Morphological Transform for Image Compression
نویسندگان
چکیده
منابع مشابه
Morphological Transform for Image Compression
A new method for image compression based on morphological associative memories (MAMs) is presented. We used the MAM to implement a new image transform and applied it at the transformation stage of image coding, thereby replacing such traditional methods as the discrete cosine transform or the discrete wavelet transform. Autoassociative and heteroassociative MAMs can be considered as a subclass ...
متن کاملThe Fourier Transform for Satellite Image Compression
The need to transmit or store satellite images is growing rapidly with the development of modern communications and new imaging systems. The goal of compression is to facilitate the storage and transmission of large images on the ground with high compression ratios and minimum distortion. In this work, we present a new coding scheme for satellite images. At first, the image will be downloaded f...
متن کاملWavelet Based Contourlet Transform for Image Compression
Wavelet transforms are not capable of reconstructing curved images perfectly, hence we go for this new concept, called Contourlet Transform. It is a multiresolution and directional decomposition of a signal using a combination of Laplacian Pyramid (LP) and a Directional Filter Bank (DFB). The Contourlet transform has good approximation properties for smooth 2D functions and finds a direct discr...
متن کاملMorphological operators for image and video compression
This paper deals with the use of some morphological tools for image and video coding. Mathematical morphology can be considered as a shape-oriented approach to signal processing, and some of its features make it very useful for compression. Rather than describing a coding algorithm, the purpose of this paper is to describe some morphological tools that have proved attractive for compression. Fo...
متن کاملOrthonormal Finite Ridgelet Transform for Image Compression
A finite implementation of the ridgelet transform is presented. The transform is invertible, non-redundant and achieved via fast algorithms. Furthermore we show that this transform is orthogonal hence it allows one to use non-linear approximations for the representation of images. Numerical results on different test images are shown. Those results conform with the theory of the ridgelet transfo...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: EURASIP Journal on Advances in Signal Processing
سال: 2008
ISSN: 1687-6180
DOI: 10.1155/2008/426580