R. Naveen Kumar

Bio: R. Naveen Kumar is an academic researcher from Kuvempu University. The author has contributed to research in topics: Scalar curvature & Image compression. The author has an hindex of 2, co-authored 5 publications receiving 11 citations.

A lossless image compression algorithm using wavelets and fractional Fourier transform

Abstract: The necessity of data transfer at a high speed, in fast-growing information technology, depends on compression algorithms. Maintaining quality of data reconstructed at high compression rate is a very difficult part of the data compression technique. In this paper, a new lossless image compression algorithm is proposed, which uses both wavelet and fractional transforms for image compression. Even though wavelets are the best choice for feature extraction from the source image at different frequency resolutions, the low-frequency sub-bands of wavelet decomposition are the untouched part in compression method in most of the existing methods. On the other hand, fractional Fourier transform is a convenient form of generalized Fourier transform that helps in the compact lossless coding of the source image with optimal fractional orders. Hence, we have used discrete fractional Fourier transform to compress those sensitive sub-bands of the wavelet transform, carefully. In this method, an image is split into low- and high-frequency sub-bands by using Daubechies wavelet filter and level 1 quantization is applied for both low-frequency and high-frequency sub-bands. The low-frequency sub-bands are compressed by using fractional Fourier transform with optimal fractional orders, and at the same time, high-frequency sub-bands are compressed by eliminating zeroes and storing only nonzero blocks and its position. The compressed wavelet coefficients are further compressed by the application of level 2 quantization and stored as a reduced array. This reduced array is encoded by using arithmetic encoder followed by run-length coding. The experimental results of the proposed algorithm with a different set of test images are compared with some of the existing image compression algorithms. The results show that the proposed method has significant improvement in image reconstruction quality.

Image contrast enhancement using DWT-SVD based masking technique

Abstract: A new approach based on masking technique for contrast enhancement of medical image is presented due to the low contrast characteristics. In medical image, preserving mean brightness, average information and noise factor reduction are essential to make input image more appealing visually. The proposed method incorporates spatial and frequency domain techniques to enhance the contrast of the medical image. The mask is formulated effectively between reconstructed approximation coefficients and inverse singular value decomposition (ISVD) to obtain contrast residual. Discrete wavelet transformation (DWT) and singular value decomposition (SVD) have used to decompose the input image. At last maximum contrast enhancement achieved by adding mask with the image obtained through intensity exposure histogram equalization (IEHE). The proposed approach is tested for medical images by comparing its peak signal to noise Ratio (PSNR), absolute mean brightness error (AMBE) and entropy with some existing methods and also measured in terms of visual quality.

Magnetic behavior of ordered DO3-type Fe3Al Heusler alloy

Abstract: Fe3Al alloy is synthesized in a vacuum arc-melting furnace under an argon atmosphere. The prepared ingot is annealed in a vacuum furnace at 800 °C for 1hr, further at 500 °C for 48 hrs to facilitate the re-crystallization. The alloys are crystallized in a cubic DO3 structure. The magnetic properties are derived from M−H, and M−T measurements and the thermal properties like transition temperature (Tc), heat flow, and heat capacity (Cp) are measured from DSC. We have observed the magnetic transition at a temperature of 925°K with an applied magnetic field of 5000 Oe. The saturation magnetization (Ms) is found to be 188 emu/g, and remanence magnetization (Mr) of 0.78 emu/g. From M−T and DSC measurements it appears that the samples have ferromagnetic ordering with second-order phase transition. The critical exponent is derived by applying mean-field, 3D-Heisenberg, 3D-Ising, and tricritical models.

Hybrid Image Compression using Modified Singular Value Decomposition and Adaptive Set Partitioning in Hierarchical Tree

Abstract: Objectives: Image communication in web applications becomes handy because of highly developed compression tools. Human eye fixate on an image’s preview, carefully adjusting the quality and optimization settings until we’ve found that sweet spot, where the file size and quality are both the best they can possibly be. Method: This paper presents a new algorithm, which uses modified singular value decomposition (SVD) and adaptive Set Partition Hierarchical Tree (ASPIHT) for grayscale image compression. This hybrid method uses modified rank one updated SVD as a pre-processing step for ASPIHT to increase the quality of the reconstructed image. Findings: The high energy compaction in SVD process offers high image quality with less compression and requires a number of bits for reconstruction. On the other hand, ASPIHT compression also offers high image quality by coding more significant coefficients adaptively with high compression at specified bitrates. The proposed method is a combination of both SVD and ASPIHT for image compression and is tested with several test images and results are compared with those of SPIHT, ASPIHT without arithmetic coding and JPEG2000. Novelty: This method improves the quality of reconstruction without altering the compression rates of SPIHT algorithm. The tabulated results show significant improvement in PSNR at higher compression ratios as compared to other methods.

Qausi Conformal Curvature Tensor on N(k)-Contact Metric Manifold

Abstract: The purpose of this paper is to studyN(k)-contact metric manifolds en- dowed with a qausi-conformal curvature tensor. Here we consider quasi-conformally at, Einstein semi-symmetric quasi-conformally at, quasi-conformally semi-sym- metric, and globally -quasiconformally symmetric N(k)-contact metric manifolds.

The fractional Fourier transform as a biomedical signal and image processing tool: A review

Abstract: This work presents a literature review of the fractional Fourier transform (FrFT) investigations and applications in the biomedical field. The FrFT is a time-frequency analysis tool that has been used for signal and image processing due to its capability in capturing the non-stationary characteristics of real signals. Most biomedical signals are an example of such non-stationarity. Thus, the FrFT-based solutions can be formulated, aiming to enhance the health technology. As the literature review indicates, common applications of the FrFT involves signal detection, filtering and features extraction. Establishing adequate solutions for these tasks requires a proper fractional order estimation and implementing the suitable numeric approach for the discrete FrFT calculation. Since most of the reports barely describe the methodology on this regard, it is important that future works include detailed information about the implementation criteria of the FrFT. Although the applications in biomedical sciences are not yet among the most frequent FrFT fields of action, the growing interest of the scientific community in the FrFT, supports its practical usefulness for developing new biomedical tools. © 2020 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences

An improved lossless image compression algorithm based on Huffman coding

Abstract: There is an increasing number of image data produced in our life nowadays, which creates a big challenge to store and transmit them. For some fields requiring high fidelity, the lossless image compression becomes significant, because it can reduce the size of image data without quality loss. To solve the difficulty in improving the lossless image compression ratio, we propose an improved lossless image compression algorithm that theoretically provides an approximately quadruple compression combining the linear prediction, integer wavelet transform (IWT) with output coefficients processing and Huffman coding. A new hybrid transform exploiting a new prediction template and a coefficient processing of IWT is the main contribution of this algorithm. The experimental results on three different image sets show that the proposed algorithm outperforms state-of-the-art algorithms. The compression ratios are improved by at least 6.22% up to 72.36%. Our algorithm is more suitable to compress images with complex texture and higher resolution at an acceptable compression speed.

Gaussian Hermite polynomial based lossless medical image compression

Abstract: The role of compression is inevitable in the storage and transmission of medical images. The polynomial based image compression is proposed in this work for the compression of abdomen CT medical images. The input images are preprocessed by min–max normalization; the pixels are scanned and subjected to polynomial approximation. The polynomial approximated coefficients are subjected to llyods quantization and encoded by arithmetic coder. The medical image compression using Gaussian Hermite polynomial gives superior results when compared with the legendre polynomial based image compression and JPEG lossless compression techniques in terms of Peak to signal noise ratio (PSNR), Mean square Error (MSE) and other picture quality metrics. The algorithms are tested on real-time DICOM abdomen CT image and can be used for data transfer in teleradiology application.

ON GENERALIZED φ-RECURRENT AND GENERALIZED CONCIRCULARLY φ-RECURRENT N(κ)-PARACONTACT METRIC MANIFOLDS

Abstract: The purpose of the present paper is to study generalized φ-recurrent, generalized concirculary φ-recurrent N(κ)-paracontact metric manifolds and generalized φ-recurrent paracontact metric manifolds of constant curvature.

Possibilities of a Hybrid Method for a Time-Scale-Frequency Analysis in the Aspect of Identifying Surface Topography Irregularities

Abstract: The article presents research results related to assessing the possibilities of applying modern filtration methods to diagnosing measurement signals. The Fourier transformation does not always provide full information about the signal. It is, therefore, appropriate to complement the methodology with a modern multiscale method: the wavelet transformation. A hybrid combination of two algorithms results in revealing additional signal components, which are invisible in the spectrum in the case of using only the harmonic analysis. The tests performed using both simulated signals and the measured roundness profiles of rollers in rolling bearings proved the advantages of using a complex approach. A combination of the Fourier and wavelet transformations resulted in the possibility to identify the components of the signal, which directly translates into better diagnostics. The tests fill a research gap in terms of complex diagnostics and assessment of profiles, which is very important from the standpoint of the precision industry.