Comparison of Bacterial Foraging Optimization (BFO) Neural Network with Haar Wavelet Transform in Image Compression

Abstract

With the growth of multimedia and internet, compression techniques have become the thrust areas in the field of computers. Compression of data in any form is a large and active field as well as a big business. The problem inherent to any digital image is the large amount of bandwidth required for transmission or storage. This has driven the research area of image compression to develop algorithms that compress images to lower data rates with better quality. Popularity of multimedia has led to the integration of various types of computer data. Multimedia combines many data types such as text, graphics, still images, animation, audio and video. A fundamental goal of image compression is to reduce the bit rate for transmission or data storage while maintaining an acceptable fidelity or image quality. Every digital image is specified by the number of pixels associated with the image. Each pixel in an image can be denoted as a coefficient, which represents the intensity of the image at that point. Image compression is a process of representing an image with fewer bits while maintaining image quality, saving cost associated with sending less data over communication lines and finally reducing the probability of transmission errors. Many different image compression techniques currently exist for the compression of different types of images. In this research work, Haar Wavelet Transform (HWT) and Bacterial Foraging Optimization (BFO) algorithm has been used. The Haar wavelet transform (HWT) is one of the simplest and basic transformations from a space domain are local frequency domain and it reduces the calculation work. HT decomposes the linear approximated image as approximation components and detail components. In Bacterial Foraging Optimization (BFO) algorithm, we find the vertical and the horizontal configuration section. Neural network decides a loop for the processing. The loop is called iteration in the scenario. The BFO algorithm proceeds from pixel to pixel. If the pixel width is more than that of the previous pixel width then it is turned to be compressed again by BFO algorithm. This algorithm is well suited in JPEG format files to transmission of images with good quality and lesser storage over networks as compared to Haar Wavelet Algorithm (HWT). The proposed schemes has been demonstrated through several experiments such as Lena, Baboon, Penguins, Koala and Bacterial Foraging Optimization (BFO) algorithm gives very promising results in compression image over Haar Wavelet Transform (HWT) algorithm.