Lossy Image Compression Based on Vector Quantization Using Artificial Bee Colony and Genetic Algorithms

In recent years, the volume of image data that are being employed for Internet and other applications has been increasing at an enormous rate. To cope up with the existing limitations on the storage space and the network bandwidth, it has become necessary to develop more efficient compression techniques. Lossy compression is more popular compared to lossless compression as it is more widely used in a variety of applications. In lossy compression, it is necessary to maintain the quality of the reconstructed image when the compression scheme is applied. Thus, compression ratio and the reconstructed image quality are the two important parameters based on which the performance of a lossy compression scheme is judged. In this paper, a new lossy compression scheme is proposed which employs codebook concept. For the generation of the codebook, a new technique denoted as ABC-GA technique which is a combination of artificial bee colony and genetic algorithms is employed. The performance of the proposed compression scheme is evaluated using two different types of databases, namely, CLEF med 2009 and standard images (Lena, Barbara etc.). The experimental results show that the proposed technique performs better than the existing algorithms yielding average PSNR values of 43.05, 41.58, 40.06, 37.41, 35.24 for compression ratios 10, 20, 40, 60, 80 respectively in the case of standard images.