No abstract

To effectively utilize the storage capacity, digital image compression has been applied to numerous science and engineering problems. There are two fundamental image compression techniques, either lossless or lossy. The former employs probabilistic models for lossless storage on a basis of statistical redundancy occurred in digital images. However, it has limitation in the compression ratio and bit per pixel ratio. Hence, the latter has also been widely implemented to further improve the storage capacities, covering various fundamental digital image processing approaches. It has been well documented that most lossy compression schemes will provide perfect visual perception under an exceptional compression ratio, among which discrete wavelet transform, discrete Fourier transform and some statistical optimization compression schemes (e.g., principal component analysis and independent component analysis) are the dominant approaches. It is necessary to evaluate these compression and reconstruction schemes objectively in addition to the visual appealing. Using a well defined set of the quantitative metrics from Information Theory, a comparative study on several typical digital image compression and reconstruction schemes will be conducted in this research.

Feature detection is a fundamental technique in broad fields of image processing, pattern recognition and computer vision. A digital image in general contains objects, edges, noises and background. Critical changes in properties of objects can be captured via detecting sharp variations in image brightness. The edges can be detected via numerous approaches on a basis of image intensity changes. Edge broken and false detection are typical problems using classical methods, which will result in information loss and feature deformity. The notion of optimization is thus introduced into edge detection. The Canny edge detector and Ant Colony Optimization (ACO) detector are among the most successful and effective approaches for edge detection. The Canny edge detector is designed to capture edges by searching local optima of the gradient of the intensity. It is susceptible to noises presenting on the raw images, so details of images could be slightly changed when Gaussian smoothing is applied. To improve accuracy, the adaptive edge tracing scheme is proposed. On the other hand, artificial intelligence has also been introduced. Being one of metaheuristic optimization approaches, the evolutionary computing oriented ACO becomes a promising approach for feature capturing without necessity of smoothing filters. Selection of maximum intensity difference as the path visibility function for ACO will contribute better to generate true edges and avoid false edges. Both the adaptive Canny edge detection and enhanced ACO are proposed in this article. Comparative studies are also conducted to evaluate the edge detection qualities. The outcomes are analyzed and evaluated from both qualitative and quantitative points of view, where merits and drawbacks of the two schemes have been indicated.