max quantization. An adaptive BTC scheme based on LPCVQ and using entropy-coded quantizers is developed. Extensive simulations are used to evaluate the performance of this scheme     

VQ Based on a Main Feature Classification in Images

A number of algorithms have been developed for lossy image compression. Among the existing techniques, a block-based scheme is widely used because of its tractability even for complex coding schemes. Fixed block-size coding, which is the simplest implementation of block-based schemes, suffers from the nonstationary nature of images. The formidable blocking artifacts always appear at low bit rates. To suppress this degradation, variable block-size coding is utilized. However, the allowable range of sizes is still limited because of complexity issues. By adaptively representing each region by its feature, input to the coder is transformed to fixed-size (8×8) blocks. This capability allows lower cross-correlation among the regions. Input feature is also classified into the proper group so that vector quantization can maximize its strength compatible with human visual sensitivity. Bit rate based on this algorithm is minimized with the new bit allocation algorithm. Simulation results show a similar performance in terms of PSNR over conventional discrete cosine transform in conjunction with classified vector quantization.     

Precoding and Decoding Paradigms for Distributed Vector Data Compression

In this paper, we consider the problem of lossy coding of correlated vector sources with uncoded side information available at the decoder. In particular, we consider lossy coding of vector source xisinR^N which is correlated with vector source yisinR^N, known at the decoder. We propose two compression schemes, namely, distributed adaptive compression (DAC) and distributed universal compression (DUC) schemes. The DAC algorithm is inspired by the optimal solution for Gaussian sources and requires computation of the conditional Karhunen-Loegraveve transform (CKLT) of the data at the encoder. The DUC algorithm, however, does not require knowledge of the CKLT at the encoder. The DUC algorithms are based on the approximation of the correlation model between the sources y and x through a linear model y=Hx+n in which H is a matrix and n is a random vector and independent of x. This model can be viewed as a fictitious communication channel with input x and output y. Utilizing channel equalization at the receiver, we convert the original vector source coding problem into a set of manageable scalar source coding problems. Furthermore, inspired by bit loading strategies employed in wireless communication systems, we propose for both compression schemes a rate allocation policy which minimizes the decoding error rate under a total rate constraint. Equalization and bit loading are paired with a quantization scheme for each vector source entry (a slightly simplified version of the so called DISCUS scheme). The merits of our work are as follows: 1) it provides a simple, yet optimized, implementation of Wyner-Ziv quantizers for correlated vector sources, by using the insight gained in the design of communication systems; 2) it provides encoding schemes that, with or without the knowledge of the correlation model at the encoder, enjoy distributed compression gains     

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 novel RS BTC coding scheme for optical communications

A novel Reed Solomon(RS) block turbo code(BTC) coding scheme of RS(63,58)×RS(63,58) for optical communications is proposed.The simulation results show that the net coding gain(NCG) of this scheme at the sixth iteration is more than that of other coding schemes at the third iteration for the bit error rate(BER) of 10-12.Furthermore,the novel RS BTC has shorter component code and rapider encoding and decoding speed.Therefore,the novel RS BTC coding scheme can be better used in high-speed long-haul optical communication systems,and the novel RS BTC can be regarded as a candidate code of the super forward error correction(super-FEC) code.Moreover,the encoding/decoding design and implementation of the novel RS BTC are also presented.     

On adaptive vector transform quantization for speech coding

Adaptive vector transform quantization (AVTQ) as a coding system is discussed. The optimal bit assignment is derived based on vector quantization asymptotic theory for different PDFs (probability density functions) of the transform coefficients. Strategies for shaping the quantization noise spectrum and for adapting the bit assignment to the changes in the speech statistics are discussed. A good estimate of the efficiency of any coding system is given by the system coding gain over scalar PCM (pulse code modulation). Based on the optimal bit allocation, the coding gain of the vector transform quantization (VTQ) system operating on a stationary input signal is derived. The VTQ coding gain demonstrates a significant advantage of vector quantization over scalar quantization within the framework of transform coding. System simulation results are presented for a first-order Gauss-Markov process and for typical speech waveforms. The results of fixed and adaptive systems are compared for speech input. Also, the AVTQ results are compared to known scalar speech coding systems     

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.     

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.     

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.     

Subband Finite-State Vector Quantization for Still Image Coding

Subband coding (SBC) with vector quantization (VQ) has been shown to be an effective method for coding images at low bit rates. The basic idea of subband coding is to split up the frequency band of the signal and then to encode the subbands. Reconstruction is performed by decoding and merging the interpolated subband images. In VQ, the image to be encoded is first processed to yield a set of vectors. The input vectors are individually quantized to the closest codewords in the codebook. In this paper, we propose a new subband finite-state vector quantization (SBC-FSVQ) scheme that combines the SBC and the FSVQ. The frequency band decomposition of an image is carried out by means of 2D separable quadrature mirror filters (QMFs). In our coding scheme, we split the image spectrum into sixteen equally sized subbands. The FSVQ is used to improve the performance by using the correlations of the neighboring samples in the same subband. Thus, our SBC-FSVQ scheme not only has the advantages of the SBC-VQ scheme but also reduces the bit rate and improves the image quality. Experimental results are given and comparisons are made using our new schemes and some other coding techniques. Our technique yields good PSNR performance, for images both inside and outside a training set of five 512 × 512 images. In the experiments, it is found that our SBC-FSVQ scheme achieves the best PSNR performance at nearly the same bit rate.     

Image coding using entropy-constrained residual vector quantization

An entropy-constrained residual vector quantization design algorithm is used to design codebooks for image coding. Entropy-constrained residual vector quantization has several important advantages. It can outperform entropy-constrained vector quantization in terms of rate-distortion performance, memory, and computation requirements. It can also be used to design vector quantizers with relatively large vector sizes and high output rates. Experimental results indicate that good image reproduction quality can be achieved at relatively low bit rates. For example, a peak signal-to-noise ratio of 30.09 dB is obtained for the 512x512 LENA image at a bit rate of 0.145 b/p.     

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.     

Codage audio haute qualité (20 Hz-15 kHz) en sous bandes à débit réduit (64 kbit/s) et à faible retard (5 ms)

In this paper, we present a new method for high quality audio coding at low delay and low bit rate for telecommunications applications such as audioconfe-rence or videoconference. The developped coder is adapted to code generic audio signals at a bit rate of 64 kbit/s with a delay close to 5 ms in the 20-15000 Hz bandwidth. The method is based on speech coding as well as audio coding concepts. The coder combines subband decomposition of the input signal and LD-CELP techniques. We introduce in this structure of coding a psychoacoustic model which allows to allocate an optimal bit rate on each subband according to perceptual properties of the human hearing. In order to satisfy the bit rate requirement of the psychoacoustic model and to reduce the complexity of such a coding algorithm, we suggested a new method of vector quantization based on lattice quantization. This method allows to quantify the residual signal in the LD-CELP coder and avoid the complexity of the full search. Objective and subjective tests have been made on a test set of audio signals which is a critical sub-set used by ISO. Formal tests showed that the quality of the proposed coder is comparable to the best implementation of the MPEG-1, Layer II, but our solution has the advantage of reaching a very low delay (5 ms).     

面向窄带通信的极低速率语音编码算法研究

提出了一种面向窄带通信的极低速率参数语音编码算法。在2.4kbps MELP标准的基础上结合听觉感知,对线谱对参数进行联合矢量量化、对基音周期进行内插和非线性量化、对能量参数进行高效压缩,可以使语音数据在0.5kbps下匀速传输;线谱对参数的预测残差用于矢量量化,这是一种提高合成语音的音质的有效方法。实验结果表明,采用本文提出的语音编码算法可以使语音数据在极低码率下有效的传输,解码端合成的语音具有较高的可懂度。      

Image compression using VQ-BTC

Vector quantization (VQ) and block truncation coding (BTC) are successful image compression techniques. However, a reproduced image using VQ or BTC suffers from edge degradation. A new technique that combines the advantages of both VQ and BTC to combat this degradation is presented and is referred to as VQ-BTC. In VQ-BTC, a low-detail block is encoded using VQ. For a high-detail block, a modification of BTC is used to determine the locations of the relatively lighter and relatively darker pixels inside the block and VQ is then used to encode each. VQ-BTC provides improved edge reproduction and much lower bit rates than those obtained by BTC     

Novel Rate Control Scheme for Low Delay Video Coding of HEVC

In this paper, a novel rate control scheme for low delay video coding of High Efficiency Video Coding (HEVC) is proposed. The proposed scheme is developed by considering a new temporal prediction structure of HEVC. In the proposed scheme, the relationship between bit rate and quantization step is exploited firstly to formulate an accurate quadratic rate‐quantization (R‐Q) model. Secondly, a method of determining the quantization parameters (QPs) for the first frames within a group of pictures is proposed. Thirdly, an accurate frame‐level bit allocation method is proposed for HEVC. Finally, based on the proposed R‐Q model and the target bit allocated for the frame, the QPs are predicted for coding tree units by using rate‐distortion (R‐D) optimization. We compare our scheme against that of three other state‐of‐the‐art rate control schemes. Experimental results show that the proposed rate control scheme can increase the Bjøntegaard delta peak signal‐to‐noise ratio by 0.65 dB and 0.09 dB on average compared with the JCTVC‐I0094 and JCTVC‐M0036 schemes, respectively, both of which have been implemented in an HEVC test model encoder; furthermore, the proposed scheme achieves a similar R‐D performance to Wang's scheme, as well as obtaining the smallest bit rate mismatch error of all the schemes.