Comparitive Analysis and Findings on Dct & Lbg Compression Techniques

The main objective of this project is to develop an application to find the best compression technique to store Muscat College students' photographs in less storage. MATLAB software will be used to develop a Graphical User Interface GUI application and implement two image compression techniques which are lossless compression using the DCT algorithm and lossy compression using the LBG algorithm. The application shall allow the user to select and test a sample image by applying both these techniques for any student image he\she selects in order to compare the results by display the image after compression and the histogram to find which the most suitable compression technique is. Also, the application shall show the size of images before and after applying the compression process and show the compression ratio and relative data redundancy of compressed image/images. The main functionality is that the application shall allow the user to do bulk processing to apply image enhancement and image compression technique to enhance and compress all the photographs of students and store them in less space.

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.

SAR IMAGE COMPRESSION USING ADAPTIVE DIFFERENTIAL EVOLUTION AND PATTERN SEARCH BASED K-MEANS VECTOR QUANTIZATION

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.

Genetic Simulated Annealing-Based Kernel Vector Quantization Algorithm

Genetic Algorithm (GA) has been successfully applied to codebook design for vector quantization and its candidate solutions are normally turned by LBG algorithm. In this paper, to solve premature phenomenon and falling into local optimum of GA, a new Genetic Simulated Annealing-based Kernel Vector Quantization (GSAKVQ) is proposed from a different point of view. The simulated annealing (SA) method proposed in this paper can approach the optimal solution faster than the other candidate approaches. In the frame of GA, firstly, a new special crossover operator and a mutation operator are designed for the partition-based code scheme, and then a SA operation is introduced to enlarge the exploration of the proposed algorithm, finally, the Kernel function-based fitness is introduced into GA in order to cluster those datasets with complex distribution. The proposed method has been extensively compared with other algorithms on 17 datasets clustering and four image compression problems. The experimental results show that the algorithm can achieve its superiority in terms of clustering correct rate and peak signal-to-noise ratio (PSNR), and the robustness of algorithm is also very good. In addition, we took “Lena” as an example and added Gaussian noise into the original image then adopted the proposed algorithm to compress the image with noise. Compared to the original image with noise, the reconstructed image is more distinct, and with the parameter value increasing, the value of PSNR decreases.