Optimal Codebook Research Articles

A novel image compression model by adaptive vector quantization: modified rider optimization algorithm

In recent days over the internet, the uploading of enormous new images is being made every day, and they necessitate large storage to accumulate the image data. For the earlier few decades, more analysts have evolved skillful image compression schemes to enhance the compression rates and the image quality. In this work, Vector Quantization is used, which uses the Linde–Buzo–Gray algorithm. As a novel intention, the codebooks are optimized by an improved optimization algorithm. In this approach, the database image is firstly separated into a set of blocks, i.e., pixels, and these sets of blocks are referred to as vectors. Then a suitable codeword is selected for each vector such that is the closest representation of that input vector. The encoder generates a codebook by mapping the vectors on the basis of these code words, and the compression of the vectors takes place. The encoder then sends a compressed stream of these vectors by pointing out their indices from the codebook to the decoder through a channel. The decoder then decodes the index to find out the compressed vector and places it on the image. For attaining a better image compression effect, the codebook is optimized using the Best Fitness Updated Rider Optimization Algorithm. The optimization of codebooks is done so that the summation of the compression ratio and the error difference between the original and decompressed images has to be minimized. Moreover, the proposed model is scruntized with other existing algorithms, and the experimental outcomes are validated.

Two constructions of asymptotically optimal codebooks via the trace functions

In this paper, we present two new constructions of complex codebooks with multiplicative characters, additive characters and trace functions over finite fields, and determine the maximal cross-correlation amplitude of these codebooks. We prove that the codebooks we constructed are asymptotically optimal with respect to the Welch bound. Moreover, in the first construction, we generalize the result in [28]. In the second construction, we generalize the results in [12], we can achieve Welch bound for any odd prime p, we also derive the whole distribution of their inner products. The parameters of these codebooks are new.

User Preference Aware Lossless Data Compression at the Edge

Data compression is an efficient technique to save data storage and transmission costs. However, traditional data compression methods always ignore the impact of user preferences on the statistical distributions of symbols transmitted over the links. Notice that the development of big data technologies and popularization of smart devices enable analyses on user preferences based on data collected from personal handsets. This paper presents a user preference aware lossless data compression method, termed edge source coding, to compress data at the network edge. An optimization problem is formulated to minimize the expected number of bits needed to represent a requested content item in edge source coding. For edge source coding under discrete user preferences, DCA (difference of convex functions programming algorithm) based and k-means++ based algorithms are proposed to give codebook designs. For edge source coding under continuous user preferences, a sampling method is applied to give codebook designs. In addition, edge source coding is extended to the two-user case and codebooks are elaborately designed to utilize multicasting opportunities. Both theoretical analysis and simulations demonstrate the optimal codebook design should take into account user preferences.

Welch bound-achieving compressed sensing matrices from optimal codebooks

Compressed sensing (CS) is a new data acquisition theory taking full use of the sparsity of signals. It reveals that higher-dimensional sparse signals can be reconstructed from fewer nonadaptive linear measurements. The construction of CS matrices in CS is the key problem. In this paper, the deterministic CS matrices from optimal codebooks are constructed. Furthermore, the maximum sparsity of recovering the sparse signals by using our CS matrices are obtained. Meanwhile, a comparison is made with the CS matrices constructed by DeVore based on polynomials over finite fields. In the numerical simulations, our CS matrix outperforms DeVore[Formula: see text]s matrix in the process of recovering sparse signals.

Six constructions of asymptotically optimal codebooks via the character sums

In this paper, using additive characters of finite field, we find a codebook which is equivalent to the measurement matrix in [20]. The advantage of our construction is that it can be generalized naturally to construct the other five classes of codebooks using additive and multiplicative characters of finite field. We determine the maximal cross-correlation amplitude of these codebooks by the properties of characters and character sums. We prove that all the codebooks we constructed are asymptotically optimal with respect to the Welch bound. The parameters of these codebooks are new.

An evolutionary lion optimization algorithm‐based image compression technique for biomedical applications

AbstractRecently, medical image compression becomes essential to effectively handle large amounts of medical data for storage and communication purposes. Vector quantization (VQ) is a popular image compression technique, and the commonly used VQ model is Linde–Buzo–Gray (LBG) that constructs a local optimal codebook to compress images. The codebook construction was considered as an optimization problem, and a bioinspired algorithm was employed to solve it. This article proposed a VQ codebook construction approach called the L2‐LBG method utilizing the Lion optimization algorithm (LOA) and Lempel Ziv Markov chain Algorithm (LZMA). Once LOA constructed the codebook, LZMA was applied to compress the index table and further increase the compression performance of the LOA. A set of experimentation has been carried out using the benchmark medical images, and a comparative analysis was conducted with Cuckoo Search‐based LBG (CS‐LBG), Firefly‐based LBG (FF‐LBG) and JPEG2000. The compression efficiency of the presented model was validated in terms of compression ratio (CR), compression factor (CF), bit rate, and peak signal to noise ratio (PSNR). The proposed L2‐LBG method obtained a higher CR of 0.3425375 and PSNR value of 52.62459 compared to CS‐LBG, FA‐LBG, and JPEG2000 methods. The experimental values revealed that the L2‐LBG process yielded effective compression performance with a better‐quality reconstructed image.

A New Construction of Codebooks Meeting the Levenshtein Bound

Codebooks with low coherence have extensive applications in many fileds such as code division multiple access (CDMA) communication systems, MIMO communications, compressed sensing and so on. In this paper, based on additive characters over finite fields, we propose a construction of optimal codebook with respect to the Levenshtein bound and verify that it is a new construction. By shortening the length of the optimal codebooks, we present a construction of codebooks asymptotically meeting the Levenshtein bound. To the best of our knowledge, the parameters of the asymptotically optimal codebooks are new.

Asymptotically Optimal Codebooks Derived From Generalised Bent Functions

Codebooks are required to have small inner-product correlation in many practical applications, such as direct spread code division multiple access communications, space-time codes and compressed sensing. In general, it is difficult to construct optimal codebooks. In this paper, two kinds of codebooks are presented and proved to be optimally optimal with respect to the Welch bound. Additionally, the constructed codebooks in this paper have new parameters.

An Uplink SCMA Codebook Design Combining Probabilistic Shaping and Geometric Shaping

Sparse Code Multiple Access (SCMA) is a promising non-orthogonal multiple access scheme owning to the shaping gain of its multi-dimensional codebook. Most of the existing designs of the codebook are based on geometric shaping (GS), while probabilistic shaping (PS) has important advantages in increasing channel capacity and reducing bit error rate. In this paper, an uplink SCMA codebook optimization algorithm which combines PS and GS is proposed. The algorithm adopts the bare bones particle swarm optimization algorithm based on maximizing the AMI. Based on the non-equiprobable codebook, a message passing algorithm with non-equalprobable distribution is introduced as the multiuser detection algorithm for SCMA scheme. We theoretically analyze the superiority of the codebooks combining PS and GS over others in terms of the average mutual information (AMI). Simulation results show that our proposed codebooks outperform the reference GS based codebooks under different overloading, different channels and different codebook sizes, and with the increase of overloading or codebook sizes, the gains are greater, which is up to 2.1dB gain in terms of block error rate performance. This observation also validates the theoretical analysis based on the AMI.

A novel deep neural network based approach for sparse code multiple access

Sparse code multiple access (SCMA) has been one of the non-orthogonal multiple access (NOMA) schemes aiming to support high spectral efficiency and ubiquitous access requirements for 5G communication networks. Conventional SCMA approaches are confronting challenges in designing low-complexity high-accuracy decoding algorithm and constructing optimum codebooks. Fortunately, the recent spotlighted deep learning technologies are of significant potentials in solving many communication engineering problems. Inspired by this, we propose and train a deep neural network (DNN) called DL-SCMA to learn to decode SCMA modulated signals corrupted by additive white Gaussian noise (AWGN). An autoencoder called AE-SCMA is established and trained to generate optimal SCMA codewords and reconstruct original bits. Furthermore, by manipulating the mapping vectors, an autoencoder is able to generalize SCMA, thus a dense code multiple access (DCMA) scheme is proposed. Simulations show that the DNN SCMA decoder significantly outperforms the conventional message passing algorithm (MPA) in terms of bit error rate (BER), symbol error rate (SER) and computational complexity, and AE-SCMA also demonstrates better performances via constructing better SCMA codebooks. The performance of deep learning aided DCMA is superior to the SCMA.

Four Constructions of Asymptotically Optimal Codebooks via Additive Characters and Multiplicative Characters

In this paper, we present four new constructions of complex codebooks with multiplicative characters, additive characters, and quadratic irreducible polynomials and determine the maximal cross-correlation amplitude of these codebooks. We prove that the codebooks we constructed are asymptotically optimal with respect to the Welch bound. Moreover, we generalize the result obtained by Zhang and Feng and contain theirs as a special case. The parameters of these codebooks are new.

Codebooks from generalized bent [formula omitted]-valued quadratic forms

Codebooks with small inner-product correlation have applications in unitary space–time modulations, multiple description coding over erasure channels, direct spread code division multiple access communications, compressed sensing, and coding theory. It is interesting to construct codebooks (asymptotically) achieving the Levenshtein bound. This paper presents a class of generalized bent Z4-valued quadratic forms, which contains functions proposed by Heng and Yue (2017). Using these generalized bent Z4-valued quadratic forms, we construct optimal codebooks achieving the Levenshtein bound. These codebooks have parameters (22m+2m,2m) and alphabet size 6.

Using Vector Quantization Technique in Recognition of Gurbani Hymns (Japji Sahib): LBG Algorithm (VQ)

An improved and different variation of Automatic Speech Recognition (ASR) is presented which is based on Vector Quantization (VQ). ASR for different languages and different applications has been introduced so far. In this paper, we have presented a Speech Recognition system to recognize the hymns (paath) of Gurbani (sentences of Japji Sahib) as continuous mode of speech. For this, speech corpus has been generated in which the entire path has been recited by different speakers. The speech mode here can be taken as continuous speech encapsulated with background music and different kinds of additional noises and have been eliminated. The work has been done by using VQ approach of speech recognition and LBG algorithm which design optimal codebooks for the process of recognition. Experimental results are included which show that recognition accuracy for such system was found to be 92.6% and 95.8% for different and same speakers with different and same sentences.

Fast Convergence Algorithms for Coherence Optimization of Rank-1 Grassmannian Codebooks

Fast Convergence Algorithms for Coherence Optimization of Rank-1 Grassmannian Codebooks

A further construction of asymptotically optimal codebooks with multiplicative characters

Codebooks with small inner-product correlation are preferred in many practical applications such as direct spread code division multiple access communications, coding theory, compressed sensing and so on. Heng et al. (IEEE Trans Inf Theory 63(10):6179–6187, 2017), Heng (Discrete Appl Math 250:227–240, 2018) and Luo and Cao (IEEE Trans Inf Theory 64(10):6498–6505, 2018) proposed constructions of asymptotically optimal codebooks via character sums related to Jacobi sums. In this paper, we mainly present a further generalization of the work by Heng (2018). The construction in this paper is an extension and generalization of the above works, and deduces a class of asymptotically optimal codebooks. Furthermore, the parameters of the codebooks are flexible and new.

An Optimal Codebook for Content-Based Image Retrieval in JPEG Compressed Domain

Images in JPEG compressed domain have a widespread use and computationally efficient retrieval of those images is of prime concern. In this paper, a content-based image retrieval system in JPEG compressed domain is presented, which generates an optimal codebook and extract features that only require partial decoding of images. The codebook is generated by selecting an optimum number of training images and feature vector length using an optimization cost based on precision and recall. The cost gives the difference in average precision and average recall, which the retrieval system can tolerate, while reducing the codebook size and feature vector length. The efficiency in retrieval performance is achieved by generating an optimal codebook. Experimental results have shown better performance for the proposed retrieval system in terms of precision, recall and number of operations, when compared with state-of-the-art image retrieval systems in JPEG compressed domain. The proposed retrieval system also shows robustness against compressed query images by using different quantization parameters.

Constructions of Codebooks Asymptotically Achieving the Welch Bound With Additive Characters

Codebooks with small inner product correlation have wide applications in many fields, such as direct spread code-division multiple access communications, signal processing, and compressed sensing. It is extremely hard to produce optimal codebooks with respect to the Welch bound or the Levenstein bound. This letter focuses on constructing asymptotically optimal codebooks with additive characters of finite fields. Following the proposed constructions, two classes of asymptotically optimal codebooks with respect to the Welch bound are obtained. In addition, the resulting codebooks could have a small alphabet size.

New Constructions of Asymptotically Optimal Codebooks via Cyclotomic Classes of Order 8

Asymptotically optimal codebooks are a family of codebooks that can approach an optimal codebook meeting the Welch bound when the lengths of codewords are large enough. They can be constructed easily and are a good alternative for optimal codebooks in many applications. In this paper, we construct a new class of asymptotically optimal codebooks by using the product of some special finite fields and almost difference sets, which are composed of cyclotomic classes of order eight.

A Generalized Construction of Asymptotically Optimal Codebooks

A Generalized Construction of Asymptotically Optimal Codebooks

基于4阶分圆类的近似最佳码本的构造

基于4阶分圆类的近似最佳码本的构造