Codebooks with small maximal cross-correlation amplitudes have important applications in code division multiple access (CDMA) communication, coding theory and compressed sensing. In this letter, we design a new codebook based on a construction of Ramanujan graphs over finite abelian groups. We prove that the new codebook with length K=q+1 and size N=q2+2q+2 is asymptotically optimal with nearly achieving the Levenshtein bound when n=3, where q is a prime power. The parameters of the new codebook are new.

Fifth-generation (5G) mobile communication systems employ beamforming technology using massive multiple-input and multiple-output (MIMO) to improve the reception quality and spectrum efficiency within a cell. Meanwhile, coordinated beamforming among multiple base stations is an effective approach to improving the spectrum efficiency at the cell edges, in which massive MIMO is deployed at geographically distant base stations and beamforming control is conducted in a cooperative manner. Codebook-based beamforming is a method for realizing multi-cell coordinated beamforming, in which each base station selects one of multiple beams that are predefined in a codebook. In codebook-based beamforming, it is important to design an efficient codebook that takes into account the beam allocation and the number of beams. In general, the larger the number of beams defined in a codebook, the more finely tuned the beam control can be and a greater improvement in spectrum efficiency can be expected. However, it requires a huge signal processing to optimize the beam combinations with a large number of beams by coordinated beamforming. This paper proposes a novel codebook design that efficiently assigns beam directions and widths in a vertical plane. Computer simulations showed that the proposed codebook performs as well as the conventional method while requiring fewer beam combinations.

Codebooks with small inner-product correlation have applied in direct spread code division multiple access communications, space-time codes and compressed sensing. In general, it is difficult to construct optimal codebooks achieving the Welch bound or the Levenstein bound. This paper focuses on constructing asymptotically optimal codebooks with characters of cyclic groups. Based on the proposed constructions, two classes of asymptotically optimal codebooks with respect to the Welch bound are presented. In addition, parameters of these codebooks are new.

In the letter, two classes of optimal codebooks and asymptotically optimal codebooks in regard to the Levenshtein bound are presented, which are based on mutually unbiased bases (MUB) and approximately mutually unbiased bases (AMUB), respectively.

In this letter, motivated by the research of Tian et al., two constructions of asymptotically optimal codebooks in regard to the Welch bound with additive and multiplicative characters are provided. The parameters of constructed codebooks are new, which are different from those in the letter of Tian et al.

In this letter, as a generalization of Luo et al.'s constructions, a construction of codebook, which meets the Welch bound asymptotically, is proposed. The parameters of codebook presented in this paper are new in some cases.

In this letter, as a generalization of Heng's constructions in the paper [9], a construction of codebooks, which meets the Welch bound asymptotically, is proposed. The parameters of codebooks presented in this paper are new in some cases.

This paper presents a new hole-filling method that uses extrapolated spatio-temporal background information to obtain a synthesized free-view. A new background codebook for extracting reliable temporal background information is introduced. In addition, the paper addresses estimating spatial local background to distinguish background and foreground regions so that spatial background information can be extrapolated. Background holes are filled by combining spatial and temporal background information. Finally, exemplar-based inpainting is applied to fill in the remaining holes using a new priority function. The experimental results demonstrated that satisfactory synthesized views can be obtained using the proposed algorithm.

Side match vector quantization (SMVQ) has been originally developed for image compression and is also useful for steganography. SMVQ requires to create its own state codebook for each block in both encoding and decoding phases. Since the conventional method for the state codebook generation is extremely time-consuming, this letter proposes a fast generation method. The proposed method is tens times faster than the conventional one without loss of perceptual visual quality.

The quality of codebook is very important in visual image classification. In order to boost the classification performance, a scheme of codebook generation for scene image recognition based on parallel key SIFT analysis (PKSA) is presented in this paper. The method iteratively applies classical k-means clustering algorithm and similarity analysis to evaluate key SIFT descriptors (KSDs) from the input images, and generates the codebook by a relaxed k-means algorithm according to the set of KSDs. With the purpose of evaluating the performance of the PKSA scheme, the image feature vector is calculated by sparse code with Spatial Pyramid Matching (ScSPM) after the codebook is constructed. The PKSA-based ScSPM method is tested and compared on three public scene image datasets. The experimental results show the proposed scheme of PKSA can significantly save computational time and enhance categorization rate.

Codebooks with good parameters are preferred in many practical applications, such as direct spread CDMA communications and compressed sensing. In this letter, an upper bound on the set size of a codebook is introduced by modifying the Levenstein bound on the maximum amplitudes of such a codebook. Based on an estimate of a class of character sums over a finite field by Katz, a family of codebooks nearly meeting the modified bound is proposed.

Per-User Unitary Rate Control (PU2RC) performs poorly when the number of users is small and suffers from the sum-rate ceiling effect in the high signal-to-noise ratio (SNR) regime. In this paper, we propose a multimode transmission (MMT) strategy to overcome these inherent shortcomings of PU2RC. In the proposed MMT strategy, the transmitter finds out the optimal transmission mode and schedules users using each user's instantaneous channel quality information (CQI) parameters. First we assume that each user's CQI parameters are perfectly reported in order to introduce the proposed MMT strategy. Then we consider the quantization of CQI parameters using codebooks designed by the Lloyd algorithm. Moreover, we modify the CQI parameters to improve the system's robustness against quantization error. Finally, in order to reduce the quantization error, we design a hierarchical codebook to jointly quantize the modified CQI parameters by considering the correlation between them. Simulation results show that the proposed MMT strategy effectively overcomes the shortcomings of PU2RC and is robust against low quantization level of CQI parameters.

Bag of visual words is a promising approach to object categorization. However, in this framework, ambiguity exists in patch encoding by visual words, due to information loss caused by vector quantization. In this paper, we propose to incorporate patch-level contextual information into bag of visual words for reducing the ambiguity mentioned above. To achieve this goal, we construct a hierarchical codebook in which visual words in the upper hierarchy contain contextual information of visual words in the lower hierarchy. In the proposed method, from each sample point we extract patches of different scales, all of which are described by the SIFT descriptor. Then, we build the hierarchical codebook in which visual words created from coarse scale patches are put in the upper hierarchy, while visual words created from fine scale patches are put in the lower hierarchy. At the same time, by employing the corresponding relationship among these extracted patches, visual words in different hierarchies are associated with each other. After that, we design a method to assign patch pairs, whose patches are extracted from the same sample point, to the constructed codebook. Furthermore, to utilize image information effectively, we implement the proposed method based on two sets of features which are extracted through different sampling strategies and fuse them using a probabilistic approach. Finally, we evaluate the proposed method on dataset Caltech 101 and dataset Caltech 256. Experimental results demonstrate the effectiveness of the proposed method.

In this paper, a block-constrained trellis coded vector quantization (BC-TCVQ) algorithm is combined with an algebraic codebook to produce an algebraic trellis vector code (ATVC) to be used in ACELP coding. ATVC expands the set of allowed algebraic codebook pulse position, and the trellis branches are labeled with these subsets. The Viterbi algorithm is used to select the excitation codevector. A fast codebook search method using an efficient non-exhaustive search technique is also proposed to reduce the complexity of the ATVC search procedure while maintaining the quality of the reconstructed speech. The ATVC block code is used as the fixed codebook of AMR-NB (12.2 kbps), which reduces the computational complexity compared to the conventional algebraic codebook.

In this paper, we present an algorithm for reducing the transmit normalization factor by perturbing the transmit signal in a Multi-User Multiple Input Multiple Output (MU-MIMO) system which uses the channel inverse matrix as its precoding matrix. A base station must normalize unnormalized transmit signals due to the limitation of the constant transmit power. This paper defines the norm of the unnormalized transmit signal as the transmit normalization factor used to normalize the transmit signal. Recalling that the transmit normalization factor consists of a combination of the singular values from the channel inverse matrix, we provide a codebook that successively reduces the coefficients of these singular values. Through computer simulations, the proposed algorithm is compared to sphere encoding in terms of the Bit Error Rate (BER) and the outage probability in a MU-MIMO signal environment. Sphere encoding is known to be an optimal solution amongst the perturbation methods that reduce the transmit normalization factor [1]. This work demonstrates that the proposed algorithm is has very good performance, comparable to that of sphere encoding, while its computational load is nearly 200 times less. Since the codebook in our algorithm depends only on the given channel, the difference in the computational complexity becomes even greater when the channel state is not changed, because the codebook can be reused. Furthermore, the codebook exhibits the characteristic of robustness to the maximum Doppler shift.

A new codebook mapping algorithm for artificial bandwidth extension (ABE) is introduced in this paper. We design a wideband line spectrum pair (LSP) codebook which is coupled with the same index as the LSP codebook of a narrowband speech codec. The received narrowband LSP codebook indices are used to directly induce wideband LSP codewords. Thus, the proposed scheme eliminates codebook search processing to estimate the wideband spectrum envelope. We apply the proposed scheme to bandwidth extension in adaptive multi-rate (AMR) compressed domain. Its performance is assessed via the perceptual evaluation of speech quality (PESQ), informal listening tests, and weighted million operations per second (WMOPS) calculations.

This paper proposes a low power artificial bandwidth extension (ABE) technique that reduces computational complexity by introducing a fast codebook mapping method. We also introduce a weighted classified codebook mapping method for constructing the spectral envelope of the wideband speech signal. Classified codebooks are used to reduce spectrum mapping errors caused by characteristic difference among voiced, unvoiced and onset sound. The weighted distortion measure is also used to handle the spectral sensibility. The performance of the proposed ABE system is evaluated by a spectral distortion (SD), a perceptual evaluation of speech quality (PESQ), informal listening tests and weighted million operations per second (WMOPS) calculations. With the use of fast codebook mapping, the WMOPS complexity of the codebook mapping module is reduced by 45.17%.

In multimedia communication, due to the limited computational capability of the personal information machine, a coder with low computational complexity is needed to integrate services from several media sources. This paper presents two efficient candidate schemes to simplify the most computationally demanding operation, the excitation codebook search procedure. For fast adaptive codebook search, we propose an algorithm that uses residual signals to predict the candidate gain-vectors of the adaptive codebook. For the fixed codebook, we propose a fast search algorithm using an energy function to predict the candidate pulses, and we redesign the codebook structure to twin multi-track positions architecture. Overall simulation results indicate that the average perceptual evaluation of speech quality (PESQ) score is degraded slightly, by 0.049, and our proposed methods can reduce total computational complexity by about 67% relative to the original G.723.1 encoder computation load, and with perceptually negligible degradation. Objective and subjective evaluations verify that the more efficient candidate schemes we propose can provide speech quality comparable to that using the original coder approach.

This paper presents an efficient yet powerful codebook model, named classified codebook model, to categorize natural scene category. The current codebook model typically resorts to large codebook to obtain higher performance for scene categorization, which severely limits the practical applicability of the model. Our model formulates the codebook model with the theory of vector quantization, and thus uses the famous technique of classified vector quantization for scene-category modeling. The significant feature in our model is that it is beneficial for scene categorization, especially at small codebook size, while saving much computation complexity for quantization. We evaluate the proposed model on a well-known challenging scene dataset: 15 Natural Scenes. The experiments have demonstrated that our model can decrease the computation time for codebook generation. What is more, our model can get better performance for scene categorization, and the gain of performance becomes more pronounced at small codebook size.

A structured codebook with various codeword configurations is proposed to overcome the sum capacity limitation in a region with finite number of users. Specifically, based on multi-user MIMO platform with a codebook of multiple orthonormal sets, called as per user unitary rate control (PU2RC), we diversify the codeword configuration of each orthonormal set and expand the corresponding codeword configuration. Numerical experiments with respect to several system parameters, such as user density and received signal to noise ratio, show that the proposed codebook offers throughput gains over the conventional system in a small to moderate number of user region.