A New Efficient Methods for Contour Extraction and Compression
Ali Abdrhman M. Ukasha · 2012
This paper presents a two algorithms of contour extraction from grey level image. The first proposed algorithm is applied in spectral domain using single-level wavelet transform (WT). Single step parallel contour extraction (SSPCE) method is used for the binary image after inverse wavelet transform is applied to the details images. Then the contours are compressed using either ramer, or triangle methods in spatial domain. The second proposed algorithm is applied in spectral domain using discrete cosine transform (DCT). The algorithm of contour extraction and image compression using low-pass filter (LPF) and high-pass filter (HPF) is presented and compared with the traditional zonal sampling algorithm of low-pass and high-pass filters in this paper. Effectiveness of the contour extraction and compression for test image is evaluated. In the paper the main idea of the analyzed procedures for both contour extraction and image compression are performed. To compare the results, the mean square error, signal-to- noise ratio criterions, and compression ratio (or bit per pixel) were used. The simplicity to obtain compressed image and extracted contours with accepted level of the reconstruction is the main advantage of the proposed algorithms. & (5). In this paper the discrete wavelet transform and discrete cosine transform will be used for two proposed algorithms respectively. In the first proposed algorithm, the compressed image and binary image are obtained using inverse wavelet transform to the approximation coefficients image and details coefficients images respectively. The contours are extracted from binary image using single step parallel contour extraction (SSPCE) method (2). Flowchart of the algorithm for image compression and contour extraction is depicted in Fig. 1. In the second proposed algorithm, by using low and high-pass filters after the zonal procedure the two spectral sub-images are obtained. The threshold is done for the obtained image by HPF to get the extracted contour. The compressed image and extracted contour are obtained by using the inverse transform for each of the two sub-images respectively. These two sub-images are combined together to reconstruct the original grey-level image. Flowchart of the algorithm for image compression and contour extraction is depicted in Fig. 2.