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Compression is the specialty of presenting the data in a conservative structure as opposed to its unique or uncompressed structure. Moreover, utilizing information compression, the size of a specific document can be decreased. This is extremely helpful when preparing, putting away, or moving a gigantic document, which needs huge resources. The speed of transmission relies on the number of pieces sent, the time needed for the encoder to create the coded message, and the time needed for the decoder to recoup the primary ensemble. In an information storage application, the level of compression is the essential concern. Compression can be named either lossless or lossy. Lossless compression methods remake the primary data from the compacted record with no loss of information. In this manner, the data does not alter during the decompression and compression measures. These sorts of compression calculations are called reversible compressions since the primary message is recreated by the decompression cycle. This paper analyzes the exhibition of the Huffman Encoding Algorithm, Lempel Zev Welch Algorithm, Arithmetic Encoding Algorithm, Adaptive Huffman Encoding Algorithm, Shannon Fano Algorithm, and Run Length Encoding Algorithm. Specifically, the efficiency of these calculations in compacting text information is compressed and assessed.

With several advantages over conventional RGB cameras, event cameras have provided new opportunities for tackling visual tasks under challenging scenarios with fast motion, high dynamic range, and/or power constraint. Yet unlike image/video compression, the performance of event compression algorithm is far from satisfying and practical. The main challenge for compressing events is the unique event data form, i.e., a stream of asynchronously fired event tuples each encoding the 2D spatial location, timestamp, and polarity (denoting an increase or decrease in brightness). Since events only encode temporal variations, they lack spatial structure which is crucial for compression. To address this problem, we propose a novel event compression algorithm based on a quad tree (QT) segmentation map derived from the adjacent intensity images. The QT informs 2D spatial priority within the 3D space-time volume. In the event encoding step, events are first aggregated over time to form polarity-based event histograms. The histograms are then variably sampled via Poisson Disk Sampling prioritized by the QT based segmentation map. Next, differential encoding and run length encoding are employed for encoding the spatial and polarity information of the sampled events, respectively, followed by Huffman encoding to produce the final encoded events. Our Poisson Disk Sampling based Lossy Event Compression (PDS-LEC) algorithm performs rate-distortion based optimal allocation. On average, our algorithm achieves greater than 6x compression compared to the state of the art., 8 main pages

In Marine diesel engine vibration monitoring system, a large number of high frequency data can be detected by high frequency sensors. Using data compression technology can effectively reduce the amount of data monitored by the ship vibration monitoring system and improve the communication efficiency of the system. In this paper, a lossless compression method combining variational mode decomposition (VMD) and run length encoding (RLE) for marine diesel high-frequency data is proposed. First, the signal is decomposed by VMD, and then the decomposed sub-signals and residual signals are encoded. In order to obtain better compression ratio, the compression method of RLE and Lempel-Ziv-Welch (LZW) is adopted. Experimental results show that this method can compress data effectively for high frequency signal of diesel engine morning and save network bandwidth.

The Electroencephalography (EEG) signals that indicate the electrical activity of the brain are acquired with a high sampling rate. Consequently, the size of the recorded EEG data is large. For storing and transmitting these data, large space and bandwidth are demanded. Therefore, preprocessing and compressing EEG data are important for efficient data transmission and storage. The purpose of this approach is to design an efficient EEG data compression system in terms of time and space complexities. The proposed system consists of three main units: preprocessing unit, compression unit, and reconstruction unit. The core of the compression process occurs in the compression unit. Different combinations of hybrid lossy/lossless compression techniques were tried in the compression process. In this study, both the Discrete Cosine Transform and the Discrete Wavelet Transform techniques were experimented for the lossy compression algorithm. The Arithmetic Coding and the Run Length Encoding were experimented then for the lossless compression algorithm. The final results showed that combining both the Discrete Cosine Transform and the Run Length Encoding yields the most optimal system complexity and compression ratio. This approach achieved up to CR = 94% at RMSE = 0.188.

This paper presents an electrocardiogram (ECG) compression algorithm based on discrete wavelet transform (DWT) and particle swarm optimization (PSO). This technique uses PSO to find the optimal values of wavelet design parameters and threshold levels for ECG compression. In this method, ECG signal is segmented into different sub-bands to obtain the wavelet coefficients. Then, threshold values for each sub- band are selected using PSO. The coefficients with value below the threshold level are left, and the significant coefficients are encoded using modified run length encoding. The performance measures used in this paper are quality score (QS), compression ratio (CR), signal-to-noise ratio (SNR) and percentage root-mean-square difference (PRD). This method is capable of providing higher compression ratio with minimum distortion in ECG signal. Compression ratio obtained using this algorithm is 28.43 at PRD=2.63.

Data in today's world is the most important asset one can have, but with it comes the issue of handling it properly. To increase data portability, facilitated by reducing the size of data to be stored due to limited storage space, a strong need for data compression has arisen. A lot of ways have been defined for data compression; since the 1950's we have been digging up and devising many algorithms to modify data compression techniques and over the years, many seemingly powerful algorithms have been developed. With a wide range of compression techniques available, there comes the need to choose the right method for text compression, which gives good results in lesser time. In this paper, we present a hybrid approach to compress text data. This hybrid approach encompasses various methodologies like Run Length Encoding (RLE), Infix Encoding and Bit reduction techniques. We aim to overlay different techniques to achieve the best possible compression ratio for large text files and dictionary data.

Images are among the most common and popular representations of data. Digital images are used for professional and personal use ranging from official documents to social media. Thus, any Organization or individual needs to store and share a large number of images. One of the most common issues associated with using images is the potentially large file-size of the image. Advancements in image acquisition technology and an increase in the popularity of digital content means that images now have very high resolutions and high quality, inevitably leading to an increase in size. Image compression has become one of the most important parts of image processing these days due to this. The goal is to achieve the least size possible for an image while not compromising on the quality of the image, that gives us the perfect balance. Therefore, to achieve this perfect balance many compression techniques have been devised and it is not possible to pinpoint the best one because it is really dependent on the type of image to be compressed. So here we are going to elaborate on converting images into binary images and the Run length Encoding (RLE) algorithm used for compressing binary images. Now, RLE is itself a very effective and simple approach for compression of images but, sometimes, the size of an image actually increases after RLE algorithm is applied to the image and this is one of the major drawbacks of RLE. In this research paper we are going to propose an extension or maybe an upgradation to RLE method which will ensure that the size of an image never exceeds beyond its original size, even in the worst possible scenario

Images are among the most common and popular representations of data. Digital images are used for professional and personal use ranging from official documents to social media. Thus, any Organization or individual needs to store and share a large number of images. One of the most common issues associated with using images is the potentially large file-size of the image. Advancements in image acquisition technology and an increase in the popularity of digital content means that images now have very high resolutions and high quality, inevitably leading to an increase in size. Storage limitations on any device mean that only a small number of such images can be stored. Moreover, transmitting and uploading larger images takes up more time and bandwidth. Image compression has thus emerged as a vital part of image processing. The need of the hour is to reduce the size of the image as much as possible ,while maintaining a high level of quality and preserving all the details in the image. Compressed images can also be transmitted faster and require less bandwidth. In this paper we will be discussing two compression techniques- "Lempel–Ziv–Welch (LZW)" and "Run length Encoding (RLE))" and how to implement them. We will then compare them based on certain parameters.

The paper deals with the problematics of lossless data compression and is focused on the use of Run-Length Encoding (RLE) principle of lossless compression of binary images, especially binarized volume datasets that can be considered as the 3D binary images. Different alternative encoding schemas are proposed in the first part of this work for bit-level run-length encoding and capacities of their encoding words are discussed also with the focus on the encoding of long symbol runs. Test results of proposed encoding schemas using binarized volume datasets obtained by the medical imaging techniques including Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) and the influence of the length of the encoding word to the compression rate of proposed encoding schemas are discussed in the second part of the paper.

A new method of signal compression, called Quantized Run Length Encoding QRLE, based upon the ‘classical’ run length method combined to discrete wavelet transform thresholding planned for a transmission via the WiFi IEEE 802.11b WLAN channel, simulated by Simulink/Matlab Integrated Development Environment IDE, is presented in this work. The key idea of our new method consists of quantifying each pair of zero followed by its corresponding run number, issued from the RLE, by one value consisting on the run number value plus an offset of predetermined integer value. In this work, the suitable offset was adjusted to 1024(≡210). This leads to quantizing the ‘new’ run number on 11 bits while the non null ECG thresholded ECG signal samples are quantized on 10 bits. The trivial advantage of this method is suppression, and consequently gaining, of all zeros. We have applied the proposed algorithm to extracted ECG signals, from the universal MIT-BIH arrhythmia data base. To evaluate the performance of the new QRLE compression method, we have simulated transmitting the compressed ECG signal via the Simulink/Maltab IEEE 802.11b WLAN noisy channel model. Besides the perceptible visual inspection, three criteria are used, in our evaluation phase, that are: compression ratio –CR-, the normalized root mean squared error (NRMSE), and the difference between the original and reconstructed versions of the ECG signal. The obtained results are around 10:1 in terms of CR, 0.07 in terms of NRMSE and a difference of about (10−4) ± 0.02. Moreover, a comparative study with respect to selected ECG compression algorithms show the higher performance of the developed new technique called ‘Quantized run length encoding QRLE’.

In this article we present a new method of stegoanalysis revealing the text produced by the synonym runlength encoding steganography. During this research, it was shown that RLE embedding method changes statistical structure of extracted message and this fact could be used for stegoanalysis. In the constructed method we compare the probability distribution of runs (with length no more than 5 bits) in the message extracted from the analyzed container with reference distributions corresponding to empty and filled containers. The Kullback-Leibler measure is used as a divergence measure of two probability distributions. It was shown that the proposed method makes it possible to detect a secret message in the container having 500 synonyms, while the false negative error is 1.5% and the false positive error is 1.3%. In comparison with known analogs, the proposed method has higher accuracy of steganalysis with smaller size of input data.

Social network, such as, Facebook, Twitter, Microblog, and WeChat, has millions of users, many of which have happily combined these sites and their daily practices. The social network platforms above support their users to connect with each other based on shared interests, political views, or other activities. Therefore, social networks will be a big market which can be explored and exploited for steganography. In this paper, according to social network behavior, we proposed an audio steganography scheme based on the time length of WeChat voice message, which utilizes the time length of voice message to represent the secret information. Based on such a social network behavior—sending a specific length of voice message, the secret message is successfully sent to the receiver secretly. Qualitative analyses show that our method has a good ability to resist statistical analysis and fault tolerance. The result of experiment indicates that the sender and the receiver can complete the transmission of secret information in a WeChat group. Although our method is simple with low capacity, it may open a new window for social network behavior-based steganography research.

Data compression plays a significant role in Body Sensor Networks (BSN). This is true since the sensors in BSNs have limited battery power and memory; sensor data needs to be transmitted regularly, and in lossless manner to provide prompt, accurate feedback. The paper evaluates lossless data compression algorithms including Run Length Encoding (RLE), Lempel Zev Welch (LZW), and Huffman on data from wearable devices and compares them in terms of Compression Ratio, Compression Factor, Savings Percentage and Compression Time. It also evaluates a data deduplication technique used for Low Bandwidth File Systems (LBFS), Two Thresholds Two Divisors (TTTD) algorithm, to determine if it is suitable for BSN data. First, through experiments s we arrive at a set of parameter values that give compression ratio above 50 on BSN data. Next, based on performance evaluation results of TTTD and classical compression algorithms including RLW, LAW, and Huffman, it proposes a technique to combine multiple algorithms in sequence. Upon comparison of the performance, it is found that the new algorithm, TTTD-H, which executes TTTD and Huffman in sequence, significantly improves the compression factor against both TTTD and Huffman. Performance evaluation has been carried out in two sets of BSN data.

Many digital media that can be used as cover in steganography, such as image, audio, and video. The most frequently used media is image. Compression technique for digital image can be differentiated into two, i.e. lossy and lossless compression techniques. In lossless compression technique there are some algorithms, which include Run Length Encoding algorithm in file Bitmap Picture Format, Lempel-Ziv-Welch in Graphic Interchange Format, and LZW algorithm which is combined with Huffman Encoding algorithm in Portable Network Graphics Format This lossless compression technique is more suitably implemented in steganography method in spatial domain, with its competitive advantage in payload capacity. In this study digital image with three file formats were used in steganography method in spatial domain, i.e. Least Significant Bit. The aim was to find out the comparison among the three by testing the change in quality and the characteristics that occur. The result, digital image with BMP and PNG file formats which identical values after assessing the quality change (Image Quality Assessment) and characteristic change. From the testing the MSE value of 7.674×10−6 and PSNR value of 99.3147 dB were found. The change in characteristic values obtained from the characters testing was very small, i.e. changes in the values for Mean, Standard Deviation and Entropy are 1.31×10−4, 2.18×10−5, and 0.0012. For the future, steganography method in two different domains can be combined and to find a better level of security they can also be combined with cryptography.

Data as well as hardware characteristics are two key aspects for efficient data management. This holds in particular for the field of in-memory data processing. Aside from increasing main memory capacities, efficient in-memory processing benefits from novel processing concepts based on lightweight compressed data. Thus, an active research field deals with the adaptation of new hardware features such as vectorization using SIMD instructions to speedup lightweight data compression algorithms. Following this trend, we propose a novel approach for run-length encoding, a well-known and often applied lightweight compression technique. Our novel approach is based on newly introduced conflict detection (CD) instructions in Intel's AVX-512 instruction set extension. As we are going to show, our CD-based approach has unique properties and outperforms the state-of-the-art RLE approach for data sets with small run lengths.

In this paper, We implement the Discrete Cosine Transform (DCT) coding (lossy compression method) followed by the proposed coding technique which called “Quantized Huffman Coding” in order to minimize the EEG data size. Therefore, adding a lossless compression algorithm after the lossy compression is a good idea to get a high compression ratio with acceptable distortion in the original signal. Here, we use DCT encoder followed by either quantized Huffman Coding or Run Length Encoding (RLE) then compare between them. Our work shows that, at the same Root Mean Square Error (RMSE), the quantized Huffman coding outperforms the RLE in some aspects as Compression Ratio (CR) and time consumed in compression and decompression, but Structural Similarity Index (SSIM) is the same for the two techniques.

In this paper, We propose a hybrid compression technique by integrating the Discrete Cosine Transform (DCT) and a Non-Uniform Quantized Huffman in order to minimize the Electroencephalography (EEG) data size. Therefore, to get a high compression ratio we apply the lossy compression followed by a lossless compression algorithm. We use DCT encoder followed by either non-uniform quantized Huffman (NonUQH) coding or Run Length Encoding (RLE) then compare between them. The system performance is evaluated in terms of the compression/decompression time, the compression ratio, and the root mean square error. The proposed hybrid technique DCT/NonUQH achieves 90% compression compared to 59% by DCT/RLE with the same similarity. Furthermore, it needs 50% less time for compression/decompression process.

The Burrows-Wheeler Transform (BWT) has received special attention due to its effectiveness in lossless data compression algorithms. Because BWT is a time-consuming task, the efficient hardware accelerator that can yield high throughputs is required in real-time applications. This paper presents a novel BWT accelerator based on the streaming sorting network. The streaming sorting network performs the suffix sorting of large amount of data which is the most difficult task in BWT. Our BWT accelerator is implemented on a NetFPGA board. Experimental results show that it achieves 14.3X speedup compared with the state-of-art work when the data block size is 4KB. Furthermore, we design and implement a lossless data compression system based on the proposed BWT accelerator. The hardware system is composed of Burrows-Wheeler Transform module, the move-to-front encoding module, the run length encoding module, and the canonical Huffman encoding module. We evaluate the system performance on a NetFPGA board at the frequency of 155MHz. The throughput of the system could reach 179 MB/s on board when we use only one streaming sorting network for a 4KB block. The system throughput can be linearly improved up to 537 MB/s in simulation on a Virtex UltraScale xcvu440 chip if we use three streaming sorting networks to compute BWT.

This paper considers implementation of audio compression using the lossless compression techniques like dynamic Huffman coding and Run Length Encoding (RLE). Audio file is firstly preprocessed to find sampling frequency and the encoded data bits in sample audio file. After that dynamic Huffman and RLE is applied. The design of dynamic Huffman coding technique involves evaluation of the probabilities of occurrence “on the fly”, as the ensemble is being transmitted and RLE is based on finding the runs of the data i.e. repeating strings and replacing it by single data element and its count. These techniques work with a common goal to obtain the utmost possible compression ratio and less Time Elapsed to compress. The competence of the proposed methods is verified by applying these techniques to variety of audio data. Stimulus behind this work is to offer a detail analysis of lossless compression methods and finding the one which is best suited for compression of multimedia data in cognitive radio environment.

Malware is a widespread problem and despite the common use of anti-virus software, the diversity of malware is still increasing. A major challenge facing the anti-virus industry is how to effectively detect thousands of malware samples that are received every day. In this paper, a novel approach based Run Length Encoding (RLE) algorithm and n-gram are proposed to improve malware detect on dynamic analysis of based on API sequences.

This paper proposes a color attribute compression method for MPEG Point Cloud Compression (PCC) by exploiting the spatial redundancy among the adjacent points. With the increased interest in representing real-world surface as 3D point clouds, compressing the attributes (i.e., colors and normal directions) of point cloud has attracted great attention in MPEG. The proposed method is based on grouping the adjacent points in blocks. And two encoding modes are supported for each block, which include the run-length encoding mode and palette mode. The final encoding mode for each block is determined through comparing two distortion values based on two encoding modes. Experimental results show that the proposed approach achieves about 28 percent compression ratio than that of MPEG PCC.

As the network technologies are improving, more challenges are coming forward in form of huge amount of data being transferred through the network. A large portion of such data is of multimedia type consisting of huge amount of digital images being sent and received through the network. In this paper, an integrated image compression and encryption technique using run length encoding scheme and henon chaotic map is presented. Run length encoding scheme is common scheme and a natural choice for image compression. Run length encoding generates (value, count) pairs such that the value is repeated ‘count’ number of times. In this paper, we used the run length encoding technique for lossy image compression. We designed a lossy run length encoder that exploits the pixel redundancy and visual imperceptibility of human eye to fine details in the digital images. Along with compression we perform image encryption using henon chaotic map. After encryption the size and resolution of the image is changed that further enhances the security. Various experiments are performed calculating various performance matrices-histogram, information entropy, PSNR, Compression ratio, and MSE. The algorithm is secure enough to thwart various statistical attacks while being easy to implement and fast.

The nematode Caenorhabditis elegans is widely used as a genetic model organism because of its relevance to human biology and disease. Quantifying C. elegans movement behavior is important for understanding the differences between genotypes under various food conditions. One of the most common ways to quantify C. elegans movement is through path analysis. However, comparing the movement paths between different C. elegans genotypes is challenging because of the variations in the trajectories' time lengths. We propose a novel approach that combines Multivariate Dynamic Time Warping and step-length path encoding to cluster C. elegans movement path data and further show that the proposed combination avoids the need for extracting complex path features.

In order to reduce the pressure of data storage and transmission on satellite, researchers implemented a method of object region data extraction from remote sensing image in orbit. This method stores and downloads pixels of interesting region through interesting region labeling. But encoding data volume (EDV), hardware scale and real-time property (RTP) are difficult to be balanced. To solve this problem, the paper proposes the Adaptive Run-length Encoding (ARLE) circuit which is used in target region labeling and applied in FPGA. The circuits are designed upon cascaded structure which is simple, lightweight, modular, extensible and transplantable. Experiment shows that comparing with the existing methods, ARLE circuit has better compression effect and better utilization of resource. And it does not only ensure RTP but also narrow the circuit scale (CS). The target region extraction method can be easily extended to various application scenarios of rapid target region extraction. The ARLE circuit can be directly applied to real-time dataflow encoding between FPGA and external storage devices.

Modern Biotechnology produces large amount of genomic data. The explosion of DNA data has given a challenge for understanding genomic structure, the disk storage and computation. It is essential for the development of efficient compression techniques to handle genomic data storage. Data compression is used to store the data in less memory. The properties of DNA sequence offer a chance to build DNA specific compression algorithms. In this paper, a novel compression technique is proposed for genomic data. In the first stage, each base in DNA sequence is converted into binary form using 2-bit encoding system. On the resultant binary string, A Modified run length encoding is applied. The output is compressed again using Huffman encoding technique in second stage. The encoded sequence is converted into ASCII characters. This technique is quite simple and effective.

Space systems demand some of the most precise and accurate technology. The body of space systems are subjected to many vibrations and shocks. To make sure their stable operation, these vibrations are monitored continuously at a higher sampling rate which results in large amount of data. The data is transmitted over a wireless network to the ground station. Therefore data compression becomes unavoidable, not only for efficient utilization of transmission bandwidth but also for reduced storage requirements. Even the smallest vibration is critical in space applications. So lossless compression techniques are preferred. In this work, a lossless compression technique is proposed where the input data is split into two sets which helps in attaining data redundancy and then the algorithm is applied. Modified Move to front(MTF) coding is done on this data, where positional values of a dictionary are transmitted instead of the sample value. Run length encoding(RLE) is done on the MTF coded data. Next all the successively repeating number patterns in the data set is recognized and RLE coded. The compression ratio obtained for data without and with noise are 4.15 and 2.75 respectively.

A hybrid test data compression method is presented which is targeted at minimizing the volume of test data, which reduces memory requirements for test data and also time required to test the entire data. The compression scheme is so called hybrid as it combines a transform along with the encoding scheme. In the proposed approach, encoding schemes such as Frequency Directed Run length encoding and Shannon Fano encoding schemes are applied on the transformed data. The proposed scheme is applied on ISCAS'85, ISCAS'89 and ITC'99 benchmark circuits and compared in terms of their compression ratio.

In the modern digital era, we are welcoming the data with an enormous collection of information from different sorts of day to day life. There are promising challenges for handling as well as storing the data. For the past three decades many researchers have developed algorithms to meet the crucial challenges in frequent pattern mining; but still there is a thirst to overcome it. Based on this, a new algorithm such as FREQVCTDB (Frequent Vertical Compressed Transaction Database) is developed for preserving memory space by compressing the transaction database with statistical analysis in vertical data layout. The proposed algorithm has also adopted and analysed the dense dataset and sparse dataset based on the statistical analysis with prior knowledge about dataset and it is carried out in three phases. In the phase 1, the basic step is carried out such as horizontal data layout is converted into vertical data layout. Then the frequency of each item is counted and checked with the minsupport threshold filter for all items whether it is frequent or not. The input data is analyzed by using statistical properties of transaction database in phase 2. The properties such as a) Density of two types such as Maximum density or Minimum density b) Distance function c) Entropy function from information theory are analyzed. Based on this analysis the algorithm the Run length Encoding is implemented for compressing the frequent patterns in phase 3. The two types of datasets such as dense dataset and sparse dataset are analysed and discussed which have been taken from the valid website Fimi.cs.uk. These datasets are executed and the experimental results are shown in a graphical representation as validity results.

The computation of convolutions is a fundamental problem that arises in applications from different fields as digital signal processing, image processing and string processing, among others. Here, we provide an in-depth investigation of the potential of Run Length Encoding and Lempel-Ziv based methods for efficiently computing convolutions between a sequence of patterns of a fixed shape/size and a given image. Our contribution consists in developing new methods and variants of existing ones and providing (extensive) empirical evaluations of them. Our fastest method outperforms a highly optimized implementation based on Fast Fourier Transform for small patterns.

Power quality disturbance is a one the challenging issues, where many researchers are gaining the attention towards it, now a day it is very much necessary to monitor power quality disturbances for analyze and other purpose. The quantity of the data captured in present power quality monitoring system has been increasing drastically. It is difficult to store and transmission huge data. So compression technique is required to reduce the storage space for data. This paper proposes Dual tree complex wavelet transform (DTCWT) method for power quality monitoring and integrates run length encoding technique for compress disturbance data. Voltage sag, swell, transients and flickers are Power quality disturbances used to test the proposed method. And Matlab was used for generation of test signals and to implement the different algorithms for data compression.

This paper presents a Zynq-based system to compute Run-Length encoding Matrix features for retinal image texture analysis. In order to improve the performance of the software implementation, we propose a co-processor architecture implemented in the programmable logic portion of the Zynq platform. Experimental results show a speedup of 26.3× with respect to the software version implemented on the ARM processor alone, for 2496 × 1664 images. The additional area to implement the co-processor is limited to 13% of DSP48E1s slices and about 2% for LUTs and flip-flops.

Three-dimensional reconstruction plays an important role in the real practice. Since the three-dimensional reconstruction technique based on structured light is seriously restricted by the light conditions, the three-dimensional reconstruction technique based on passive optical binocular vision is widely applied with the superiority of unlimited by the environment conditions. However, the precision of the reconstruction is not satisfying. The point cloud registration technique of three-dimensional reconstruction can greatly improve the drawback, but the traditional ICP algorithm endures a low iteration speed owing to large iterations and the precision is still unsatisfying. This paper presents a point cloud registration technique algorithm of three-dimensional reconstruction based on assistant camera array system, which creatively takes advantage of run length encoding to do the corner recognition, and can acquire accurate registration results without iteration. Experimental results indicate a greater improvement in accuracy and speed compared with ICP algorithm.

This paper presents a fast implementation of multi-band blending for combining a set of registered images into a composite mosaic with no visible seams and minimal texture distortion. We first compute a unique seam image using two-pass nearest distance transform, which is independent on the order of input images and has good scalability. Each individual mask can be extracted from this seam image quickly. To promote execution speed and reduce memory usage in building large area mosaics, the seam image and masks are compressed using run-length encoding, and all the following mask operations are built on run-length encoding scheme. The use of run-length encoding for masks processing leads to reduced memory requirements and a compact storage of the mask data. We apply our fast blending system to large scale data sets and present detailed quantitative results compared with OpenCV and Enblend to demonstrate the speed improvements.

Augmented reality brings reality into virtual world experience, it's one of the fastest emerging and challenging field experienced. In this paper, we proposed a model VISORV which helps partially visually impaired people to easily navigate through the streets by reading aloud sign (direction) boards usually placed on the streets using a marker detection technique. The proposed algorithm is divided into three phases: Marker Detection Algorithm (MDA), Marker Identification Algorithm (MIA) and Audio formation. The Marker Detection phase involves detecting a square marker by calculating angle for each pixel and applying edge dwindling algorithm (EDA). In the second phase, marker already being detected in the first phase is identified by efficiently matching it against the markers stored in the database. The Matched marker fetches the direction along with their destinations from the database. In the final phase, guidance is being provided to the end user in the form of audio, stored in a database corresponding to marker ID identified in second phase. Various models adopted in this work are: RGB to Gray scale conversion, Shrinking, Expansion, Gray Scale to Binary Conversion, Run length encoding algorithm. Finally the model is simulated for test markers with different patterns stored and validate the proposed design.

In this paper a new compression scheme has been formulated based on jointly delta modulation with biasing and Run Length Encoding technique for Direct Normalized Solar Irradiance data. The key features of this Compression Algorithm (Delta Modulation with Biasing and Run Length Encoding techniques viz. DMBRLE) are: it requires less memory as it belongs to direct compression method and its lossless nature. The main Goal of this algorithm has been achieved by compressing successive same sample elements as well as low frequency sample changes region. This is substantiated on jointly First Sample Difference (FSD) and S-Run Length Encoding (S-RLE) techniques. FSD is modified to concentrate at low frequency spectra. Simultaneously S-Run length Encoding is structured to fix the region having successive same sample value. The Compression Algorithm is modified stepwise and its effect on that is a large non-linear in nature. However, the quality of the recovered signal is demonstrated by computing Normalized Root Mean Square Error (NRMES). The result establishes that this developed compression scheme increases the Compression Ratio up to a reasonable value without threw off the Lossless nature.

Photoplethysmography (PPG) is one of the prime non-invasive vascular assessment methodology adopted in modern clinical practice. This paper proposes a modification of delta modulation for effective compression of PPG signal for real time measurements and monitoring applications. Based on the energy content and fluctuations, the PPG signal was windowed and categorized into ‘complex’ or ‘plain’ zones. The algorithm applies an adaptive hard thresholding, run length encoding and selective biasing of the first difference array based on zonal complexity. The final stage employs a selective nibble combination or offsetting rule for further compression. For performance assessment of the compression-decompression algorithms, PPG data was collected from fingers of healthy volunteers using Biopac Systems®. An average compression ratio of 3.84, percentage root mean square difference of 5.82 and percentage root mean square difference normalized of 7.57 were obtained with 20 sets of volunteers' data at 10 bit resolution and 125 Hz sampling. The decompressed data were clinically validated by Cardiologists.