62,280 research outputs found
Steerable Discrete Cosine Transform
In image compression, classical block-based separable transforms tend to be
inefficient when image blocks contain arbitrarily shaped discontinuities. For
this reason, transforms incorporating directional information are an appealing
alternative. In this paper, we propose a new approach to this problem, namely a
discrete cosine transform (DCT) that can be steered in any chosen direction.
Such transform, called steerable DCT (SDCT), allows to rotate in a flexible way
pairs of basis vectors, and enables precise matching of directionality in each
image block, achieving improved coding efficiency. The optimal rotation angles
for SDCT can be represented as solution of a suitable rate-distortion (RD)
problem. We propose iterative methods to search such solution, and we develop a
fully fledged image encoder to practically compare our techniques with other
competing transforms. Analytical and numerical results prove that SDCT
outperforms both DCT and state-of-the-art directional transforms
Handwritten Arabic character recognition: which feature extraction method?
Recognition of Arabic handwriting characters is a difficult task due to similar appearance of some different characters. However, the selection of the method for feature extraction remains the most important step for achieving high recognition accuracy. The purpose of this paper is to compare the effectiveness of Discrete Cosine Transform and Discrete Wavelet transform to capture discriminative features of Arabic handwritten characters. A new database containing 5600 characters covering all shapes of Arabic handwriting characters has also developed for the purpose of the analysis. The coefficients of both techniques have been used for classification based on a Artificial Neural Network implementation. The results have been analysed and the finding have demonstrated that a Discrete Cosine Transform based feature extraction yields a superior recognition than its counterpart
Type-IV DCT, DST, and MDCT algorithms with reduced numbers of arithmetic operations
We present algorithms for the type-IV discrete cosine transform (DCT-IV) and
discrete sine transform (DST-IV), as well as for the modified discrete cosine
transform (MDCT) and its inverse, that achieve a lower count of real
multiplications and additions than previously published algorithms, without
sacrificing numerical accuracy. Asymptotically, the operation count is reduced
from ~2NlogN to ~(17/9)NlogN for a power-of-two transform size N, and the exact
count is strictly lowered for all N > 4. These results are derived by
considering the DCT to be a special case of a DFT of length 8N, with certain
symmetries, and then pruning redundant operations from a recent improved fast
Fourier transform algorithm (based on a recursive rescaling of the
conjugate-pair split radix algorithm). The improved algorithms for DST-IV and
MDCT follow immediately from the improved count for the DCT-IV.Comment: 11 page
Evolution of the discrete cosine transform using genetic programming
Compression of 2 dimensional data is important for the efficient transmission, storage and manipulation
of Images. The most common technique used for lossy image compression relies on fast application of
the Discrete Cosine Transform (DCT). The cosine transform has been heavily researched and many
efficient methods have been determined and successfully applied in practice; this paper presents a novel
method for evolving a DCT algorithm using genetic programming. We show that it is possible to evolve a
very close approximation to a 4 point transform. In theory, an 8 point transform could also be evolved
using the same technique
Image Compression using Discrete Cosine Transform & Discrete Wavelet Transform
Image Compression addresses the problem of reducing the amount of data required to represent the digital image. Compression is achieved by the removal of one or more of
three basic data redundancies: (1) Coding redundancy, which is present when less than optimal (i.e. the smallest length) code words are used; (2) Interpixel redundancy, which results from correlations between the pixels of an image & (3) psycho visual redundancy which is due to data that is ignored by the human visual system (i.e. visually
nonessential information). Huffman codes contain the smallest possible number of code symbols (e.g., bits) per source symbol (e.g., grey level value) subject to the constraint that the source symbols are coded one at a time. So, Huffman coding when combined with technique of reducing the image redundancies using Discrete Cosine Transform (DCT)
helps in compressing the image data to a very good extent.
The Discrete Cosine Transform (DCT) is an example of transform coding. The current JPEG standard uses the DCT as its basis. The DC relocates the highest energies to the upper left corner of the image. The lesser energy or information is relocated into other areas. The DCT is fast. It can be quickly calculated and is best for images with smooth edges like photos with human subjects. The DCT coefficients are all real numbers unlike the Fourier
Transform. The Inverse Discrete Cosine Transform (IDCT) can be used to retrieve the image from its transform representation. The Discrete wavelet transform (DWT) has gained widespread acceptance in signal processing and image compression. Because of their inherent multi-resolution nature, wavelet-coding schemes are especially suitable for applications where scalability and tolerable degradation are important. Recently the JPEG committee has released its new image coding standard, JPEG-2000, which has been based upon DWT
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