A novel Vector Quantization (VQ) technique for encoding the Bi-orthogonal wavelet decomposed image using hybrid Adaptive Differential Evolution (ADE) and a Pattern Search optimization algorithm (hADEPS) is proposed. ADE is a modified version of Differential Evolution (DE) in which mutation operation is made adaptive based on the ascending/descending objective function or fitness value and tested on twelve numerical benchmark functions and the results are compared and proved better than Genetic Algorithm (GA), ordinary DE and FA. ADE is a global optimizer which explore the global search space and PS is local optimizer which exploit a local search space, so ADE is hybridized with PS. In the proposed VQ, in a codebook of codewords, 62.5% of codewords are assigned and optimized for the approximation coefficients and the remaining 37.5% are equally assigned to horizontal, vertical and diagonal coefficients. The superiority of proposed hybrid Adaptive Differential Evolution and Pattern Search (hADE-PS) optimized vector quantization over DE is demonstrated. The proposed technique is compared with DE based VQ and ADE based quantization and with standard LBG algorithm. Results show higher Peak Signal-to-Noise Ratio (PSNR) and Structural Similiraty Index Measure (SSIM) indicating better reconstruction.
Most common vector quantization (VQ) is Linde Buzo Gray (LBG), that designs a local optimal codebook for image compression. Recently firefly algorithm (FA), particle swarm optimization (PSO) and Honey bee mating optimization (HBMO) were designed which generate near global codebook, but search process follows Gaussian distribution function. FA experiences a problem when brighter fireflies are insignificant and PSO undergoes instability in convergence when particle velocity is very high. So, we proposed Cuckoo search (CS) metaheuristic optimization algorithm, that optimizes the LBG codebook by levy flight distribution function which follows the Mantegna’s algorithm instead of Gaussian distribution. Cuckoo search consumes 25% of convergence time for local and 75% of convergence time for global codebook, so it guarantees the global codebook with appropriate mutation probability and this behavior is the major merit of CS. Practically we observed that cuckoo search algorithm has high peak signal to noise ratio (PSNR) and better fitness value compared to LBG, PSO-LBG, Quantum PSO-LBG, HBMO-LBG and FA-LBG at the cost of high convergence time. Keywords: Cuckoo search (CS), Firefly algorithm (FA), Particle swarm optimization (PSO), Linde-Buzo-Gray (LBG), Vector quantization, Image compression
Image compression is very significant process in image transmission at high data rate over a communication channel and to increase the storage capacity of storage device. Ordinary image thresholding is a class of clustering technique used for image compression because of its simplicity, robustness and accuracy but it is computationally expensive when extending for multilevel image thresholding. An attempt is made in this paper to reduce the computational time of multilevel image thresholding using hybrid gravitational search algorithm and pattern search (hGSA-PS) by optimising a criterion such as Shannon entropy or Fuzzy entropy for seeking appropriate threshold values. From literature, gravitational search algorithm (GSA) is designed to explore the global search space (exploitation), and pattern search (PS) is designed to exploit a local search space (exploration), so we hybridise the GSA and PS to achieve exploitation and exploration of search space by incorporating strengths and weakness of both, and results are compared with differential evolution, particle swarm optimisation and bat algorithm and proved better in standard deviation, peak signal-to-noise ratio (PSNR), weighted PSNR and reconstructed image quality. The performance of the proposed algorithm is found better with fuzzy entropy compared to Shannon entropy.