Length Encoding Research Articles

Аdaptive combined image coding with prediction of arithmetic code volume

The problem of increasing the efficiency of coding of halftone images in the space of bit planes of differences in pixel values obtained using differential coding (DPCM – Differential pulse-code modulation) is considered. For a compact representation of DPCM pixel values, it is proposed to use a combined compression encoder that implements arithmetic coding and run-length coding. An arithmetic encoder provides high compression ratios, but has high computational complexity and significant encoding overhead. This makes it effective primarily for compressing the mean-value bit-planes of DPCM pixel values. Run-length coding is extremely simple and outperforms arithmetic coding in compressing long sequences of repetitive symbols that often occur in the upper bit planes of DPCM pixel values. For DPCM bit planes of pixel values of any image, a combination of simple run length coders and complex arithmetic coders can be selected that provides the maximum compression ratio for each bit plane and all planes in general with the least computational complexity. As a result, each image has its own effective combined encoder structure, which depends on the distribution of bits in the bit planes of the DPCM pixel values. To adapt the structure of the combined encoder to the distribution of bits in the bit planes of DPCM pixel values, the article proposes to use prediction of the volume of arithmetic code based on entropy and comparison of the obtained predicted value with the volume of run length code. The entropy is calculated based on the values of the number of repetitions of ones and zero symbols, which are obtained as intermediate results of the run length encoding. This does not require additional computational costs. It was found that in comparison with the adaptation of the combined encoder structure using direct determination of the arithmetic code volume of each bit plane of DPCM pixel values, the proposed encoder structure provides a significant reduction in computational complexity while maintaining high image compression ratios.

Research on telemetry data compression technology based on inter frame differential adaptive run length encoding

Aiming at the problem of massive historical telemetry data storage in flight test, a lossless compression method based on adaptive interval run length encoding is proposed. Aiming at the problem of low compression efficiency of traditional run length encoding algorithm for word data, by studying the storage characteristics of telemetry data, this algorithm automatically identifies the frame format of telemetry data, and carries out longitudinal run length adaptive interval encoding for inter frame differential data to improve the compression efficiency. The test results show that the compression ratio of the improved algorithm is improved by 58.1% and 1.5% compared with the traditional run length encoding algorithm and the inter frame differential lateral run length encoding algorithm.

An Audio Steganography Based on Run Length Encoding and Integer Wavelet Transform

This paper proposes an audio steganography method based on run length encoding and integer wavelet transform which can be used to hide secret message in digital audio. The major contribution of the proposed scheme is to propose an audio steganography with high capacity, where the secret information is compressed by run length encoding. In the applicable scenario, the main purpose is to hide as more information as possible in the cover audio files. First, the secret information is chaotic scrambling, then the result of scrambling is run length encoded, and finally, the secret information is embedded into integer wavelet coefficients. The experimental results and comparison with existing technique show that by utilizing the lossless compression of run length encoding and anti-attack of wavelet domain, the proposed method has improved the capacity, good audio quality, and can achieve blind extraction while maintaining imperceptibility and strong robustness.

WITHDRAWN: Compression techniques for performance optimisation in columnar databases

Data has become an integral part of today’s life. Traditional relational databases are row oriented databases designed for row wise data storage and retrieval which consumes more time to return queries. Recently, Columnar databases have dominated traditional databases due to their effective read operations to and from hard disk to decrease the interval to return queries. Columnar databases are mainly designed for data warehouses where huge amount of data collected from several sources are needed to perform Business Intelligence to build decision making systems. Columnar databases have quicker query performance because the data is packed close together which decreases seek time. They support various compression techniques for quicker data access with the help of aggregation queries to filter the irrelevant data. This paper discusses about various compression techniques such as NULL Suppression, Dictionary Encoding, Run Length Encoding, Bit Vector Encoding and Lempel Ziv Encoding which are being popularly used to optimise the performance of the columnar databases. Database operations are performed on the compressed data to prove the reduction in the requirements of memory and enhancements in the performance.

A mobile based communication solution for monitoring remote agricultural locations

In this paper a method for the effective transmission of data, from a Wireless Sensor Network (WSN) in a remote agricultural location with limited connectivity, is proposed; along with a meaningful visualisation of the data at the user end to help in decision making and control of the irrigation system. The paper encompasses three main segments which are data compression, checking for network availability, and user interface. First data compression is implemented to reduce the amount of transmitted data, due to limited connectivity in the area, which is translated in low data rate and high cost of sending the data. Four different techniques are compared: Huffman encoding, Lempel-Ziv-Welsh (LZW), differential encoding, and Run Length Encoding (RLE) to carry out data compression, where Huffman encoding showed the best results when combined with differential encoding. The second phase is the handling of poor network conditions in the agricultural plant by switching between two different communication modes: Mobile data and Short Message Service (SMS). Lastly, the user monitoring centre which is a mobile application that allows the user to monitor and control the agricultural plant remotely. It includes mapping and visualization of temperature and soil moisture data and a control system to control the irrigation system. The solution presents a low-cost alternative for start-up agricultural projects in third world countries, where network instability is an issue and off-the-shelf expensive solutions are not a viable option.

An efficient and novel data clustering and run length encoding approach to image compression

AbstractThe paper explores the domain of lossy compression, specifically incorporating data mining techniques in the process of image encoding. Clustering is employed to group similar pixels in the image and henceforth use cluster labels in compressing the image. The proposed approach replaces the Discrete Cosine Transform phase of Conventional JPEG with a combination of clustering and Run Length Encoding so as to handle redundant data in the image effectively. Simulation with respect to benchmark data indicates improved compression (42.5%) in relation to existing solutions. Image quality metrices such as PSNR, structural similarity have also been tested and it is observed that the proposed approach achieves significant compression ratio with negligible loss in visual quality.

On-the-Fly Parallel Processing IP-Core for Image Blur Detection, Compression, and Chaotic Encryption Based on FPGA

This paper presents a 3 in 1 standalone FPGA system which can perform color image blur detection in parallel with compression and encryption. Both blur detection and compression are based on the 3-level Haar wavelet transform, which is used as a common building block to save the resources. The compression is based on performing the hard thresholding scheme followed by the Run Length Encoding (RLE) technique. The encryption is based on the 128-bit Advanced Encryption Standard (AES), which is considered one of the most secure algorithms. Moreover, the modified Lorenz chaotic system is combined with the AES to perform the Cipher Block Chaining (CBC) mode. The proposed system is realized using HDL and implemented using Xilinx on XC5VLX50T FPGA. The system has utilized only 25% of the available slices. Furthermore, the system can achieve a throughput of 3.458 Gbps, which is suitable for real-time applications. To validate the compression performance, the system has been tested with all the standard 256×256 images. It is shown that depending on the amount of details in the image, the system can achieve 30dB PSNR at compression ratios in the range of (0.08-0.38). The proposed system can be integrated with digital cameras to process the captured images on-the-fly prior to transmission or storage. Based on the application, the blurred images can be either marked for future enhancement or simply filtered out.

Improvement of data compression technology for power dispatching based on run length encoding

Abstract Data compression is of great significance to the storage of power dispatch data. The compressed data not only saves a lot of space, but also improves the speed of data retrieval. Therefore, this paper proposes an improved algorithm based on run length encoding to compress power dispatch data. In the case of high data similarity, run length coding and dictionary coding have good compression effects, so this article classifies power dispatch data, and uses an improved run length coding algorithm to compress data with high similarity. For similarity Low data is compressed using the Lz4 algorithm that comes with HBase, and a Base-128 Varints encoding the number of occurrences of elements in the run-length encoding is proposed to further improve the compression rate of similar data. This improved algorithm is proven in experiments In some scenarios, the compression rate is indeed improved.

Parallel lossless HSI compression based on RLS filter

The recent advancement in the field of electronics has led to development of sensors that capture the image of an area or object in spectral-domain along with spatial information. Due to continuity of spectral domain in hyperspectral images, it is difficult to store, process, analyze or transmit the critical information contained in it. Prediction based compression technique is used to reduce this size by a certain level. It predicts the value of a pixel with some error from previous pixels using a filter and finally, encode that error using variable length encoder. The execution time taken by this technique is very high which can be reduced by high performance computing. In this paper, we designed a mechanism to use high-performance computing techniques in the execution of prediction based image compression algorithms. The average execution time of the RLS-filter based compression algorithm is reduced significantly (by a factor of 29 using 2 nodes with 28 cores each, on PARAM SHIVAY supercomputer) with the proposed technique.

Merge and Split approach incolor image Steganography using Run Length Encoding and LSB Techniques

The purpose of steganography is to communicate secret messages between the sender and intended recipient in such a way that no one suspects the very existence of the message. The techniques aim to protect the secret information from third parties by embedding them into other information such as text, audio signals, images, and video frames. In this paper we propose a novel approach of hiding secret messages in multiple images (a cover image)using run length encoding and LSB techniques and communicate the message to intended person over the communication channel by transmitting individual images.Experiments are performed on a set of color images and performance of the proposed system is presented.

A Signal Complexity-Based Approach for AM–FM Signal Modes Counting

Frequency modulated signals appear in many applied disciplines, including geology, communication, biology and acoustics. They are naturally multicomponent, i.e., they consist of multiple waveforms, with specific time-dependent frequency (instantaneous frequency). In most practical applications, the number of modes—which is unknown—is needed for correctly analyzing a signal; for instance for separating each individual component and for estimating its instantaneous frequency. Detecting the number of components is a challenging problem, especially in the case of interfering modes. The Rényi Entropy-based approach has proven to be suitable for signal modes counting, but it is limited to well separated components. This paper addresses this issue by introducing a new notion of signal complexity. Specifically, the spectrogram of a multicomponent signal is seen as a non-stationary process where interference alternates with non-interference. Complexity concerning the transition between consecutive spectrogram sections is evaluated by means of a modified Run Length Encoding. Based on a spectrogram time-frequency evolution law, complexity variations are studied for accurately estimating the number of components. The presented method is suitable for multicomponent signals with non-separable modes, as well as time-varying amplitudes, showing robustness to noise.

SC-EEDC: Similarity Based Clustering for Energy Efficient Data Collection in WSN

Wireless sensor networks (WSN) are widely used in various situations. Energy saving is one of the most important issues because of the limited power. Communication is the mainly energy consuming part of sensor nodes. Reducing the size of transmitted data can conserve the energy of nodes. Spatial and temporal correlation is ubiquitous in wireless sensor networks. By exploiting spatial and temporal correlation, only a subset of data need to be transmitted and the rest of the data can be estimated. Data aware clustering is an effective way to exploit spatial correlation among sensor nodes. In our SC-EEDC framework, fuzzy ART artificial neural network is used to measure the shape similarity among data sequences and the magnitude similarity is estimated by a magnitude similarity model. And two corresponding estimation algorithm are proposed. We propose Weighting based K-means clustering algorithm (WK-means), which considers multiple clustering factors in addition to the data similarity. The K-means algorithm structure is introduced in clustering algorithm to search the more energy-saving clustering topology. Anchor node based data collection strategy is proposed to measure the spatial correlation in real time and suppress the transmission of spatial redundant data at source node. Sleeping scheduling is introduced to dynamically adjust the spatial sampling rate and the temporal redundancy is reduced using Length Encoding. The cluster maintenance scheme keeps the cluster structure’s efficiency over the network lifetime. Simulation results show that our SC-EEDC achieves significant data reduction without affecting the accuracy of collected data, reduces the energy consumption in each round of data collection and effectively prolongs the network lifetime.

Geometrical Fusion Based on Chain Code Representation

In this paper, a new geometrical image fusion method introduced by using the representation of chain code for the image objects that been matched among the images to be fused. The input images are separated into number of objects. A matching method is applied to extract the matching objects from the overall objects that included in the origin images. Then the chain code string is calculated for each one. The run length encoding is applied on that code strings to reduce the space that required. The image resulted from the geometrical fusion is more informative which contain the more important features those included in the origin images.

A new technique for data compression

Data compression has become more important than ever, due to the increasing demand for internet use and the exchange of a huge amount of images, videos, audio and documents as well as the growing demand for electronic archiving by government departments that produce thousands of documents per day. In this paper, the proposed technique for document compression will be presented.The proposed technique is a lossless and completed technique it is consists of two parts the compression part and decompression part. The compression part contains of some basic stages such as: pre-processing, blocks processing, run length encoding(RLE), replace maximum values by unused values, minimize levels, delta encoding, compression of ones values, encryption. After the encryption process is complete, the outputs are stored in two separate binary files with the extension of bmp;one of them is the header file and is considered a key for the second file which contains the compressed data. This technique applied on twenty documents and compared with other methods compression such as RLE, jpeg, tiff and png. The experimental results showed that the proposed technique gives a higher compression ratio than the rest of the methods.

Metaheuristic-based vector quantization approach: a new paradigm for neural network-based video compression

Video compression has great significance in the communication of motion pictures. Video compression techniques try to remove the different types of redundancy within or between video sequences. In the temporal domain, the video compression techniques remove the redundancies between the highly correlated consequence frames of the video. In the spatial domain, the video compression techniques remove the redundancies between the highly correlated consequence pixels (samples) in the same frame. Evolving neural-networks based video coding research efforts are focused on improving existing video codecs by performing better predictions that are incorporated within the same codec framework or holistic methods of end-to-end video compression schemes. Current neural network-based video compression adapts static codebook to achieve compression that leads to learning inability from new samples. This paper proposes a modified video compression model that adapts the genetic algorithm to build an optimal codebook for adaptive vector quantization that is used as an activation function inside the neural network’s hidden layer. Background subtraction algorithm is employed to extract motion objects within frames to generate the context-based initial codebook. Furthermore, Differential Pulse Code Modulation (DPCM) is utilized for lossless compression of significant wavelet coefficients; whereas low energy coefficients are lossy compressed using Learning Vector Quantization (LVQ) neural networks. Finally, Run Length Encoding (RLE) is engaged to encode the quantized coefficients to achieve a higher compression ratio. Experiments have proven the system’s ability to achieve higher compression ratio with acceptable efficiency measured by PSNR.

A scalable data transmission scheme for implantable optogenetic visual prostheses

Objective. This work described a video information processing scheme for optogenetic forms of visual cortical prosthetics. Approach. The architecture is designed to perform a processing sequence: Initially simplifying the scene, followed by a pragmatic visual encoding scheme which assumes that initially optical stimulation will be stimulating bulk neural tissue rather than driving individual phosphenes. We demonstrate an optical encoder, combined with what we called a zero-run length encoding (zRLE) video compression and decompression scheme—to wirelessly transfer information to an implantable unit in an efficient manner. In the final step, we have incorporated an even power distribution driver to prevent excessive power fluctuations in the optogenetic driving. Significance. The key novelty in this work centres on the completeness of the scheme, the new zRLE compression algorithm and our even power distributor. Main results. Furthermore, although the paper focusses on the algorithm, we confirm that it can be implemented on real time portable processing hardware which we will use for our visual prosthetics.

Combination of Cryptography Algorithm Knapsack and Run Length Enconding (RLE) Compression in Treatment of Text File

The Knapsack algorithm cryptographic method is asymmetric cryptography in which the encryption key is different from the decryption key. In addition to text file security issues, the issue of the size of a text file is also a consideration. Large text files can be compressed by doing the compression process. Run Length Encoding (RLE) algorithm is an algorithm that reduces the size of a text file, if the text has a lot of repetition of characters. The combination of Knapsack and RLE algorithms can guarantee Text files cannot be seen by unauthorized users and can guarantee text files can be stored in low capacity media files. In this study, the authors made a combination of knapsack and RLE algorithm in text files. In the Knapsack algorithm there will be an increase in the size of the text file, this can be seen in the example of a case where the size of the plaintext (the original message) is 9 bytes, then after the encryption process the text file size becomes 7 bytes. Because of that the use of a combination of encryption and data compression is better because the file becomes smaller than the combination of data compression and encryption. Plaintext that has a lot of repetition of characters will be well compressed.

Nonsubsampled Contourlet Transform based Video Compression using Huffman and Run Length Encoding for Multimedia Applications

Nonsubsampled Contourlet Transform based Video Compression using Huffman and Run Length Encoding for Multimedia Applications

IoT-based Production Output Monitoring System for Plastic Packaging Manufacturing Process

This paper discusses about the development of an IoT-based production output monitoring system in the production line of Chiga Light Industries Sdn. Bhd. which is a plastic packaging manufacturing company. Although the existing company Enterprise Resource Planning (ERP) system provides a platform for production manager and officers to monitor as well as collect the length of plastic film produced on a printing machine, only the total amount of finished goods in kilograms (kg) is recorded. However, the total length of plastic produced, and time taken to finish one tubing are needed for machine analysis and production planning. Therefore, a length encoder is used to detect the length of plastic film produced and connected to a counter meter to display the present value of plastic produced as well as the setting value for one roll of plastic. Besides, an Arduino board is used to read data from the counter meter while an ESP8266-01 Wi-Fi module is used to send the data to ThingSpeakTM cloud for storing. ThingSpeakTM analysed and visualised the collected present and setting values of the counter meter on a private channel. The duration for uploading a data is set to one hour. From the developed monitoring system, production manager and officers could monitor machine performance, operators working performance and collect the length of plastic produced on a printing machine. Besides, the total length of plastic in a roll could also be detected to ensure customer requirement is achieved.

Lossless Compression of Medical Images based on the Differential Probability of Images

Lossless compression is crucial in the remote transmission of large-scale medical image and the retainment of complete medical diagnostic information. The lossless compression method of medical image based on differential probability of image is proposed in this study. The medical image with DICOM format was decorrelated by the differential method, and the difference matrix was optimally coded by the Huffman coding method to obtain the optimal compression effect. Experimental results obtained using the new method were compared with those using Lempel–Ziv–Welch, modified run–length encoding, and block–bit allocation methods to verify its effectiveness. For 2-D medical images, the lossless compression effect of the proposed method is the best when the object region is more than 20% of the image. For 3-D medical images, the proposed method has the highest compression ratio among the control methods. The proposed method can be directly used for lossless compression of DICOM images.