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Authors
Affiliations
1 Computer Science & Engineering, Samrat Ashok Technological Institute, Vidisha-464001 (M.P.), IN
2 Computer Science & Engineering, Laxminarayana College of Engineering, Bhopal (M.P.), IN
Source
Digital Image Processing, Vol 2, No 5 (2010), Pagination: 147-156
Abstract
To overcome the limitations of the bandwidth and storage, the images must be effectively compressed for efficient utilization of available resources such as storage and bandwidth of communication media. The objective of this paper is to provide the performance analysis and comparison of image compression using Discrete Cosine Transform (DCT) and Wavelet Transform (WT). The performance analysis and comparison is carried out on equal footing. The choice of transform used depends on a number of factors, in particular, computational complexity and coding gain. In present scenario, the most effective and popular way to achieve efficient compression of images are based on either Discrete Cosine Transform (DCT) or Wavelet Transform (WT). The paper discusses important features of both the discrete cosine transform (DCT) and the wavelet transform (WT) in compression of still images. DCT represent an image as a superposition of cosine functions with different discrete frequencies i.e. the basis of Discrete Cosine Transform (DCT) is cosine functions, while the basis of Wavelet Transform (WT) is wavelet function that satisfies requirement of multi-resolution analysis. The influences of image contents of variety of images at different compression ratios are assessed. The test images selected for experiment are of different frequency content, size and resolution. Two quality measures are used: Peak Signal to Noise Ratio (PSNR) and visual quality of image. In this paper, we have analyzed visual quality of image at a compression ratio of 50:1 using both DCT and WT (at decomposition level 5) for image compression on the variety of test images. Our analysis reveals that for images, the wavelet transform outperforms the DCT in both peak signal-to-noise-ratios as well as in visual quality of image.
Keywords
Discrete Cosine Transform, Wavelet Transform, PSNR, Image Compression, Compression Ratio, Image Quality.
