Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
7 results
Search Results
Item Texture Classification based Efficient Image Compression Algorithm for Wireless Capsule Endoscopy(Institute of Electrical and Electronics Engineers Inc., 2019) Sushma, B.; Aparna., P.This paper presents a novel method for classification of blocks into smooth and edge blocks in transform domain and a compression scheme for Wireless Capsule Endoscopy (WCE) with block classifier. WCE involves capturing, transmission and processing of gastrointestinal images. Power consumption is a critical issue in WCE, as it uses a button battery driven capsule endoscope to capture and transmit images. The captured image needs to be compressed to save the transmission power and low complexity compressor should be used to avoid more power consumption from the compressor itself. JPEG based compression techniques which consists Discrete Cosine Transform(DCT), quantizer and entropy encoder provides the best compression performance with less complexity compared to other various techniques. Pixel distribution in smooth blocks is uniform and energy is compacted only into low frequency bands in spectral domain. Because high frequency bands are almost having zero energy, only low frequency bands are quantized and entropy coded which saves power in processing high bands. Most of the endoscopic image has smooth region, this method is more suitable to WCE. Proposed algorithm improves compression rate by 9% without sacrificing quality compared to JPEG based compression algorithm. © 2019 IEEE.Item 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.Item 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.Item Perceptually lossless coder for volumetric medical image data(Academic Press Inc. apjcs@harcourt.com, 2017) Chandrika, B.K.; Aparna., P.; Sumam David, S.S.With the development of modern imaging techniques, every medical examination would result in a huge volume of image data. Analysis, storage and/or transmission of these data demands high compression without any loss of diagnostically significant data. Although, various 3-D compression techniques have been proposed, they have not been able to meet the current requirements. This paper proposes a novel method to compress 3-D medical images based on human vision model to remove visually insignificant information. The block matching algorithm applied to exploit the anatomical symmetry remove the spatial redundancies. The results obtained are compared with those of lossless compression techniques. The results show better compression without any degradation in visual quality. The rate-distortion performance of the proposed coders is compared with that of the state-of-the-art lossy coders. The subjective evaluation performed by the medical experts confirms that the visual quality of the reconstructed image is excellent. © 2017Item Gradient-oriented directional predictor for HEVC planar and angular intra prediction modes to enhance lossless compression(Elsevier GmbH journals@elsevier.com, 2018) Shilpa Kamath, S.; Aparna., P.; Antony, A.Recent advancements in the capture and display technologies motivated the ITU-T Video Coding Experts Group and ISO/IEC Moving Picture Experts Group to jointly develop the High-Efficiency Video Coding (HEVC), a state-of-the-art video coding standard for efficient compression. The compression applications that essentially require lossless compression scenarios include medical imaging, video analytics, video surveillance, video streaming etc., where the content reconstruction should be flawless. In the proposed work, we present a gradient-oriented directional prediction (GDP) strategy at the pixel level to enhance the compression efficiency of the conventional block-based planar and angular intra prediction in the HEVC lossless mode. The detailed experimental analysis demonstrates that the proposed method outperforms the lossless mode of HEVC anchor in terms of bit-rate savings by 8.29%, 1.65%, 1.94% and 2.21% for Main-AI, LD, LDP and RA configurations respectively, without impairing the computational complexity. The experimental results also illustrates that the proposed predictor performs superior to the existing state-of-the-art techniques in the literature. © 2018 Elsevier GmbHItem Deep chroma prediction of Wyner–Ziv frames in distributed video coding of wireless capsule endoscopy video(Academic Press Inc., 2022) Sushma, B.; Aparna., P.Compression of captured video frames is crucial for saving the power in wireless capsule endoscopy (WCE). A low complexity encoder is desired to limit the power consumption required for compressing the WCE video. Distributed video coding (DVC) technique is best suitable for designing a low complexity encoder. In this technique, frames captured in RGB colour space are converted into YCbCr colour space. Both Y and CbCr representing luma and chroma components of the Wyner–Ziv (WZ) frames are processed and encoded in existing DVC techniques proposed for WCE video compression. In the WCE video, consecutive frames exhibit more similarity in texture and colour properties. The proposed work uses these properties to present a method for processing and encoding only the luma component of a WZ frame. The chroma components of the WZ frame are predicted by an encoder–decoder based deep chroma prediction model at the decoder by matching luma and texture information of the keyframe and WZ frame. The proposed method reduces the computations required for encoding and transmitting of WZ chroma component. The results show that the proposed DVC with a deep chroma prediction model performs better when compared to motion JPEG and existing DVC systems for WCE at the reduced encoder complexity. © 2022 Elsevier Inc.Item An Approach for Diagnostically Lossless Coding of Volumetric Medical Data Based on Wavelet and Just-Noticeable-Distortion Model(Taylor and Francis Ltd., 2023) Chandrika, B.K.; Aparna., P.; Sumam David, S.This paper explores a technique for visually/diagnostically lossless coding in the wavelet domain to effectively compress the three-dimensional medical image data. The quantisation module based on Just Noticeable Distortion (JND) for wavelets guarantees the visual quality in the reconstructed data. This method has been further extended to present the Volume of Interest (VOI) based technique that enables to preserve the quality of the diagnostically significant VOI region. The proposed method tested on several datasets outperforms the state-of-the-art methods. Apart from the conventional quality metric, Human Visual System (HVS) based quality metrics are also used to evaluate the visual quality of the reconstructed image. A subjective and objective evaluation carried out for VOI based coder shows that the quality-compression needs of the medical community are well addressed. © 2023 IETE.