Partitioning In Hierarchical Trees Research Articles

VLSI design for adjustable compression rate in lossless/lossy compression of EEG signal

In the realm of remote health monitoring, the compression of electroencephalography (EEG) signals at the edge holds significant importance. This paper presents a VLSI structure capable of adjustable compression rates to achieve both lossless and lossy compression of EEG data, catering to environments requiring flexible adjustment of compression types and rates. The architecture integrates two-dimensional discrete wavelet transform (2D-DWT) and the Set Partitioning in Hierarchical Trees (SPIHT). Notably, the Haar wavelet lifting structure is employed in 2D-DWT, and the SPIHT scanning sequence is optimized for enhanced resource efficiency. The proposed algorithm is validated using the CHB-MIT Scalp EEG Database, demonstrating compression ratios of ≥1.95 for normal EEG and ≥1.69 for epileptic EEG. Through synthesis and layout design in the 130 nm CMOS technology, the resulting circuit achieves a maximum clock frequency of 57 MHz and occupies an area of 81 kμm2, with an average power consumption of less than 0.71nj per data for 16-bit data. These findings affirm the efficacy of the proposed VLSI structure for achieving efficient lossless/lossy compression of edge EEG data.

Advancements in Lossless and Reversible Compression of Digital Pathology Images via Auto-Recursive Set Partitioning in Hierarchical Trees and Wavelet Decomposition

Advancements in Lossless and Reversible Compression of Digital Pathology Images via Auto-Recursive Set Partitioning in Hierarchical Trees and Wavelet Decomposition

An Enhanced Compression Method for Medical Images Using SPIHT Encoder for Fog Computing

When it comes to filtering and compressing data before sending it to a cloud server, fog computing is a rummage sale. Fog computing enables an alternate method to reduce the complexity of medical image processing and steadily improve its dependability. Medical images are produced by imaging processing modalities using X-rays, computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, and ultrasound (US). These medical images are large and have a huge amount of storage. This problem is being solved by making use of compression. In this area, lots of work is done. However, before adding more techniques to Fog, getting a high compression ratio (CR) in a shorter time is required, therefore consuming less network traffic. Le Gall5/3 integer wavelet transform (IWT) and a set partitioning in hierarchical trees (SPIHT) encoder were used in this study’s implementation of an image compression technique. MRI is used in the experiments. The suggested technique uses a modified CR and less compression time (CT) to compress the medical image. The proposed approach results in an average CR of 84.8895%. A 40.92% peak signal-to-noise ratio (PSNR) PNSR value is present. Using the Huffman coding, the proposed approach reduces the CT by 36.7434 s compared to the IWT. Regarding CR, the suggested technique outperforms IWTs with Huffman coding by 12%. The current approach has a 72.36% CR. The suggested work’s shortcoming is that the high CR caused a decline in the quality of the medical images. PSNR values can be raised, and more effort can be made to compress colored medical images and 3-dimensional medical images.

An Empirical Selection of Wavelet for Near-Lossless Medical Image Compression.

Wavelets are defined as mathematical functions that segment the data into different frequency levels. We can easily capture the fine and coarse details of an image or signal referred to as a subband. And it also helps in subband thresholding to achieve good compression performance. In recent days in telemedicine services, the handling of medical images is prominently increasing and it leads to the demand for medical image compression. While compressing the medical images, we have to concentrate on the data that holds important information, and at the same time, it must retain the image quality. Near-Lossless compression plays an essential role to achieve a better compression ratio than lossy compression and provides better quality than lossless compression. In this paper, we analyzed the sub-banding of Discrete Wavelet Transform (DWT) using different types of wavelets and made an optimal selection of wavelets for subband thresholding to attain a good compression performance with an application to medical images. We used Set Partitioning In Hierarchical Trees (SPIHT) compression scheme to test the compression performance of different wavelets. The Peak Signal to Noise Ratio (PSNR), Bits Per Pixel (BPP), Compression Ratio, and percentage of number of zeros are used as metrics to assess the performance of all the selected wavelets. And to find out its efficiency in possessing the essential information of medical images, the subband of the selected wavelets is further utilized to devise the near-lossless compression scheme for medical images.

A Visually Meaningful Image Encryption Scheme Based on Lossless Compression SPIHT Coding

With the popularity of social networks and the increase of cloud platform applications, service computing has also developed. Therefore, the protection of information even privacy uploaded to the cloud server has become critical. Recently, some researchers have proposed encryption schemes of visual meaningful image by using compressive sensing. However, these schemes generally cannot hide the large-size of plain image into the small-size of cover image and cannot recover the original plain image lossless. To solve above problems, this paper proposed a visually meaningful image encryption scheme based on lossless compression set partitioning in hierarchical trees (SPIHT) coding. The sender encrypts the plain image into the cipher image through the proposed encryption scheme, and then uploads the cipher image to the cloud server which is assumed as semi-honest trusted. Authorized receiver can completely decrypt the plain image after downloading the cipher image. In addition, even if the cipher image is attacked by attacker in the cloud server, the final decrypted image is still readable. Experimental results show that the proposed scheme is not only completely reversible and can hide the large-size of plain image into the small-size of cover image, but also superior to other schemes in visual quality and anti-attack performance.

Performance analysis of wireless transmission of compressed images using DCT‐OFDMA system with different compression schemes

AbstractTransmitting images over an Orthogonal Frequency Division Multiplexing Access (OFDMA) system poses a significant challenge. The process entails sending a substantial amount of data, which consumes a significant amount of bandwidth. Consequently, compressing the transmitted image becomes essential to reduce the required bandwidth. The aim of this paper is to examine and analyse the wireless transmission of a compressed image via Discrete Cosine Transform (DCT‐OFDMA). A comparison is made with Discrete Fourier Transform (DFT‐OFDMA), across various subcarrier mapping schemes (localized and inter‐leaved), and different modulation schemes (16 Quadrature Amplitude Modulation (16QAM) and Quadrature Phase Shift Keying (QPSK)) using vehicular A, Stanford University Interim (SUI3), and uniform channel models. To evaluate the performance of the system, the minimum Signal‐to‐Noise Ratio (SNR) necessary to recover the transmitted compressed image is calculated. This work considers nine standard compression techniques. The results are carried out using the MATLAB simulator. According to the simulation results, the minimum SNR required to recover the transmitted compressed image was found to be 19 dB. This result was achieved when using Discrete Cosine Transform‐Loaclized‐Orthogonal Frequency Division Multiplexing Access (DCT‐LOFDMA) with QPSK modulation and set partitioning in hierarchical trees (SPIHT) compression method over the SUI3 channel model. Moreover, it was observed that the DCT‐LOFDMA and DFT‐IOFDMA systems attained equal SNR while utilizing the SPIHT_3D compression technique and QPSK modulation on the SUI3 channel model. Overall, the results suggest that the performance of DCT‐based localized OFDMA is somewhat superior to DFT‐based localized OFDMA, particularly when utilizing the SUI3 channel model and the QPSK modulation scheme. Therefore, it is feasible to transmit and receive a compressed image effectively over an OFDMA system with DCT.

Perancangan Aplikasi Kompresi File Video Menggunakan Algritma SPIHT (Set Partioning In Hirarcical Tress)

Video files have a large enough capacity, and to access them takes a long time, so a data compression technique is needed. In this case the video file will be compressed using the SPIHT (Set Partitioning In Hierarchical Trees) algorithm, SPIHT (Set Partitioning In Hierarchical Trees) is a video file compression capable of achieving a high compression ratio. Problems that arise due to the large file size include the wasteful storage media used, as well as more time in terms of data exchange, data transfer over the internet is a big problem in Indonesia because of the very low download and upload speeds. To overcome this problem, data compression techniques are needed. The concept of compression was born as a real solution to problems regarding the space needed to store various digital files, including the MKV (Matroska Video) format video files. The compression technique is a technique that compresses files so that they become smaller in size while maintaining the quality of the representation of the file. Through the compression process of MKV (Matroska video) format video files using the SPIHT (Set Partioning In Hirarcical Tress) algorithm, it is hoped that in the future this compression system will have a major effect on video file storage and can increase storage and it is also hoped that this algorithm can be used for other objects to be compressed.

A novel method of data compression using ROI for biomedical 2D images

In the era of modern medical imaging communal, huge volume of medical image data are being acquired and need to be transmitted and archived which necessitate the development of efficient image compression techniques on both 2D and 3D images. The compression of medical images is an essential process to support remote healthcare services. The medical diagnostics through these services require more accurate information from an image. As the property of inverse proportionality between the compression rate and quality of the image takes place in any kind of compression method, there is a need to sacrifice any one of those credentials (Quality or Compression Rate). With this context, Region of Interest (ROI) codecs are emerging and reduces this proportionality that yields more compression rate without compromising the quality. In this paper, presents an ROI based near lossless image compression method that incorporates the Set Partitioning in Hierarchical Trees (SPIHT) and Vector Quantization coding for medical images.

Improving Medical Video Coding Using Multi Scale Quincunx Lattice: From Low Bitrate to High Quality

In recent years, the healthcare sector has seen an increase in the use of medical images and videos. However, storage and transmission of this huge volume of data remain a challenging task, requiring the use of compression techniques. In this paper, the authors propose an algorithm to improve the visual quality of compressed medical video for lower bitrate without modifying the content of information such as edges and textures, this is a unique way for doctors to store and share medical data over the internet. The algorithm has not yet been sufficiently explored in medical video coding. In this study, the performances of the quincunx wavelet transform (QWT) combined with the set partitioning in hierarchical trees (SPIHT) encoder are discussed. The QWTs were chosen due to their limited number of wavelets family and reduced dilatation factor. The high efficiency of the suggested algorithm is checked against the coding standard based on the discrete cosines transform (DCT) or discrete wavelet transform (DWT). The assessment of the quality of the decoded video is based on the use of the peak signal to noise ratio (PSNR), the mean structural similarity (MSSIM) and the visual information fidelity (VIF). The results prove that the QWT+SPIHT provide competing performance where the PSNR reached 33 dB value for lower bitrate (137.408 Kbps) against previous standards.

Grayscale and color images encryption in a DRPE assisted by SPHIT, RLE, QR code and two chaos logistic maps

A non-linear encryption approach for compressed grayscale and color images is proposed in Double Random Phase Encoding (DRPE) setup using two compression algorithms: Set Partitioning in Hierarchical Trees (SPIHT) and Run-length encoding (RLE). The compressed image is then segmented and embedded in multiple Quick Response (QR) codes, which are combined again into a single image. Further, this single image is scrambled by two-coupled chaos logistic maps, at which the scrambled image is now ready for DRPE encryption. The use of two-coupled chaos logistic increases the security level of this proposed DRPE encryption from various attacks. The proposed DRPE encryption is extended to handed color images, which are segregated into three color-channels (Red, Green and Blue), where each channel is individually encrypted. For grayscale images, the proposed system will have three security key codes while for color images it has nine security key codes. The robustness and invulnerability of the proposed scheme against various attacks is demonstrated through cryptoanalysis and computer simulation results.

An efficient and Highly Scalable Listless SPIHT Image Compression Framework

The set partitioning in hierarchical trees is a powerful image compression algorithm. It has reasonable complexity and produces a rate scalable bit-stream. Unfortunately, SPIHT fails to explore the multi-resolution nature of the wavelet transform as the output bit-stream doesn't support resolution scalability. Moreover it requires huge memory and has complex memory management as it depends on utilizing lists with memory of about 2.5 the image size. This paper proposes three related algorithms. The first one modifies SPIHT to reduce its complexity and improve its efficiency especially at low rates. The second is the main contribution of the paper. It provides a simultaneous solution to the memory and scalability problems of SPIHT. Memory is reduced by utilizing status bits of average 2.5 bits per pixel instead of the lists. Resolution scalability is maintained by encoding the resolution levels in increasing order within each coding pass. Another important attribute of our algorithm is that it has very little increment in complexity in comparison with the original SPIHT algorithm. In contrast, the existing solutions have much more complexity, and/or more memory resources. The third has slightly lower complexity and memory than the second but at the same time, it has slightly lower performance.

A METAHEURISTIC APPROACH FOR TETROLET BASED MEDICAL IMAGE COMPRESSION

Over recent times, medical imaging plays a significant role in clinical practices. Storing and transferring the huge volume of images becomes complicated without an efficient image compression technique. This paper proposes a compression algorithm that uses a Haar based wavelet transform called Tetrolet transform, which reduces the noise on the input images and decomposes with a 4 x 4 blocks of equal squares called tetrominoes. It opts for a decomposing using optimal scheme for achieving the input image into a sparse representation which gives a much-detailed performance for texture and edge information better than wavelet transform. Set Partitioning in Hierarchical Trees (SPIHT) is used for encoding the significant coefficients to achieve efficient image compression. It has been investigated with various metaheuristic algorithms. Experimental results prove that the proposed method outperforms the other transform-based compression in terms of PSNR, CR, and Complexity. Also, the proposed method shows an improved result with another state of work.

Adaptive two-step method for providing the desired visual quality for SPIHT

Lossy compression has been widely used in various applications due to its variable compression ratio. However, distortions are introduced unavoidably, and this decreases the image quality. Therefore, it is often required to control the quality of the compressed images. A two-step method has been proposed recently to provide the desired visual quality. The average rate-distortion curve was used to determine the proper parameter value that controls compression. However, its performance for the wavelet-based coder Set Partitioning in Hierarchical Trees (SPIHT) is insufficient because there are very wide limits of visual quality variation for different images for a given value of the compression control parameter (CCP). Additionally, previous work has demonstrated that the level of errors, which is the subject of our study relates to texture features of an image to be compressed, where texture presence is an inherent property of remote sensing images. In this paper, our goal is to develop an adaptive two-step method for SPIHT to improve accuracy. The following tasks were solved. First, a prediction of visual quality for a particular parameter value is conducted. The prediction scheme is based on the information extraction from a certain number of image blocks to perform a visual quality calculation of the image compressed for a given CCP value. A threshold is adopted as the complexity grouping; in this paper, images are divided into two groups: simple and complex images. Second, the results of the grouping determine the adaptive curve model adopted. Finally, a two-step compression method is applied according to this curve. The classical metric Peak signal-to-noise ratio (PSNR) is employed to evaluate the image quality. The research method is based on a validation experiment that is conducted for an image set covering different image complexity and texture features. The comparison results of four typical desired values prove that the accuracy has been generally improved, the variances of both the first and second steps have been reduced sufficiently, and the mean absolute error has also been improved. Conclusion: the improvement effects are significant, particularly in the low desired visual quality. A remote sensing image is taken as an example to analyze in detail; the quality of the decompressed images meets the user’s visual requirement, and the errors are acceptable.

Blind Dual Watermarking Scheme Using Stucki Kernel and SPIHT for Image Self-Recovery

In this paper we propose a blind dual watermarking scheme using Set Partitioning in Hierarchical Trees (SPIHT) and Stucki Kernel halftone technique for the tamper detection and image self-recovery. The watermark consists of authentication bits for tampering area location and recovery bits for image restoration. We generate two recovery bits to ensure the high-quality recovery of the tampered image. The primary recovery bit is generated by the SPIHT encoding, and the secondary recovery bit is generated by the Stucki Kernel halftone technique. Then the authentication bit is generated based on the recovery bits. Before embedding the watermark, we shuffle the watermark bits through Arnold cat mapping and diagonal mapping to improve the security and quality of the restored image. LSB-based watermarking technique is used to embed the watermark into the original image to ensure the invisibility of the watermarked image. Experiments have been conducted on two datasets, BOW2 and USC-SIPI, and results show that the proposed scheme can achieve high restoration quality. Comparison with the existing works demonstrate the good performance and superiority of the proposed scheme.

Research on Lossless Compression Coding Algorithm of N-Band Parametric Spectral Integer Reversible Transformation Combined With the Lifting Scheme for Hyperspectral Images

After considering the spectral correlation of structure and statistics of the hyperspectral image, the redundancy reduction is optimized using reversible transformation of integer matrices in conjunction with the N-band integer reversible transformation of spectral matrices. This paper proposes a new quantization algorithm, XCJRCT, that uses invertible matrix transformation to remove spectral redundancy, optimize bit allocation in structure, the lifting scheme of the discrete wavelet transform (DWT) is used in conjunction with other algorithms to reduce redundancy and set partitioning in hierarchical trees (SPIHT) coding. The experimental results show that lossless compression is significantly better than JPEG-LS, WinZip, ARJ, and DPCM. Using the Jet Propulsion Laboratory (JPL) Canal test image (Band Sequential) as an example data set, the average compression ratio increases by about 73.691598%, 67.713276%, 65.175242%, and 59.107580%, respectively, compared to the above algorithms.

An effective image compression technique based on burrows wheeler transform with set partitioning in hierarchical trees

AbstractIn this article, the efficient image compression which consists of Burrows–Wheeler transform (BWT) with set partitioning in hierarchical trees (SPIHT). The main phases of the proposed system are: partitioning, compression of non‐ROI areas, Fusion and compression of ROI areas. To enhance the propose of the proposed methodology, the morphological functions are updated by dividing two types of images with the consideration of dilation and erosion control. After that, the convolution and correlation in the deformation provide good accuracy of the segmentation at the fastest speed. In this proposed methodology, SPIHT encryption is a lossy compression technique aimed at understanding the non‐ROI area, while the BWT is a lossless compression technique performed to achieve a summary of the ROI area. Finally, separating these two parts of the image merges the image and reconstructs it to the desired quality. The test sends a variety of images and analyzes the performance of the proposed system using compression ratio and PSNR measurements.

Compressed domain image retargeting in hierarchical wavelet trees

This paper presents an efficient algorithm with content aware spatial scalability for effective displaying on various screens of different sizes. It combines the set partitioning in hierarchical trees (SPIHT) algorithm and the multiscale seam carving (MSC) algorithm in the compressed domain. As seams of insignificant wavelet trees can be discarded as early as possible, the burden of unnecessary computation and memory space is alleviated. Experimental results show that the proposed algorithm is preferable to other existing pixel-domain algorithms.

Compression of MRI brain images based on automatic extraction of tumor region

In the compression of medical images, region of interest (ROI) based techniques seem to be promising, as they can result in high compression ratios while maintaining the quality of region of diagnostic importance, the ROI, when image is reconstructed. In this article, we propose a set-up for compression of brain magnetic resonance imaging (MRI) images based on automatic extraction of tumor. Our approach is to first separate the tumor, the ROI in our case, from brain image, using support vector machine (SVM) classification and region extraction step. Then, tumor region (ROI) is compressed using Arithmetic coding, a lossless compression technique. The non-tumorous region, non-region of interest (NROI), is compressed using a lossy compression technique formed by a combination of discrete wavelet transform (DWT), set partitioning in hierarchical trees (SPIHT) and arithmetic coding (AC). The classification performance parameters, like, dice coefficient, sensitivity, positive predictive value and accuracy are tabulated. In the case of compression, we report, performance parameters like mean square error and peak signal to noise ratio for a given set of bits per pixel (bpp) values. We found that the compression scheme considered in our setup gives promising results as compared to other schemes.

Ultra low bitrate retinal image compression using integer lifting scheme and subband encoder

Recently, ophthalmic clinics have seen many complaints related to retinal diseases. The degree of clarity of the blood vessels (BV) in the eye can be an important indicator of some diseases affecting the retina such as diabetic retinopathy. To diagnose it, we need to intervene more than a medical team, especially in some difficult cases, through the exchange of medical images obtained by photography. This method has contributed significantly to the production of large data that can quickly saturate transmission, storage systems and increase processing time, so the need to compress images efficiently without modifying the content before transmission represents a major challenge. This paper provides an effective method for compressing color retinal images (CRI), which relies on the use of an integer lifting scheme (ILS) based on cohen daubechies-feaveau wavelet (CDFW9/7) and the set partitioning in hierarchical trees (SPIHT) to encode large coefficients. The obtained results demonstrate that the suggested method reduce algorithmic complexity, improve the retinal image quality and achieves high objective parameters values for ultra-low bitrate compared to the conventional methods.

A Metaheuristic Approach for Tetrolet-Based Medical Image Compression

Over recent times, medical imaging plays a significant role in clinical practices. Storing and transferring the huge volume of images becomes complicated without an efficient image compression technique. This paper proposes a compression algorithm that uses a Haar based wavelet transform called Tetrolet transform, which reduces the noise on the input images and decomposes with a 4 x 4 blocks of equal squares called tetrominoes. It opts for a decomposing using optimal scheme for achieving the input image into a sparse representation which gives a much-detailed performance for texture and edge information better than wavelet transform. Set Partitioning in Hierarchical Trees (SPIHT) is used for encoding the significant coefficients to achieve efficient image compression. It has been investigated with various metaheuristic algorithms. Experimental results prove that the proposed method outperforms the other transform-based compression in terms of PSNR, CR, and Complexity. Also, the proposed method shows an improved result with another state of work.