A comparative study of image compression between Singular value decomposition, Block truncating coding, Discrete cosine transform and Wavelet

With the growth of multimedia and internet, compression techniques have become the thrust area in the fields of computers. Popularity of multimedia has led to the integration of various types of computer data. Multimedia combines many data types like text, graphics, still images, animation, audio and video. Image compression is a process of efficiently coding digital image to reduce the number of bits required in representing image. Its purpose is to reduce the storage space and transmission cost while maintaining good quality. Many different image compression techniques currently exist for the compression of different types of images. Image compression is fundamental to the efficient and cost-effective use of digital imaging technology and applications. In this study Image compression was applied to compress and decompress image at various compression ratios. This was then compared with the formal compression standard “Discrete Cosine Transform” DCT, “Singular Value Decomposition” SVD, “Block Truncation Coding” BTC, Wavelet. Histogram analysis, Bar Comparison, Graph comparison was used as a set of criteria to determine the ‘acceptability’ of image compression. Wavelet methods have been shown to have no significant differences in diagnostic accuracy for compression ratios of up to 30:1. Visual comparison was also made between the original image and compressed image to ascertain if there is any significant image degradation. The image compression techniques are categorized into two main classifications namely lossy compression techniques and Lossless compression techniques [1]. Lossless compression ratio gives good quality of compressed images, but yields only less compression whereas the lossy compression techniques [2] lead to loss of data with higher compression ratio.