Hybrid Compression Technique Research Articles

Hybrid technique for crop image compression using discrete wavelet transform and sparse singular vector reconstruction

In this paper, a hybrid compression technique, based on discrete wavelet transform (DWT) and singular vector sparse reconstruction (SVSR), is proposed. In the initial phase, an original image has been divided into four sub-band by using the DWT technique. After that, SVSR is utilized for boosting the compression ratio and to improve reconstruction using piecewise cubic Hermite interpolation (PCHIP). Inverse discrete wavelet transform (IDWT) process has been applied to obtain a compressed crop image. The first time, this developed technique has been applied for compression. The performance of proposed hybrid techniques has been assessed in terms of several evaluation parameters, which are compression ratio (CR), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and mean square error (MSE) on ten crop image data sets. It is observed from the results that the proposed technique is quite effective in compression of different types of crop images. Further, the experimental results show that the proposed hybrid method achieved significantly higher compression over the existing techniques. The proposed technique with Haar wavelet provides 412.5% and 94.97% higher CR as compared to Singular Value Decomposition (SVD), and SVSR respectively with desirable image quality. Further, other wavelets Db4 provide 396.48% and 88.88% and Bior3.5 also provides 306.74% and 54.74% higher CR as compared to SVD, and SVSR respectively with similar image quality.

Agent-based beat-by-beat compression of 12-lead electrocardiogram signal using adaptive Fourier decomposition

ObjectiveThe multilead electrocardiogram (MECG) signal provides much more detailed information related to the cardiac activity of the human heart than single channel signal. Long-term recording of MECG data needs a huge amount of storage space, thus data compression becomes truly necessary. MethodsIn this work, a beat-wise MECG data compression is proposed that is based on adaptive Fourier decomposition (AFD). To reduce dimensionality, an ECG beat was treated as a multiagent, upon which principal component (PC) analysis was used in non-linear space. A new Möbius transform was introduced along with AFD, to convert the dominant PCs in complex domain using Nevanlinna factorization. An offline trained multilayer perceptron neural network was also employed to provide optimal decomposition levels in AFD to limit the PRD within 3%. ResultThe entire work was tested on 546 ptbdb MECG records available in Physionet which yielded an average compression ratio and a percent root mean squared difference (PRD) of 48.21 and 3.88, respectively. Regardless of annotation type, PRD variance within each lead was found to be nearly uniform. ConclusionWithin a single beat, the agent-based compression established a hybrid compression technique, almost completely preserving critical clinical aspects. SignificanceBeat-wise compression, therefore requires less buffer memory, allowing it to be used for real-time data compression. The low reconstruction error enhanced the acceptability of proposed work in medical applications.

Hybrid compression technique for image hiding using Huffman, RLE and DWT

All kinds of information should be compressed before being sent over the web to save time & expense. The primary goal of this article is to create and construct a systematic and safe technique for encryption data that can be utilized to carry out a crisp compacting steganography technique. The approach may be used to reduce the volume of data sent with each transfer, allowing for faster delivery when utilizing sluggish web or taking up less space on a drive. The study offers a densification with steno approach that may be utilized to hide information with substantial privacy and complete invisible by combining RSA encryption technique with steganography expertise such as RLE (Run-length encoding), Huffman coding, DWT (Discrete Wavelet Transform) with LSB cryptographic technology. The very first process is based on RSA's ability to decode and encode private texts. The next process is compressing, which will include Huffman encoding based approaches to compress confidential stuff using efficient steganography technologies, RLE, which is a traditional approach to encrypt information, or DWT based on hatchbacks covering imagery by loss encryption in order to decrease the covering picture's size. The encrypted information will be implanted in the compressed cover picture using LSB (Least Significant Bit). When compared to various developed countries (Chen et al., 2021; Lakshmanan, 2020; Song et al., 2011; Yang et al., 2021; Ali et al., 2020) of the architecture that uses simulation results, this technique has a good ranking efficiency and a methodical system.

Improving Reconstructed Image Quality Via Hybrid Compression Techniques

Data compression is one of the core fields of study for applications of image and video processing. The raw data to be transmitted consumes large bandwidth and requires huge storage space as a result, it is desirable to represent the information in the data with considerably fewer bits by the mean of data compression techniques, the data must be reconstituted very similarly to the initial form. In this paper, a hybrid compression based on Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) is used to enhance the quality of the reconstructed image. These techniques are followed by entropy encoding such as Huffman coding to give additional compression. Huffman coding is optimal prefix code because of its implementation is more simple, faster, and easier than other codes. It needs less execution time and it is the shortest average length and the measurements for analysis are based upon Compression Ratio, Mean Square Error (MSE), and Peak Signal to Noise Ratio (PSNR). We applied a hybrid algorithm on (DWT–DCT 2 × 2, 4 × 4, 8 × 8, 16 × 16, 32 × 32) blocks. Finally, we show that by using a hybrid (DWT–DCT) compression technique, the PSNR is reconstructed for the image by using the proposed hybrid algorithm (DWT–DCT 8 × 8 block) is quite high than DCT.

Medical Image Application by Hybrid Transform Coding Scheme

Efficient compression of images using hybrid schemes has become the spotlight for developers in recent times. Hybrid compression techniques (DWT and DCT) are the basis of the proposed compression scheme presented in this paper. The aim here is to achieve higher compression threshold while maintaining the quality of the reconstructed image, attempting to achieve this by using different compression levels on wavelet coefficients of the DWT (HH and LL) bands, in the time when (HL and LH) bands are undergoing DCT transform. According to the type of transformation, adaptive quantization of the retained coefficient is performed. Lastly the quantization indices are encoded using entropy coding (variable shift coding). Results of our trails indicate using hybrid DWT-DCT algorithm significantly improves coding performance.

MICCS: A Novel Framework for Medical Image Compression Using Compressive Sensing

The vision of some particular applications such as robot-guided remote surgery where the image of a patient body will need to be captured by the smart visual sensor and to be sent on a real-time basis through a network of high bandwidth but yet limited. The particular problem considered for the study is to develop a mechanism of a hybrid approach of compression where the Region-of-Interest (ROI) should be compressed with lossless compression techniques and Non-ROI should be compressed with Compressive Sensing (CS) techniques. So the challenge is gaining equal image quality for both ROI and Non-ROI while overcoming optimized dimension reduction by sparsity into Non-ROI. It is essential to retain acceptable visual quality to Non-ROI compressed region to obtain a better reconstructed image. This step could bridge the trade-off between image quality and traffic load. The study outcomes were compared with traditional hybrid compression methods to find that proposed method achieves better compression performance as compared to conventional hybrid compression techniques on the performances parameters e.g. PSNR, MSE, and Compression Ratio.

Survey of Hybrid Image Compression Techniques

A compression process is to reduce or compress the size of data while maintaining the quality of information contained therein. This paper presents a survey of research papers discussing improvement of various hybrid compression techniques during the last decade. A hybrid compression technique is a technique combining excellent properties of each group of methods as is performed in JPEG compression method. This technique combines lossy and lossless compression method to obtain a high-quality compression ratio while maintaining the quality of the reconstructed image. Lossy compression technique produces a relatively high compression ratio, whereas lossless compression brings about high-quality data reconstruction as the data can later be decompressed with the same results as before the compression. Discussions of the knowledge of and issues about the ongoing hybrid compression technique development indicate the possibility of conducting further researches to improve the performance of image compression method.

Consistently faster and smaller compressed bitmaps with Roaring

SummaryCompressed bitmap indexes are used in databases and search engines. Many bitmap compression techniques have been proposed, almost all relying primarily on run‐length encoding (RLE). However, on unsorted data, we can get superior performance with a hybrid compression technique that uses both uncompressed bitmaps and packed arrays inside a two‐level tree. An instance of this technique, Roaring, has recently been proposed. Due to its good performance, it has been adopted by several production platforms (e.g., Apache Lucene, Apache Spark, Apache Kylin, and Druid). Yet there are cases where run‐length‐encoded bitmaps are smaller than the original Roaring bitmaps—typically when the data are sorted so that the bitmaps contain long compressible runs. To better handle these cases, we build a new Roaring hybrid that combines uncompressed bitmaps, packed arrays, and RLE‐compressed segments. The result is a new Roaring format that compresses better. Overall, our new implementation of Roaring can be several times faster (up to two orders of magnitude) than the implementations of traditional RLE‐based alternatives (WAH, Concise, and EWAH) while compressing better. We review the design choices and optimizations that make these good results possible. Copyright © 2016 John Wiley & Sons, Ltd.

An Enhanced N-Pattern Hybrid Technique for Medical Images in Telemedicine

In the last couple of years, the communication technology has rapidly revolutionized the human society, whose benefits are not homogeneously felt by the nations. There are still network coverage problems in the under developed and developing nations. This scarcity of the bandwidth obviously hinders telemedicine technologies benefits being available to the major population of the society. So, the data compression will be of great support to overcome the bandwidth limitations. Hence, an effective hybrid compression technique has been introduced for the medical images in telemedicine. In this method, the considered medical image is being segmented into two parts namely, Region of Interest (ROI) and Non-Region of Interest (Non-ROI). The ROI are compressed using the lossless “Enhanced N-Pattern Huffman” compression algorithm. The Non-ROI is compressed using the lossy “JPEG” compression algorithm. This combined method improves the space saving that enhances effective transmission of the compressed data even in the scarce bandwidth network coverage areas and at the same time maintaining the quality of the relevant diagnostic information.

Editorial Note

In this issue, variety of topics is presented as part of the rich world of Robotics and Mechatronics. We present to readers five papers, which are related to image compression technique, product development model, heaving wave energy control, human emotion detection, and computer controlled polishing for modern free-form lenses. The first paper, authored by Falila et al., presents a comparative study on traditional techniques of multi-spectral image compression, such as JPEG, JPEG2000, set partitioning in hierarchical trees (SPIHT), and their proposed modified SPIHT algorithm. The proposed technique is a hybrid compression technique which depends on interband JPEG prediction and the SPIHT algorithm which is extended for multi-spectral case. The experimental results presented indicate that the proposed technique outperformed the other three techniques in term of peak-signal-to noise ratio (PSNR) for multi-spectral image compression and transmission over OFDM communication system. The second paper, authored by Tseng Hsu et al., discusses the implementation of user-oriented model for collaborative product development, illustrated on medical-equipment industry. This type of industry has the characteristics of generating products with short life cycle, high technical complexity and a low chance of success. Therefore, it requires an intelligent system for its collaborative product development. The model contains a knowledge-based operation and maintenance system, a clinical test system, a file management system, an accessibility system, a calendar function and an announcement module. The system proposed by the authors can simplify the originally complex and dispersed process of product development. The third paper, authored by Wahyudie et al. is on optimal control strategy for heaving wave energy converters. The authors proposed a robust hierarchical control strategy which can provide optimal reference signal for various combinations of wave specific heights and frequencies under elevation's constraints. The controller consists of two parts, higher hierarchical controller (HHC) and lower hierarchical controller (LHC). The optimal reference signal is provided by the HHC, and the robust tracking action is performed by the LHC, which is based on a proportional-integral-derivative (PID) controller with sliding mode control (SMC). The proposed controller provides better results in terms of captured mechanical power, converted electrical power and efficiency in comparison to other existing control strategies, such as resistive loading control and conventional hierarchical control system. The fourth paper, authored by Md. Yusoff, et al., proposes human emotion detection system based on thermal imaging. The method uses the Gray Level Co-occurrence Matrix (GLCM) to analyze the heat pattern on the facial skin corresponds to particular affective states, and the k-Nearest Neighbor classifier to classify three human basic emotions (fear, happy and sad). Human-robot interaction has become one of very important topics in Robotics. In order to develop an emotionally intelligent robot with social and interactive skills, it is essential to understand human affective states. The proposed system has successfully detected the three states of emotions with an overall accuracy of approximately 90.3%. Last but not least, the fifth paper, authored by Mosaddegh et al., presents an open-loop fuzzy optimizer for computer controlled polishing (CCP) for optical lens manufacturing. CCP is an alternative approach for manufacturing process of optical lenses. The proposed open-loop fuzzy controller for CCP is designed to optimize the tool feed rate distribution over the toolpath to reduce the surface error in minimum time. Simulation and experimental results indicated that the proposed method has reduced the surface error over 85%. We hope readers find those valuable contributions beneficial and reflect the richness of research and development in the fields of Robotics and Mechatronics. We are looking forward to more high quality contributions in IJRM from all of you!

A Memory-Assisted Lossless Compression using Combination Methods

Image compression can be done in two ways namely lossy or lossless. Lossy methods are used for expected results like photographs where some negligible loss of data is tolerable. Rapid growth of medical science such as ehealth and telemedicine requires lossless Image compression. The correlation and redundancy which exists across different medical images are considered to achieve better compression ratio. . Better compression results in less storage and less overhead while transmission on the network. A memory- assisted hybrid compression technique is proposed. The approach is motivated by using a combination of methods, lossy and lossless. The original image is compressed using PCA algorithm and then using lossless coding methods. PCA is used to find the correlation among the similar medical images. PCA algorithm is used with only some principal components.

PERFORMANCE ANALYSIS OF IMAGE SECURITY BASED ON ENCRYPTED HYBRID COMPRESSION

In this research, we propose an image security scheme using hybrid compression techniques. In this scheme, the data is being provided two-fold security by both encryption stage and hiding stage. The data/message which has to be secured undergoes encryption technique at the initial stage. In this stage, the permutation algorithm is employed which requires a pair of numbers as a key to permute the original message. Following the encryption stage, the deformed message is then embedded onto a JPEG image by considering the low and high quantization tables. The main motivation behind this research work is to provide image security through compression. The final result is an encrypted and compressed JPEG image with a different image quality. The receiver has to perform the reverse process to extract the original data/information. The performance analysis is performed in terms of PSNR for different quantization tables.

Hybrid colour medical image compression by CDF wavelet and cosine transforms

In this research, we introduce an idea for Retinographic medical colour image compression, this new technique hybrid between Discrete Wavelet Transform (DWT) and Discrete Cosinus Transform (DCT). In recent times, developing hybrid schemes for effective image compression has gained enormous popularity among researchers. This research paper presents a proposed scheme for medical image compression based on hybrid compression technique (DWT and DCT). The goal is to achieve higher compression rates by applying different compression thresholds for the wavelet coefficients of each DWT band (LL and HH) while DCT transform is applied on LL bands with preserving the quality of reconstructed medical image. The retained coefficients are quantised by using adaptive quantisation according to the type of transformation. Finally the entropy coding (variable shift coding) is used to encode the quantisation indices. Experimental results show that the coding performance can be significantly improved by the hybrid DWT-DCT algorithm.

ECG Signal Compression: A Transform Based Approach

Over the years, a variety of other linear transforms have been developed which include discrete Fourier transform (DFT), discrete wavelet transform (DWT) and discrete cosine transform (DCT) and many more, each with its own advantages and disadvantages. Among these techniques, DWT has been proven to be very efficient for ECG signal coding. It is proposed to develop, a hybrid two-stage ECG signal compressor based on DCT and DWT. Proposed method is hybrid ECG compression technique based on wavelet transformation of the DCT coefficients of the signal. Interaction of DCT analysis with DWT transformations, signal thresholding and coding are a few of the many outstanding challenges in ECG compression. Proposed method will offer optimized CR and PRD, which would be suitable for most monitoring and diagnostic applications. KeywordsDCT, DWT, Wavelet, Image Processing, Signal Compression.

Efficient Hybrid Transform Scheme for Medical Image Compression

In recent times, developing hybrid schemes for effective image compression has gained enormous popularity among researchers. This research paper presents a proposed scheme for medical image compression based on hybrid compression technique (DWT and DCT). The goal is to achieve higher compression rates by applying different compression thresholds for the wavelet coefficients of each DWT band (LL and HH) while DCT transform is applied on (HL and LH) bands with preserving the quality of reconstructed medical image. The retained coefficients are quantized by using adaptive quantization according to the type of transformation. Finally the entropy coding (variable shift coding) is used to encode the quantization indices. Experimental results show that the coding performance can be significantly improved by the hybrid DWTDCT algorithm.

The Design and Evaluation of a Hybrid Compression Technique (HCT) for Wireless Sensor Network

The Design and Evaluation of a Hybrid Compression Technique (HCT) for Wireless Sensor Network

Crypt analysis of two time pads in case of compressed speech

Keystream reuse, also known as the two time pad problem, is a well known weakness in stream ciphers. The implementers of the cryptographic algorithms are still underestimating this threat. The keystream reuse exploitation techniques presented so far assume the underlying plaintext to be textual data and all the heuristics presented previously are based on the language characteristics of the underlying text based data, which fail when compression is applied on the plaintext before encryption. This paper presents exploitation techniques for two time pads in case of stream ciphered digitized and compressed speech signals. In this paper we show that how an adversary can automatically recover the digitized speech signals encrypted under the same keystream provided the language (e.g. English) and digital encoding/compression scheme details of the underlying speech signals are known. Our technique of cryptanalysis is based on the modeling of the speech parameters by exploiting the inter frame correlations between each components of the speech vectors in different frames and then using these models to decode the two speech signals in the keystream reuse scenario. The technique is flexible enough to incorporate all modern speech coding schemes based on parameter or hybrid encoding and compression techniques. The simulation experiments have showed promising results for most of the present day speech digitization and compression techniques.

A hybrid aggregation and compression technique for road network databases

Vector data and in particular road networks are being queried, hosted and processed in many application domains such as in mobile computing. Many client systems such as PDAs would prefer to receive the query results in unrasterized format without introducing an overhead on overall system performance and result size. While several general vector data compression schemes have been studied by different communities, we propose a novel approach in vector data compression which is easily integrated within a geospatial query processing system. It uses line aggregation to reduce the number of relevant tuples and Huffman compression to achieve a multi-resolution compressed representation of a road network database. Our experiments performed on an end-to-end prototype verify that our approach exhibits fast query processing on both client and server sides as well as high compression ratio.

Mobile Commerce

Mobile commerce is emerging as the new Internet business model, where wireless devices are used to extend electronic business services and reach. In mobile commerce, most net traffic is multimedia-based, which means there is a real need for an efficient hybrid multimedia compression technique that improves the communication channel performance. Most document files consist of a combination of text, graphic, and picture segments and consequently require different compression algorithms for their efficient coding. This combination of data usually comes in different file formats. Therefore, a technique is needed to recognize each of these formats before the optimal compression algorithms can be applied. This paper proposes a framework for improving the performance of mobile commerce through an efficient and intelligent hybrid compression technique that is able to automatically separate images from text data and recognize different image file formats in a data stream being transmitted. The resulting system will significantly improve the communication capacity in wireless communication, which is one of the important success factors in mobile commerce application.

Numerical techniques for solving nonlinear instability problems in solid rocket motors

The results of an investigation to select a satisfactory numerical method for calculating the propagation of steep-fronted, shock-like waveforms in a solid rocket motor combustion chamber are presented. A number of different numerical schemes were evaluated by comparing the results obtained for three related but simpler problems: the shock-tube problem, the linear wave equation, and nonlinear wave propagation in a closed tube. The most promising method, a combination of the Lax-Wendroff, Hybrid, and Artificial Compression techniques, was incorporated into an existing nonlinear combustion instability program. The capability of the modified program to treat steep-fronted wave instabilities in low-smoke tactical motors was verified by solving a number of motor test cases with disturbance amplitudes as high as 80% of the mean pressure.