Encoding of still pictures by a wavelet transform and singular value decomposition
This paper proposes a new scheme for picture compression which decomposes a picture to (3/spl times/3+1) subband pictures using the 2-D discrete wavelet transform three times, and then decomposes a picture block matrix A(16/spl times/16 pixels) of the lowest frequency subband picture to singular values and eigenvectors of AA/sup T/ and A/sup T/A and encode them by PCM. For all other subband pictures, first we encode the 0-level wavelet coefficient run-length by a modified Huffman code and then encode the non-0-level coefficients by PCM with non-linear quantizer.
SVD-wavelet algorithm for image compression
We explore the use of the singular value decomposition (SVD) in image compression. We link the SVD and the multiresolution algorithms. In [22] it is derived a multiresolution representation of the SVD decomposition, and in [15] the SVD algorithm and Wavelets are linked, proposing a mixed algorithm which roughly consist on applying firstly a discrete Wavelet transform and secondly the SVD algorithm to each subband. We propose a new algorithm, which is carried out in two main steps. Firstly we decompose the data matrix corresponding to the image following a singular value decomposition. Secondly we apply a Harten's multiresolution decomposition to the singular vectors which are considered significant. We study the compression capabilities of this new algorithm. We also propose a variant of the implementation, where the multiresolution transformation is carried out by blocks. We apply on each block, depending on a selection process, either the algorithm presented or the 2D multiresolution algorithm based on biorthogonal wavelets.
Comparison Of Transform Image Coding Techniques For Compression Of Tactical Imagery
The vast quantities and high generation rates of tactical imagery require very efficient data compression in order to conserve precious bandwidth for transmission and to limit the required storage volume for archiving. This paper describes the results of ef-ficiency and image quality comparisons for several transform image coding techniques. Specifically, the research effort was focused on developing the Singular Value Decomposition (SVD) as an approach to image compression. Detailed comparisons were made to the two-dimensional cosine transform, Hadamard transform and Karhunen-Loeve techniques.