Digital Image Processing

K. Indira Devi ¹, R. Shanmugalakshmi ²

Affiliations
1 Electrical and Electronics Engineering Department, Sri Shakthi Institute of Engineering and Technology, Coimbatore, IN
2 Computer Science and Engineering, Government College of Technology, Coimbatore, IN

Abstract

Recently compression techniques plays a vital role in the field of computer due to the development of multimedia and internet. Fame of multimedia has led to the integration of several types of computer data. Multimedia includes various data types like text, graphics, still images, animation, audio and video. Image compression is a method of effectively coding digital image to minimize the number of bits required in representing image. The main goal of the image compression techniques is to minimize the storage space and transmission cost without affecting the quality of the image. Various image compression approaches are currently available in the literature for the compression of various types of images. But still research is being done for the development of new algorithms for image compression based on several approaches. In this paper, various image compression techniques present in the literature has been analyzed.

Keywords

Image Compression, Fractal Image Compression, Discrete Wavelet Transform.

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M. Thamarai ¹, R. Shanmugalakshmi ²

Affiliations
1 Department of ECE in Karpagam College of Engineering, Coimbatore, IN
2 Department of CSE in GCT, Coimbatore, IN

Abstract

Particle Swarm Optimization (PSO) is global optimization technique based on swarm intelligence. It simulates the behavior of bird flocking. It is widely accepted and focused by researchers due to its profound intelligence and simple algorithm structure. Currently PSO has been implemented in a wide range of research areas such as functional optimization, pattern recognition, neural network training and fuzzy system control etc., and obtained significant success. Particle Swarm Optimization (PSO) recently has been successfully applied to perform block-based motion estimation. In this paper, a modified version of PSO called Directed PSO (DPSO) is proposed for video coding. In this modified version, we have proposed a new parameter called Velocity Rate (VR) for changing the position of the particles, and the particles are replaced insensitive with the direction also. The experimental results show that DPSO reduces the computational complexity and increases the performance of motion estimation while comparing with other exiting algorithms.

Keywords

Block Matching, Video Coding, Particle Swarm Optimization.

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