A fast VQ codeword search algorithm for AMR Wideband speech codec

The adaptive multi-rate wideband (AMR-WB) speech codec is widely used in modern mobile communication systems for high speech quality in handheld devices. Nonetheless, a major disadvantage is that vector quantization (VQ) of immittance spectral frequency (ISF) coefficients takes a considerable computational load in the AMR-WB coding. Accordingly, a binary search space-structured VQ (BSS-VQ) algorithm is adopted to efficiently reduce the complexity of ISF quantization in AMR-WB. This search algorithm is done through a fast locating technique combined with lookup tables, such that an input vector is efficiently assigned to a subspace where relatively few codeword searches are required to be executed. In terms of overall search performance, this work is experimentally validated as a superior search algorithm relative to a multiple triangular inequality elimination (MTIE), a TIE with dynamic and intersection mechanisms (DI-TIE), and an equal-average equal-variance equal-norm nearest neighbor search (EEENNS) approach. With a full search algorithm as a benchmark for overall search load comparison, this work provides an 87% search load reduction at a threshold of quantization accuracy of 0.96, a figure far beyond 55% in the MTIE, 76% in the EEENNS approach, and 83% in the DI-TIE approach.

Vector quantization is a popular data compression method with a widespread use in signal processing and pattern recognition applications. However its computational demand is increased tremendously as the size of date set and the dimension of samples grow larger. Here we have proposed a new fast-search algorithm to enhance the computational cost and the execution time of the conventional VQ method. This algorithm relies on the Chebyshev distance for distortion measurement and Triangular Inequality Elimination (TIE) for codeword elimination. The proposed algorithm was employed for the recognition of isolated spoken Persian digits. Results show that this algorithm can remarkably reduce the number of calculations while its encoding error is closed to the full search algorithm.

A fast codeword search algorithm for vector quantization is presented. Three inequalities based on the characteristics of sums and variances of a vector and its two sub-vectors are introduced to reject a larger number of codewords. Simulation results demonstrate the effectiveness of the proposed algorithm.

A novel variable-rate image-coding scheme called the predictive subcodebook search algorithm (PSS) is proposed. In PSS, the initial searched codeword for each input vector is predicted by its encoded neighbors and the closest codeword in the codebook is searched from a specific range (or subcodebook) that is determined dynamically according to the block type of its encoded neighbors. In addition, the relative addressing technique is employed to encode the searched closest codeword. According to the simulation results, it is shown that PSS not only provides better objective image quality peak SNR (PSNR) but also reduces the storage cost for storing multiple codebooks. Furthermore, PSS requires very little computational cost, compared to the full search algorithm for the vector quantization (VQ) scheme. Therefore, it is concluded that PSS indeed provides a good means for variable-rate image coding.

In this paper, we derived a relationship between Linear Predictive Coding (LPC) coefficients and Immittance Spectral Pairs (ISP) pseudo-cepstrum. In Adaptive Multi-Rate Wideband(AMR-WB) speech codec, ISP parameters were used for quantisation and interpolation. This paper first described the representation of ISP pseudo-cepstrum. ISP pseudo-cepstrum was defined as the inverse z-transform of half natural logarithm of immittance function. References [2] and [3] proposed two kinds of new perceptual weighting filters based on ISP pseudo-cepstrum. Spectral tilt is more pronounced in wideband signals due to the wide dynamic range between low and high frequencies. The proposed filters are helpful for solving this problem. Then we gave the conversion method between ISP pseudo-cepstrum and polynomials cepstrum coefficients. Finally we derived the recursive relations between ISP pseudo-cepstrum and LPC coefficients. There are two methods of converting LPC coefficients to ISP pseudo-cepstrum. The one is directly from LPC coefficients to ISP pseudo-cepstrum. The other is from ISP parameters.

Due to its applications in cloud computing, research on reversible data hiding in encrypted images (RDHEI) is becoming more and more important. This paper proposes a reversible data hiding scheme for encrypted images that utilizes an all-permutation technique to embed data into encrypted images. The proposed scheme follows a block-wise data hiding process. Message extraction and image restoration are performed by the receiver using the trained vector quantization (VQ) codebook. This scheme can provide a high embedding rate and reduce the hardware burden on the receiver.

The adaptive codebook used in code-excited linear prediction (CELP)-like speech codecs is very effective for modeling the quasi-periodic component of the excitation signal but, unfortunately, introduces a strong interframe dependency that renders the decoder vulnerable to frame erasures. For voiced speech, the error affects not only the erased frame but also all the subsequent frames. In this paper, a technique to improve the recovery after a frame erasure is proposed. The technique consists in a constrained excitation search at the encoder and a resynchronization procedure at the decoder. The constraint aims at reducing the contribution of the adaptive codebook by making the innovation codebook partially model the pitch excitation. Further, for highly voiced frames, the pitch-related information contained in the innovation excitation is exploited at the decoder to speed up the resynchronization of the adaptive codebook after a frame erasure. When applied to the adaptive multirate wideband (AMR-WB) codec, the method brings a significant improvement in the case of frame erasures, at the cost of a minor quality loss compared to the standard codec at the same bit rate. The method does not need additional delay and has the advantage of maintaining full interoperability between the standard codec and its modified version.

In this paper we propose partial yet efficient codebook search algorithm which uses sorting technique and uses only comparison. Our proposed algorithm does not use Euclidean distance computation and hence it is fastest as compared to other search methods ES, HOSM, DTPC. Form the results it is observed that proposed algorithm gives more MSE as compared to the exhaustive search method but with good execution speed. We also discuss codebook design methods LBG and FCG. The codebooks of different sizes 128, 256, 512 and 1024 are generated using LBG and FCG algorithm. Both the codebook generation algorithms are compared with respect to the execution speed. All the various search algorithms are implemented on the codebooks of different sizes 128, 256, 512 and 1024 obtained from LBG and FCG algorithms. From the results it is observed that FCG codebook gives better performance parameters MSE and PSNR as compared to LBG codebook and among the search algorithm proposed algorithm gives least time to encode the image with slight degradation in image quality.

Summary form only given. Vector quantization (VQ),is a well-known compression technique which has been widely used in many speech and image coding systems. Techniques for codebook design attempt to produce a codebook that is optimum for a given source. To date, the most widely used technique for VQ codebook design is the LBG (Linde-Buzo-Gray) algorithm. Madeim et al. (1999) show that an unsupervised neural network algorithm, referred to as SOA (self-organizing algorithm), provides good VQ codebooks, leading to reconstructed signals with better quality when compared to the ones obtained by using LBG codebooks. In this paper, an investigation is carried out to evaluate the inherent quality of SOA and LBG codebooks regarding the computational complexity of medium distortion encoding. The present work shows that the SOA codebooks overperforms the LBG codebooks in the sense that they yield a smaller average number of multiplications per sample for image VQ.

An ideal image compression technique for image data archival, retrieval and distribution would be one with the asymmetrical computational requirements of vector quantization (VQ), but without the complications arising from VQ codebooks. Codebook generation and maintenance are stumbling blocks which have limited the use of VQ as a practical image compression algorithm. Model-based VQ (MVQ), a variant of VQ described here, has the computational properties of VQ but does not require explicit codebooks. The codebooks are internally generated using mean removed error and human visual system (HVS) models. The error model assumed is the Laplacian distribution with mean, (lambda) , computed from a sample of the input image. A Laplacian distribution with mean, (lambda) , is generated with a uniform random number generator. These random numbers are grouped into vectors. These vectors are further conditioned to make them perceptually meaningful by filtering the DCT coefficients from each vector. The DCT coefficients are filtered by multiplying by a weight matrix that is found to be optimal for human perception. The inverse DCT is performed to produced the conditioned vectors for the codebook. The only image dependent parameter used in the generation of codebook is the mean, (lambda) , that is included in the coded file to repeat the codebook generation process for decoding.

This paper discusses some algorithms to be used for the generation of an efficient and robust codebook for vector quantization (VQ). Some of the algorithms reduce the required codebook size by 4 or even 8 b to achieve the same level of performance as some of the popular techniques. This helps in greatly reducing the complexity of codebook generation and encoding. We also present a new adaptive tree search algorithm which improves the performance of any product VQ structure. Our results show an improvement of nearly 3 dB over the fixed rate search algorithm at a bit rate of 0.75 b/pixel. >

Image retrieval and image compression are both very active fields of research. Unfortunately, in the past they were pursued independently leading to image indexing methods being both efficient and effective but restricted to uncompressed images. In this paper we introduce an image retrieval technique that operates in the compressed domain of vector quantize images. Vector quantization (VQ) achieves compression by representing image blocks as indices into a codebook of prototype blocks. By realizing that, if images are coded with their own VQ codebook then much of the image information is contained in the codebook itself, we propose the comparison of the codebooks, based on a Modified Hausdorff distance, as a novel method for compressed domain image retrieval. Experiments, based on an image database comprising many colorful pictures show this technique to give excellent results, outperforming classical color indexing techniques.

This work presents an analysis of a vector quantization (VQ) process based on Kohonen's self‐organizing feature map algorithm [T. Kohonen, Self‐Organization and Associative Memory (Springer, New York, 1988), 2nd ed.]. This process is an unsupervised learning technique that converges to a set of ordered nodes that represent cluster centers. These ordered nodes, the VQ codebook elements, are closely related to the sample density function of the training data. The VQ codebooks generated with this algorithm are compared to those generated with a previously developed speech VQ algorithm based on K‐means clustering. Performance criteria include the average distortion between input vectors and their coded values and the maximization of entropy, a measure of the distribution of codebook element usage. The Kohonen algorithm provides superior codebooks without requiring more processing. Also, because the codebooks generated by this algorithm are ordered and reflect the statistical characteristics of the training data, the codebooks are useful tools for speech analysis, as well as for speech categorization.

It is well known that precoding techniques in multiuser multiple-input multiple-output (MU-MIMO) communication systems improve the whole data throughput since they allow for spatial multiplexing of different users. However, these techniques require a precise knowledge of the channel information at the transmitter. In most of the current systems, transmitter can not estimate the channel so feedback information has to be provided by the receiver. In this paper, we propose a new limited feedback approach where Vector Quantization (VQ) is applied over the channel frequency response. Different configurations of feedback bits per subcarrier in a multiuser MIMO-OFDM system have been simulated. Additionally, different precoding schemes based on the estimated channel information have been tested and BER performance has been evaluated. Results show that, for a similar cost of feedback information (bits/subcarrier), better channel estimates are achieved using longer vectors in the VQ codebook, whereas optimal non-linear precoding schemes perform well even for large channel estimation errors.

Image compression frequently supports reduced storage requirement in a computer system, as well as enhancement of effective channel bandwidth in a communication system, by decreasing the source bit rate through reduction of source redundancy. The majority of image compression techniques emphasize pixel-level operations, such as matching rectangular or elliptical sampling blocks taken from the source data stream, with exemplars stored in a database (e.g., a codebook in vector quantization or VQ). Alternatively, one can represent a source block via transformation, coefficient quantization, and selection of coefficients deemed significant for source content approximation in the decompressed image. This approach, called transform coding (TC), has predominated for several decades in the signal and image processing communities. A further technique that has been employed is the deduction of affine relationships from source properties such as local self-similarity, which supports the construction of adaptive codebooks in a self-VQ paradigm that has been called <i>iterated function systems </i>(IFS). Although VQ, TC, and IFS based compression algorithms have enjoyed varying levels of success for different types of applications, bit rate requirements, and image quality constraints, few of these algorithms examine the higher-level spatial structure of an image, and fewer still exploit this structure to enhance compression ratio. In this paper, we discuss a fourth type of compression algorithm, called <i>object-based compression</i>, which is based on research in joint segmentaton and compression, as well as previous research in the extraction of sketch-like representations from digital imagery. Here, large image regions that correspond to contiguous recognizeable objects or parts of objects are segmented from the source, then represented compactly in the compressed image. Segmentation is facilitated by source properties such as size, shape, texture, statistical properties, and spectral signature. In particular, discussion addresses issues such as efficient boundary representation, variance assessment and representation, as well as a texture classification and replacement algorithms that can decrease compression overhead and increase reconstruction fidelity in the decompressed image. Contextual extraction of motion patterns in digital video sequences, using a frequency-domain pattern recognition technique based on interframe correlation, is described in a companion paper. This technique can also be extended to multidimensional image domains, to support joint spectral, spatial, and temporal compression.