Selection of wavelet compression or seam carving technique based on image type

Uncompressed multimedia (graphics, audio and video) data require significant storage capacity and transmission bandwidth. Despite rapid development in mass-storage density, processor speeds, and performance of digital communication systems, demand for data storage capacity and data-transmission bandwidth outperform the capabilities of available multimedia technologies. The recent developments of multimedia based applications have contributed not only for efficient ways of encoding signals but also in compression of signals. Therefore, the theory of data compression becomes more and more significant for reducing the data redundancy to save more hardware space and transmission bandwidth. In computer science and more specifically in information theory, data compression is the process of encoding information using fewer bits or other information bearing units rather than an un-encoded representation. Data compression is useful because, it cuts down expensive resources such as hard disk space or transmission bandwidth. Image compression is an application of data compression on digital images, as it reduces the computational time and consequently the cost of image storage and transmission. The fundamental concept about image compression is to remove redundant and unimportant data, and at the same time keeping the compressed image with acceptable quality. An important concept in image compression is to select the proper compression technique based on different categories of images, otherwise the data storage methods or transmission bandwidth will not be utilized in an optimized way. The objective of this research is to find an optimized image compression technique based on different categories of images. These categories are defined as high contrast, highly textured, images with a complex background with multiple objects and images with clear cut background with an object. The optimized method for compression was predicted based on the entropy of the image. The first comp- ession technique employed was based on the wavelet transform, where the testing was carried out using three types of wavelet functions namely: Haar, Morlet and Meyer. The second compression method called seam carving, is an image resizing algorithm where, the image can be downscaled or up scaled by removing the seams or establishing number of seams. The results show the effectiveness of the proposed methodology. MatLab™ was used for the comparisons.