Quantitative analysis of information loss in digital image compression and reconstruction

Digital image compression has numerous practical applications to effectively utilize storage capacities. Two basic techniques are lossless and lossy compression. The former employs probabilistic models for lossless storage on a basis of statistical redundancy occurred in digital images. However, it has limitation in compression ratio. Thus, the latter has been widely applied to further improve storage capacities, covering various fundamental digital image processing approaches. It has been documented that most lossy compression schemes will provide perfect visual perception under an exceptional compression ratio, among which discrete wavelet transform (DWT), discrete Fourier transform (DFT) and statistical optimization compression schemes (e.g., principal component analysis or independent component analysis) are dominant approaches. It is necessary to evaluate these compression and reconstruction schemes objectively. Using a well defined set of quantitative metrics by means of Information Theory, a comparative study on several typical image compression and reconstruction schemes will be made in this research.