Vector Quantization Codebook Research Articles

Embedding Secret Data in a Vector Quantization Codebook Using a Novel Thresholding Scheme

In recent decades, information security has become increasingly valued, including many aspects of privacy protection, copyright protection, and digital forensics. Therefore, many data hiding schemes have been proposed and applied to various carriers such as text, images, audio, and videos. Vector Quantization (VQ) compression is a well-known method for compressing images. In previous research, most methods related to VQ compressed images have focused on hiding information in index tables, while only a few of the latest studies have explored embedding data in codebooks. We propose a data hiding scheme for VQ codebooks. With our approach, a sender XORs most of the pixel values in a codebook and then applies a threshold to control data embedding. The auxiliary information generated during this process is embedded alongside secret data in the index reordering phase. Upon receiving the stego codebook and the reordered index table, the recipient can extract the data and reconstruct the VQ-compressed image using the reverse process. Experimental results demonstrate that our scheme significantly improves embedding capacity compared to the most recent codebook-based methods. Specifically, we observe an improvement rate of 223.66% in a small codebook of size 64 and an improvement rate of 85.19% in a codebook of size 1024.

On the Initialization of Swarm Intelligence Algorithms for Vector Quantization Codebook Design.

Vector Quantization (VQ) is a technique with a wide range of applications. For example, it can be used for image compression. The codebook design for VQ has great significance in the quality of the quantized signals and can benefit from the use of swarm intelligence. Initialization of the Linde-Buzo-Gray (LBG) algorithm, which is the most popular VQ codebook design algorithm, is a step that directly influences VQ performance, as the convergence speed and codebook quality depend on the initial codebook. A widely used initialization alternative is random initialization, in which the initial set of codevectors is drawn randomly from the training set. Other initialization methods can lead to a better quality of the designed codebooks. The present work evaluates the impacts of initialization strategies on swarm intelligence algorithms for codebook design in terms of the quality of the designed codebooks, assessed by the quality of the reconstructed images, and in terms of the convergence speed, evaluated by the number of iterations. Initialization strategies consist of a combination of codebooks obtained by initialization algorithms from the literature with codebooks composed of vectors randomly selected from the training set. The possibility of combining different initialization techniques provides new perspectives in the search for the quality of the VQ codebooks. Nine initialization strategies are presented, which are compared with random initialization. Initialization strategies are evaluated on the following algorithms for codebook design based on swarm clustering: modified firefly algorithm-Linde-Buzo-Gray (M-FA-LBG), modified particle swarm optimization-Linde-Buzo-Gray (M-PSO-LBG), modified fish school search-Linde-Buzo-Gray (M-FSS-LBG) and their accelerated versions (M-FA-LBGa, M-PSO-LBGa and M-FSS-LBGa) which are obtained by replacing the LBG with the accelerated LBG algorithm. The simulation results point out to the benefits of the proposed initialization strategies. The results show gains up to 4.43 dB in terms of PSNR for image Clock with M-PSO-LBG codebooks of size 512 and codebook design time savings up to 67.05% for image Clock, with M-FF-LBGa codebooks with size N=512, by using initialization strategies in substitution to Random initialization.

A Codebook Compression Method for Vector Quantization Algorithm

This study introduces a novel approach to enhance the compression ratio of the vector quantization (VQ) algorithm by specifically targeting the compression of its codebook. The VQ algorithm typically generates an index matrix and a codebook to represent compressed images. The proposed method focuses on reducing the size of the codebook, which comprises N codewords, each with elements quantized into four levels. Each 8-bit element in a codeword is compressed to 2-bits, and the encoded codeword is accompanied by the minimum value and a threshold value in the codebook. Experimental results on benchmark color images, such as baboon, airplane, Lena, and others, demonstrate a significant reduction of 62.50% in the size of the VQ codebook.

Resizing codebook of vector quantization without retraining

Resizing codebook of vector quantization without retraining

Novel approach to design matched digital filter with Abelian group and fuzzy particle swarm optimization vector quantization

This paper presents a new method for designing matched digital filters with discrete valued coefficients. The fuzzy particle swarm optimization vector quantization (FPSOVQ) has been applied to obtain the optimum codebook in design of matched wavelet function. Abelian group has been used to extract the similarity present in the input voiced signal. Fuzzy particle swarm optimization (FPSO) process is used to find approximate ideal vector quantization (VQ) codebook to be carried out for compression of data. FPSOVQ scheme utilises features of fuzzy inference method (FIM) and expert particle swarm optimization (PSO). The generated codebook consists of set of highly ideal features, which are considered as the filter coefficients. These coefficients are used in the designing of the filter. All of the phonemes in the American English language were included in the 30 sentences that were chosen from the IEEE database. The sentences were originally down-sampled from 25 kHz to 8 kHz. The magnitude responses of each filter have been drawn, which indicates the characteristics of the filter. A comparison has been provided using energy compaction ratio as a parameter to judge the performance of matched designed filter with db4 filter. Experimental results show the advantage of the developed algorithm as the average value of energy compaction ratio for sampled voice signals is 2.8046 times lower for matched designed filter.

Hiding and Extracting Important Information in Encrypted Images by Using the Transformation of All Possible Permutations and VQ Codebook

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.

AoD-Adaptive Channel Feedback for FDD Massive MIMO Systems With Multiple-Antenna Users

In this paper, we propose an efficient feedback scheme for an angle of departure (AoD) based channel estimation in frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems with multiple antennas at the users. The channel feedback scheme is based on zero-forcing block diagonalization (BD) and it is proposed for two distinct design cases; in case I, the number of streams intended for a user equals the number of antennas at that user; in case II, the number of streams is less than the number of receive antennas. Case I is applicable in scenarios where high data rate requirements are needed as it transmits data symbols over all of the available degrees of freedom of the system. Diversely, case II is applicable when reliability is a priority in the system as it uses the additional <i>receive</i> antennas at the user to achieve spatial diversity to enhance the link performance. The proposed scheme is analyzed for the two cases by quantifying the downlink rate gap from the case of perfect channel state information (CSI). Moreover, we design structured feedback codebooks based on optimal subspace packing in the Grassmannian manifold and show that these codes achieve close performance to the perfect CSI case. Additionally, a vector quantization scheme is proposed to quantize the user&#x2019;s channel matrix when optimal power allocation across multiple streams is adopted in the low signal-to-noise ratio (SNR) region. The feedback codebooks are based on optimal line packing in the Grassmannian manifold, where every vector of the user&#x2019;s channel matrix is quantized and sent to the BaseStation. The results demonstrate a fundamental trade-off between vector quantization, with power optimization across the data streams, and subspace quantization. Specifically, vector quantization codebooks outperform subspace-based codebooks in the low SNR region, while the situation is reversed in the high SNR region.

Small Binary Codebook Design for Image Compression Depending on Rotating Blocks

The searching process using a binary codebook of combined Block Truncation Coding (BTC) method and Vector Quantization (VQ), i.e. a full codebook search for each input image vector to find the best matched code word in the codebook, requires a long time. Therefore, in this paper, after designing a small binary codebook, we adopted a new method by rotating each binary code word in this codebook into 900 to 2700 step 900 directions. Then, we systematized each code word depending on its angle to involve four types of binary code books (i.e. Pour when , Flat when , Vertical when, or Zigzag). The proposed scheme was used for decreasing the time of the coding procedure, with very small distortion per block, by designing small binary codebook, then rotating each block in it. Moreover, it can be used for improving the efficiency of the coding process even further with the decrease in the bit rate (i.e. increasing the compression ratio(.

An Efficient Codebook Search Algorithm for Line Spectrum Frequency (LSF) Vector Quantization in Speech Codec

A high-performance vector quantization (VQ) codebook search algorithm is proposed in this paper. VQ is an important data compression technique that has been widely applied to speech, image, and video compression. However, the process of the codebook search demands a high computational load. To solve this issue, a novel algorithm that consists of training and encoding procedures is proposed. In the training procedure, a training speech dataset was used to build the squared-error distortion look-up table for each subspace. In the encoding procedure, firstly, an input vector was quickly assigned to a search subspace. Secondly, the candidate code word group was obtained by employing the triangular inequality elimination (TIE) equation. Finally, a partial distortion elimination technique was employed to reduce the number of multiplications. The proposed method reduced the number of searches and computation load significantly, especially when the input vectors were uncorrelated. The experimental results show that the proposed algorithm provides a computational saving (CS) of up to 85% in the full search algorithm, up to 76% in the TIE algorithm, and up to 63% in the iterative TIE algorithm. Further, the proposed method provides CS and load reduction of up to 29–33% and 67–69%, respectively, over the BSS-ITIE algorithm.

Lossless Image Compression Using Wavelet and Vector Quantization.(Dept.E)

In this paper, we propose a new low bit rate scheme that combines the Discrete Wavelet transform (IWT) and Vector Quantization (VQ) for image compression with high Compression Ratio (CR) in order to achieve acceptable quality of output images, Image compression using the combination of DWT willy VQ has been considered in many recent works; here in our paper the DWT was performed on several gray scale standard images, each of (256X256) byte. We used 4 images as the training images of the train VO using LBG algorithm and got the final optimal codebook. Then we used another two images to compare our output image with JPEG 2000 standard winch based on Wavelet and Scalar quantization. Our proposed system used a VO codebook with Variable cod hook size and dimensions were used to compress wavelet Transform) coefficients resulting using Danny wavelet types. Our pro system is slowed so that the best Wavelet types that you can use for compression is COIF 2. Our proposed system gave better PSNR than G2000 standard at high (CR), above than (5L), and this achieved a VQ codebook size (1024 -512) with dimension equal to 256. In the paper a typical system for the application of image compression system and new scientific results are present.

Metaheuristic-based vector quantization approach: a new paradigm for neural network-based video compression

Video compression has great significance in the communication of motion pictures. Video compression techniques try to remove the different types of redundancy within or between video sequences. In the temporal domain, the video compression techniques remove the redundancies between the highly correlated consequence frames of the video. In the spatial domain, the video compression techniques remove the redundancies between the highly correlated consequence pixels (samples) in the same frame. Evolving neural-networks based video coding research efforts are focused on improving existing video codecs by performing better predictions that are incorporated within the same codec framework or holistic methods of end-to-end video compression schemes. Current neural network-based video compression adapts static codebook to achieve compression that leads to learning inability from new samples. This paper proposes a modified video compression model that adapts the genetic algorithm to build an optimal codebook for adaptive vector quantization that is used as an activation function inside the neural network’s hidden layer. Background subtraction algorithm is employed to extract motion objects within frames to generate the context-based initial codebook. Furthermore, Differential Pulse Code Modulation (DPCM) is utilized for lossless compression of significant wavelet coefficients; whereas low energy coefficients are lossy compressed using Learning Vector Quantization (LVQ) neural networks. Finally, Run Length Encoding (RLE) is engaged to encode the quantized coefficients to achieve a higher compression ratio. Experiments have proven the system’s ability to achieve higher compression ratio with acceptable efficiency measured by PSNR.

Humpback whale song analysis based on automatic classification performance

Song is a prevalent behavior in humpback whale populations, even in regions that are considered to be foraging habitat such as the Monterey Bay National Marine Sanctuary in the Northeast Pacific, where song is detected nine months out of the year. In this work, we explore various machine learning methods to classify song units, as a basis for studying song structure and its changes. We report on a number of analyses and classification exercises based on linear predictive coding, vector quantization, and machine learning classifiers including Naive Bayes, first-order Markov chain, and Hidden Markov modeling. As a baseline for comparison purposes, the distortion measure used to create the codebooks for vector quantization is itself also used as a means for classification. With classification accuracy ranging from 65% to 83% across the selected methods on a 4.5 h recording involving 5000 unit occurrences over 20 different unit types, we evaluate the effect of several signal processing, clustering, and learning parameters on classification performance with the goal of laying a foundation that can be used to characterize song vocalization at not only the unit level but also below (subunit) and above (phrase, theme, song).

On the use of the i-vector speech representation for instrumental quality measurement

The i-vector framework has been widely used to summarize speaker-dependent information present in a speech signal. Considered the state-of-the-art in speaker verification for many years, its potential to estimate speech recording distortion/quality has been overlooked. This paper is an attempt to fill this gap. We conduct a detailed analysis of how distortions are captured in the total variability space. We then propose a full-reference speech quality model based on i-vector similarities and three no-reference approaches. The first no-reference approach makes use of a single reference i-vector based on the average of i-vectors extracted from clean signals. A second approach relies on a vector quantizer codebook of representative clean speech i-vectors. Lastly, i-vectors and subjective ratings were used to train a no-reference deep neural network model for speech quality assessment. Four experiments have shown that the proposed methods, based on the i-vector speech representation, are well-suited for assessing speech quality. Results show correlations with subjective quality judgments similar to those achieved with standardized instrumental algorithms, particularly for degradations caused by noise and reverberation.ϖ

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.

Modified Firefly Algorithm for Vector Quantization Codebook Design in Image Compression

In the recent days, the importance of image compression techniques is exponentially increased due to the generation of massive amount of data which needs to be stored or transmitted. Numerous approaches have been presented for effective image compression by the principle of representing images in its compact form through the avoidance of unnecessary pixels. Vector quantization (VA) is an effective method in image compression and the construction of quantization table is an important process is an important task. The compression performance and the quality of reconstructed data are based on the quantization table, which is actually a matrix of 64 integers. The quantization table selection is a complex combinatorial problem which can be resolved by the evolutionary algorithms (EA). Presently, EA became famous to resolve the real world problems in a reasonable amount of time. This chapter introduces Firefly (FF) with Teaching and learning based optimization (TLBO) algorithm termed as FF-TLBO algorithm for the selection of quantization table and introduces Firefly with Tumbling algorithm termed as FF-Tumbling algorithm for the selection of search space. As the FF algorithm faces a problem when brighter FFs are insignificant, the TLBO algorithm is integrated to it to resolve the problem and Tumbling efficiently train the algorithm to explore all direction in the solution space. This algorithm determines the best fit value for every bock as local best and best fitness value for the entire image is considered as global best. When these values are found by FF algorithm, compression process takes place by efficient image compression algorithm like Run Length Encoding and Huffman coding. The proposed FF-TLBO and FF-Tumbling algorithm is evaluated by comparing its results with existing FF algorithm using a same set of benchmark images in terms of Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Noise Ratio (SNR). The obtained results ensure the superior performance of FF-TLBO and FF-Tumbling algorithm over FF algorithm and make it highly useful for real time applications.

Design and Comparison of Vector Quantization Codebooks for Narrowband Speech Coding

Vector quantization codebook algorithms are used for coding of narrow band speech signals. Multi-stage vector quantization and split vector quantization methods are two important techniques used for coding of narrowband speech signals and these methods are very popular due to the high bit rate minimization during coding of the signals. This paper presents performance measurements of multistage vector quantization and split vector quantization methods. We used line spectral frequencies for coding of the speech signals in codebook tables so as to ensure filter stability after quantization. The codebooks were generated by using the Linde-Buzo-Gray (LBG) algorithm. The tests were performed by selecting large amount of input data in training and test stages and to evaluate noise robustness of the methods, both noisy and clean speech signals were used. As a result, different codebooks were designed and tested in many stages and different bit rates to measure quantization performance. It is measured in terms of spectral distortion evaluation. We obtained the best result in 24bit multistage vector quantization codebook with a spectral distortion less than 1 dB for clean speech training data input. When we compared multistage and split vector quantization codebook spectral distortion results, multistage codebooks gave better performance in each option.

Intelligent Technology for the Monitoring and Protection of Insect Biodiversity

Monitoring and identification are vitally important to insect biodiversity conservation and protection. As a popular and comparatively well known order, Lepidoptera (moths and butterflies) are good indicators of insect biodiversity. Through much research and testing, a reliable image recognition method and online software were developed. The method includes a color histogram and dual tree complex wavelet transform (DTCWT), local mean color feature based method, sparse coding and scale conjugate gradient (SCG) back propagation neural network (BPNN), and Opponent SIFT (scale invariant feature transform). The result showed that Opponent SIFT was the best choice, reaching 100.00% recognition accuracy, while the first three were 91.98%, 91.96% and 99.43%, respectively. Thus an online consulting system was established, based on Opponent SIFT. After the users load the unknown image of the lepidopteran, the system can output the recognition result and all information about the species. New, but otherwise known species can be added to continuously expand the system. A simple and viable scheme to identify insect sounds automatically was also introduced, using a sound parameterization technique that dominates speaker recognition technology. The acoustic signal is preprocessed and segmented into a series of sound samples. MFCC (Mel-frequency cepstrum coefficient) is extracted from the sound sample, and a feature model is trained using Linde-Buzo-Gray algorithm (Linde et al. 1980) to generate vector quantization (VQ) codebook from the above MFCC. The matching for a test sample is completed by finding the nearest neighbour in all the VQ codebooks. The method is tested in a database with acoustic samples of 70 different insect sounds (data from insect sound library, Mankin et al. 2008). The recognition rate was above 96%, and an ideal time performance was also achieved with the recognition time of 9.13s. Sound recognition software system will also be installed.

Vector quantization using the improved differential evolution algorithm for image compression

Vector quantization (VQ) is a popular image compression technique with a simple decoding architecture and high compression ratio. Codebook designing is the most essential part in vector quantization. Linde–Buzo–Gray (LBG) is a traditional method of generation of VQ codebook which results in lower PSNR value. A codebook affects the quality of image compression, so the choice of an appropriate codebook is a must. Several optimization techniques have been proposed for global codebook generation to enhance the quality of image compression. In this paper, a novel algorithm called IDE-LBG is proposed which uses improved differential evolution algorithm coupled with LBG for generating optimum VQ codebooks. The proposed IDE works better than the traditional DE with modifications in the scaling factor and the boundary control mechanism. The IDE generates better solutions by efficient exploration and exploitation of the search space. Then the best optimal solution obtained by the IDE is provided as the initial codebook for the LBG. This approach produces an efficient codebook with less computational time and the consequences include excellent PSNR values and superior quality reconstructed images. It is observed that the proposed IDE-LBG find better VQ Codebooks as compared to IPSO-LBG, BA-LBG and FA-LBG.

Vector quantization codebook design based on Fish School Search algorithm

Vector Quantization (VQ) has been used in image coding systems since it allows high compression rates. Codebook design can be seen as a high dimensional optimization problem and, in this scenario, swarm intelligence techniques have been used. This paper presents a new VQ codebook design algorithm based on swarm clustering. The method, based on Fish School Search (FSS) algorithm, is introduced. The FSS is embedded in Linde–Buzo–Gray (LBG) algorithm as a swarm clustering method, here called FSS-LBG. Also, a modification in the original FSS breeding operation is proposed in order to favor the exploration ability and, therefore, achieve better results in terms of PSNR of the reconstructed images. Simulation results show gains up to 1.57 dB in terms of PSNR when compared to LBG algorithm for the image Lena at 0.5625 bpp using a codebook of size 512.

Hybrid Precoding Architecture for Massive Multiuser MIMO With Dissipation: Sub-Connected or Fully Connected Structures?

In this paper, we study the hybrid precoding structures over limited feedback channels for massive multiuser multiple-input multiple-output (MIMO) systems. We focus on the system performance of hybrid precoding under a more realistic hardware network model, particularly, with inevitable dissipation. The effect of quantized analog and digital precoding is characterized. We investigate the spectral efficiencies of two typical hybrid precoding structures, i.e., the sub-connected structure and the fully-connected structure. It is revealed that increasing signal power can compensate the performance loss incurred by quantized analog precoding. In addition, by capturing the nature of the effective channels for hybrid processing, we employ a channel correlation-based codebook and demonstrate that the codebook shows a great advantage over the conventional random vector quantization (RVQ) codebook. It is also discovered that, if the channel correlation-based codebook is utilized, the sub-connected structure always outperforms the fully-connected structure in either massive MIMO or low signal-to-noise ratio (SNR) scenarios; otherwise, the fully-connected structrue achieves better performance. Simulation results under both Rayleigh fading channels and millimeter wave (mmWave) channels verify the conclusions above.