Faculty Publications

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Subject: search.filters.subject.Discrete cosine transforms

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    Improved Wigner-Ville distribution performance based on DCT/DFT harmonic wavelet transform and modified magnitude group delay
    (2008) Narasimhan, S.V.; Haripriya, A.R.; Shreyamsha Kumar, B.K.
    A new Wigner-Ville distribution (WVD) estimation is proposed. This improved and efficient WVD is based on signal decomposition (SD) by DCT or DFT harmonic wavelet transform (DCTHWT or DFTHWT) and the modified magnitude group delay (MMGD). The MMGD processing can be either in fullband or subband. The SD by DCTHWT provides better quality low leakage decimated subband components. The concatenation of WVDs of the subbands results in an overall WVD, significantly free from crossterms and Gibbs ripple. As no smoothing window is used for the instantaneous autocorrelation (IACR), MMGD removes or reduces the Gibbs ripple preserving the frequency resolution achieved by the DCT/DFT HWT. The SD by DCTHWT compared to that of DFTHWT, has improved frequency resolution and detectability. These are due to the symmetrical data extension and the consequential low leakage (bias and variance). As the zeros due to the associated white noise are removed by the MMGD effectively in subband domain than in fullband, the proposed WVD based on subband has a better noise immunity. Compared to fullband WVD, the subband WVD is computationally efficient and achieves a significantly better: frequency resolution, detectability of low-level signal in the presence of high-level one and variance. The SD-based methods, however cannot bring out the frequency transition path from band to band clearly, as there will be gap in the contour plot at the transition. For the proposed methods, the heart rate variability (HRV) real data is also considered as an example. © 2007 Elsevier B.V. All rights reserved.
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    Discrete cosine harmonic wavelet transform and its application to signal compression and subband spectral estimation using modified group delay
    (2009) Narasimhan, S.V.; Harish, M.; Haripriya, A.R.; Basumallick, N.
    This paper proposes a new harmonic wavelet transform (HWT) based on discrete cosine transform (DCTHWT) and its application for signal or image compression and subband spectral estimation using modified group delay (MGD). Further, the existing DFTHWT has also been explored for image compression. The DCTHWT provides better quality decomposed decimated signals, which enable improved compression and MGD processing. For signal/image compression, compared to the HWT based on DFT (DFTHWT), the DCTHWT reduces the reconstruction error. Compared to DFTHWT for the speech signal considered for a compression factor of 0.62, the DCTWHT provides a 30% reduction in reconstruction error. For an image, the DCTHWT algorithm due to its real nature, is computationally simple and more accurate than the DFTHWT. Further compared to Cohen-Daubechies-Feauveau 9/7 biorthogonal symmetric wavelet, the DCTHWT, with its computational advantage, gives a better or comparable performance. For an image with 6.25% coefficients, the reconstructed image by DFTHWT is significantly inferior in appearance to that by DCTHWT which is reflected in the error index as its values are 3.0 and 2.65%, respectively. For spectral estimation, DCTHWT reduces the bias both in frequency (frequency resolution) and spectral magnitude. The reduction in magnitude bias in turn improves the signal detectability. In DCTHWT, the improvement in frequency resolution and the signal detectability is not only due to good quality DCT subband signals but also due to their stretching (decimation) in the wavelet transform. The MGD reduces the variance while preserving the frequency resolution achieved by DCT and decimation. In view of these, the new spectral estimator facilitates a significant improvement both in magnitude and frequency bias, variance and signal detection ability; compared to those of MGD processing of both DFT and DCT fullband and DFT subband signals. © Springer-Verlag London Limited 2008.
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    Implementation of comprehensive address generator for digital signal processor
    (2013) Ramesh Kini, R.M.; Sumam David, S.
    The performance of signal-processing algorithms implemented in hardware depends on the efficiency of datapath, memory speed and address computation. Pattern of data access in signal-processing applications is complex and it is desirable to execute the innermost loop of a kernel in a single-clock cycle. This necessitates the generation of typically three addresses per clock: two addresses for data sample/coefficient and one for the storage of processed data. Most of the Reconfigurable Processors, designed for multimedia, focus on mapping the multimedia applications written in a high-level language directly on to the reconfigurable fabric, implying the use of same datapath resources for kernel processing and address generation. This results in inconsistent and non-optimal use of finite datapath resources. Presence of a set of dedicated, efficient Address Generator Units (AGUs) helps in better utilisation of the datapath elements by using them only for kernel operations; and will certainly enhance the performance. This article focuses on the design and application-specific integrated circuit implementation of address generators for complex addressing modes required by multimedia signal-processing kernels. A novel algorithm and hardware for AGU is developed for accessing data and coefficients in a bit-reversed order for fast Fourier transform kernel spanning over log 2 N stages, AGUs for zig-zag-ordered data access for entropy coding after Discrete Cosine Transform (DCT), convolution kernels with stored/streaming data, accessing data for motion estimation using the block-matching technique and other conventional addressing modes. When mapped to hardware, they scale linearly in gate complexity with increase in the size. © 2013 Copyright Taylor and Francis Group, LLC.
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    2D-spectral estimation based on DCT and modified magnitude group delay
    (2013) Sandeep, P.; Shreyamsha Kumar, B.K.; Narasimhan, S.V.
    This paper proposes two new 2D-spectral estimation methods. The 2D-modified magnitude group delay (MMGD) is applied to 2D-discrete Fourier transform (2D-DFT) for the first and to the analytic 2D-discrete Cosine transform for the second. The analytic 2D-DCT preserves the desirable properties of the DCT (like, improved frequency resolution, leakage and detectability) and is realized by a 2D-discrete cosine transform (2D-DCT) and its Hilbert transform. The 2D-MMGD is an extension from 1D to 2D, and it reduces the variance preserving the original frequency resolution of 2D-DFT or 2D-analytic DCT, depending upon to which is applied. The first and the second methods are referred to as DFT-MMGD and DCT-MMGD, respectively. The proposed methods are applied to 2D sinusoids and 2D AR process, associated with Gaussian white noise. The performance of the DCT-MMGD is found to be superior to that of DFT-MMGD in terms of variance, frequency resolution and detectability. The performance of DFT-MMGD and DCT-MMGD is better than that of 2D-LP method even when the signal to noise ratio is low. © 2012 Springer-Verlag London Limited.
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    Nonlocal linear minimum mean square error methods for denoising MRI
    (Elsevier Ltd, 2015) Sudeep, P.V.; Ponnusamy, P.; Kesavadas, C.; Rajan, J.
    The presence of noise results in quality deterioration of magnetic resonance (MR) images and thus limits the visual inspection and influence the quantitative measurements from the data. In this work, an efficient two stage linear minimum mean square error (LMMSE) method is proposed for the enhancement of magnitude MR images in which data in the presence of noise follows a Rician distribution. The conventional Rician LMMSE estimator determines a closed-form analytical solution to the aforementioned inverse problem. Even-though computationally efficient, this approach fails to take advantage of data redundancy in the 3D MR data and hence leads to a suboptimal filtering performance. Motivated by this observation, we put forward the concept of nonlocal implementation with LMMSE estimation method. To select appropriate samples for the nonlocal version of the LMMSE estimation, the similarity weights are computed using Euclidean distance between either the gray level values in the spatial domain or the coefficients in the transformed domain. Assuming that the signal dependent component of the noise is optimally suppressed by this filtering and the rest is a white and uncorrelated noise with the image, we adopt a second stage LMMSE filtering in the principal component analysis (PCA) domain to further enhance the image and the noise variance is adaptively adjusted. Experiments on both simulated and real data show that the proposed filters have excellent filtering performance over other state-of-the-art methods. © 2015 Elsevier Ltd. All rights reserved.
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    Magnetic resonance image denoising using nonlocal maximum likelihood paradigm in DCT-framework
    (John Wiley and Sons Inc, 2015) Kumar, P.K.; Darshan, P.; Kumar, S.; Ravindra, R.; Rajan, J.; Saba, L.; Suri, J.S.
    The data acquired by magnetic resonance (MR) imaging system are inherently degraded by noise that has its origin in the thermal Brownian motion of electrons. Denoising can enhance the quality (by improving the SNR) of the acquired MR image, which is important for both visual analysis and other post processing operations. Recent works on maximum likelihood (ML) based denoising shows that ML methods are very effective in denoising MR images and has an edge over the other state-of-the-art methods for MRI denoising. Among the ML based approaches, the Nonlocal maximum likelihood (NLML) method is commonly used. In the conventional NLML method, the samples for the ML estimation of the unknown true pixel are chosen in a nonlocal fashion based on the intensity similarity of the pixel neighborhoods. Euclidean distance is generally used to measure this similarity. It has been recently shown that computing similarity measure is more robust in discrete cosine transform (DCT) subspace, compared with Euclidean image subspace. Motivated by this observation, we integrated DCT into NLML to produce an improved MRI filtration process. Other than improving the SNR, the time complexity of the conventional NLML can also be significantly reduced through the proposed approach. On synthetic MR brain image, an average improvement of 5% in PSNR and 86%reduction in execution time is achieved with a search window size of 91 × 91 after incorporating the improvements in the existing NLML method. On an experimental kiwi fruit image an improvement of 10% in PSNR is achieved. We did experiments on both simulated and real data sets to validate and to demonstrate the effectiveness of the proposed method. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 256-264, 2015 © 2015 Wiley Periodicals, Inc.
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    Randomised visual secret sharing scheme for grey-scale and colour images
    (Institution of Engineering and Technology journals@theiet.org, 2018) Mhala, N.C.; Jamal, R.; Pais, A.R.
    Randomised visual secret sharing is an encryption technique that utilises block-based progressive visual secret sharing and discrete cosine transform (DCT) based reversible data embedding technique to recover a secret image. The recovery method is based on progressive visual secret sharing, which recovers the secret image block by block. The existing block based schemes achieve the highest contrast level of 50% for noise-like and meaningful shares. The proposed scheme achieves a contrast level of 70-90% for noise-like and 70-80% for meaningful shares. The enhancement of contrast is achieved by embedding additional information in the shares using DCT-based reversible data embedding technique. Experimental results showed that the proposed scheme restores the secret image with better visual quality in terms of human visual system based parameters. © The Institution of Engineering and Technology 2017.