Comparative Performance Analysis of Optimization Techniques on Vector Quantization for Image Compression

For constructing the best local codebook for image compression, there are many Vector Quantization (VQ) procedures, but the simplest VQ procedure is the Linde–Buzo–Gray (LBG) procedure. Techniques such as the Gaussian Dissemination Function (GDF) are used for the searching process in generating a global codebook for particle swarm optimization (PSO), Honeybee mating optimization (HBMO), and Firefly (FA) procedures. However, when particle velocity is very high, FA encounters a problem when brighter fireflies are trivial, and PSO suffers uncertainty in merging. A novel procedure, Cuckoo Search–Kekre Fast Codebook Generation (CS-KFCG), is proposed that enhances Cuckoo Search–Linde–Buzo–Gray (CS-LBG) codebook by implementing a Flight Dissemination Function (FDF), which produces more speed than other states of the art algorithms with appropriate mutation expectations for the overall codebook. Also, CS-KFGC has generated a high Peak Signal Noise Ratio (PSNR) in terms of high duration (time) and better acceptability rate.

Parameter estimation is an important issue in nonlinear science, which can be formulated as a multi-dimensional problem. Numbers of nature-inspired meta-heuristic algorithms have been applied for parameter estimation of chaotic systems; however, many of them are not able to achieve an appropriate trade-off between exploration and exploitation. Therefore, this paper proposes an effective hybrid cuckoo search (HCS) algorithm to obtain higher quality solutions and convergence speed. Inspired by the powerful efficiency of differential evolution, the proposed HCS provides two novel mutation strategies to fully exploit the neighborhood among the current population. Furthermore, a crossover operator under self-adaptive parameters control is introduced to balance the exploration and exploitation ability of the proposed two mutation strategies. Besides, the opposition-based learning is incorporated into HCS for initializing population and producing new candidate solutions during the evolutionary process. HCS is further employed to estimate the unknown parameters and time delays of chaotic systems. Numerical simulations and comparisons with some other optimization methods are conducted on three chaotic systems with and without time delays to demonstrate the performance of HCS. The experimental results show a superiority of HCS in parameter estimation of chaotic systems, and can be regarded as a promising method in terms of its high calculation accuracy, fast convergence speed, and strong robustness.

Future remote systems should have the option to handle the gain in a different system state. One of the most of the time tried issues for coordination is the vertical handoff, which is an option to handoff portable hubs between different systems. While customary handoff codes are based on quality checks, vertical handoffs must assess additional factors, for example, funding costs, proposed governance, systematic location, and customer learning. In this paper, some improvements are proposed for the implementation of vertical handoff option calculations aimed at increasing the nature of the administration experienced by each customer. Firstly, talk about the idea of alignment-based handoff is done. At that point, cost efficiency can be achieved by sending decisions on the target systems depending on the classification of the customer and the measurements provided by the system. Finally, a demonstration investigation suggests that the proposed reforms can achieve significant gains like governance and the productive use of assets. In the present work, two other half-schemes are proposed using the cuckoo search calculation and the genealogical calculation. In the two mixing plots, the calculation consists of two steps: the primary stage, which examines the location of the cuckoo search (CS), or the genetic algorithm (GA). In the subsequent stage, to improve worldwide pursuit and dispose of catching into a few neighbourhood optima is done. Reproducibility results and testing with old-time CS and GA calculations confirm the feasibility of the proposed computation in designing various benchmark correction capabilities.

In this paper, a novel hybrid Invasive Weed Optimization (IWO) and Cuckoo Search (CS) algorithm (IWO/CS) is presented for phase-only pattern synthesis of large array antenna. The hybrid IWO/CS algorithm embeds the Levy flight of CS algorithm into IWO algorithm as a global guide, thereby enhancing the global search ability of the algorithm. At the same time, in order to avoid trapping into the local optimal solution, mutation operator is introduced to enhance search ability of the algorithm. Finally, competitive exclusion operation is improved to maintain the diversity of population. The hybrid algorithm is applied to phase-only beam shaping to achieve main lobe broadening and side lobe depression. Simulation results demonstrate the superiority of the hybrid IWO and CS algorithm.

Fast vector quantization using a Bat algorithm for image compression

Cuckoo search algorithm (CSA) is one of behavior algorithm which is effective to solve optimization problem including the clustering problem. Based on investigation, k-means is also effective to solve the clustering problem specially in fast convergence. This paper combines two algorithms, cuckoo search algorithm and k-means algorithm in clustering problem called FCSA. Cuckoo search is used to build the robust initialization, while K-means is used to accelerate by building the solutions. The result confirms that FCSA's computational time in ten datasets is faster than the compared algorithm.

Cuckoo search (CS) is an efficient bio-inspired algorithm and has been studied on global optimisation problems extensively. It is expert in solving complicated functions but converges slowly. Another optimisation algorithm, covariance matrix adaption evolution strategy (CMA_ES) can speed up the convergence rate via the self-adaptative mutation distribution and the cumulative evolution path, whereas it performs badly in complex functions. Therefore, in this paper, we devise a hybridisation of CS and CMA_ES and name it CS_CMA, to improve performance for the different optimisation problems. An evolved population is initialised at the beginning of iteration, using the information of previous evolution. Self-adaptive parameter adjustments are employed through the successful parameter values. To validate the performance of CS_CMA, comparative experiments are conducted based on seven high-dimensional benchmark functions provided for CEC 2008 and an engineering optimisation problem chosen from CEC' 2011, and computational results demonstrate that CS_CMA outperforms other competitor algorithms.

A new finite-state vector quantization scheme called jigsaw-puzzle vector quantization (JPVQ) is proposed to provide better image quality, especially in the low bit rate context. For low bit rate image coding with conventional finite-state vector quantization (FSVQ) techniques, image quality degrades due to error propagation from one state to the next. The proposed JPVQ algorithm exploits the four-step side-match prediction technique to optimize the spatial continuity of each encoded block to improve the coding performance and reduce the error propagation effect. In the proposed coding scheme, an input block can be encoded by the jigsaw-puzzle block, the dynamic codebook, or the super-codebook. It is demonstrated with experimental results that JPVQ performs significantly better than traditional FSVQ techniques.

Conditional-entropy-constrained trellis-searched vector quantization for image compression

Vector quantization (VQ) applies effectively to very low bit-rate image transmission. The high compression ratios are often balanced by limited reconstruction quality due to blockiness effects due to coarse quantization. Interpolating different codevectors can overcome such drawback by enhancing the generalization ability and the adaptiveness of the coding system at run time. Experimental results on real testbeds show the method's advantages as compared with related techniques.

This paper proposes a Hybrid Cuckoo search algorithm to solve Multi-area economic dispatch problem (MAED). Hybrid Cuckoo search algorithm is a combination of the Cuckoo search algorithm and Teaching-learning-based optimization, where the learner phase of TLBO is added to improve performance of Cuckoo eggs. The proposed method has been applied for solving three tested cases of Multi-area economic dispatch problem. The objective of this problem is to minimize a total generation cost while satisfying generator operational constraints and tie- line constraints. The proposed method has been compared with the conventional Cuckoo search algorithm and Teaching-learning-based optimization to obtain its effectiveness. Numerical results show that the proposed method gives better solutions than two compared methods with high performance.

The cuckoo search (CS) algorithm, which is a new meta-heuristic optimization algorithm, is an efficient approach to solve continuous function optimization problems. Normally, the parameters of the CS are fixed constants. This may result in affecting the algorithm efficiency. To cope with this issue, we properly tune the parameters of the CS and propose an adaptive cuckoo search (ACS) algorithm to enhance the convergence rate and accuracy of the CS. Then, we validate the proposed ACS against test functions and compare its performance with those of particle swarm optimization (PSO), improved cuckoo search algorithm (ICS) and CS by simulation. The simulation results demonstrate that the ACS obtains some solutions better than those obtained by PSO, ICS and CS.

In recent times, fiber optic communication networks have become commonly applied for commercial as well as military applications. Fiber optic networks have gained popularity owing to the high data rate. At the same time, the generation of huge quantity of data at a faster rate poses a major challenge in the storing and transmission process. To resolve this issue, data compression approaches have been presented to reduce the quantity of transmitted data and thereby minimizes bandwidth utilization and memory. Vector quantization (VQ) is a commonly employed image compression technique and Linde Buzo Gray (LBG) is used to construct an optimum codebook to compress images. With this motivation, this paper presents a new oppositional glowworm swarm optimization based LBG (OGSO-LBG) technique for image compression in fiber optic communication. The OGSO algorithm involves the integration of oppositional based learning (OBL) concept into the GSO algorithm to boost its convergence rate. The OGSO-LBG algorithm produces the codebook at a faster rate with minimal computation complexity. In order to highlight the enhanced compression performance of the OGSO-LBG technique, a series of experiments were carried out and the results are examined under different dimensions.

The generalized Lloyd algorithm (GLA), better known as the Linde-Buzo-Gray (LBG) algorithm, is the most widely used technique in classical vector quantization (VQ) for speech or image signal compression. However, the encoding complexity of the algorithm grows exponentially with the product of coding rate and vector dimension, which prohibits applying the technique to tasks with moderate to large encoding rates or vector dimensions. The authors present a new VQ scheme which overcomes the successive search coding computation of traditional techniques by using a quasi-parallel mapping technique and makes VQ practical for higher encoding rates and/or vector dimensions. Neural computing techniques are used to implement parallel encoding and decoding mappings in VQ and the developed algorithm was applied to quantizing Gauss-Markov processes and artificial data sources. Comparisons of performance with the LBG algorithm are given. >

Purpose The role of image compression is inevitable in telemedicine for the transfer of medical images. The diagnostic quality of the reconstructed image plays a vital role, and hence, the choice of compression algorithm is necessary. The medical images are analysed by the physicians for the diagnosis of anomalies. Procedures Linde Buzo Gray (LBG) is a classical algorithm for vector quantization; it produces a local optimum codebook that results in low compression efficiency. The efficiency of the vector quantization (VQ) relies on the appropriate codebook; hence, many research works based on optimization have developed for the generation of global codebook. This paper couples LBG with BAT optimization algorithm which generates an appropriate codebook. The optimization algorithm is employed not only for codebook design but also for the codebook size selection. Results The proposed BAT-LBG with dynamic codebook selection was compared with the classical LBG-VQ, JPEG lossless and BAT-LBG with static codebook selection. The algorithms were tested on abdomen CT images of five datasets, and performance evaluation was done by performance metrics like peak signal-to-noise ratio (PSNR) and mean square error (MSE). Conclusions The BAT-LBG compression with dynamic codebook size selection was found to produce efficient results when compared with JPEG lossless, classical LBG-VQ and BAT-LBG with static codebook selection. The quality of the reconstructed image was found to be good for BAT-LBG with dynamic codebook selection, and hence, it is efficient for transfer of medical images in telemedicine.