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

Compression purpose to reduce the redundancy data as small as possible and speed up the data transmission process. To solve the size problem in saving data and transmission process, we use Run Length Encoding and Fibonacci Code algorithm to do compression process. Run Length Encoding and Fibonacci Code algorithm is a type of lossless data compression used in this research, which performance will be measured by comparison parameters of the Compression Ratio (CR), Redundancy (RD), Space Saving (SS) and Compression Time. The compression process is only done on image files with Bitmap format (*.bmp) and encode using Run Length Encoding or Fibonacci Code, then perform the compression process. The final result of the compression is file with extension *.rle or *.fib which contains compressed information that can be decompressed back. The output of the decompression result is an original image file that is stored with *.bmp extension. Fibonacci algorithm will give a better compressed size on image color, while in a grayscale image Run Length Encoding will give a better compressed size. Based on the results of research at two different types of images, each algorithm has its own advantages. Fibonacci Code algorithm is better for color image compression while Run-Length algorithm Encoding is better for grayscale image compression.

The exponential growth in remote sensing, coupled with advancements in integrated circuits (IC) design and fabrication technology for communication, has prompted the progress of Wireless Sensor Networks (WSN). WSN comprises of sensor nodes and hubs fit for detecting, processing, and communicating remotely. Sensor nodes have limited resources such as memory, energy and computation capabilities restricting their ability to process large volume of data that is generated. Compressing the data before transmission will help alleviate the problem. Many data compression methods have been proposed but mainly for image processing and a vast majority of them are not pertinent on sensor nodes because of memory impediment, energy utilization and handling speed. To overcome this issue, authors in this research have chosen Run Length Encoding (RLE) and Adaptive Huffman Encoding (AHE) data compression techniques as they can be executed on sensor nodes. Both RLE and AHE are capable of balancing compression ratio and energy utilization. In this paper, a hybrid method comprising RLE and AHE, named as H-RLEAHE, is proposed and further investigated for sensor nodes. In order to verify the efficacy of the data compression algorithms, simulations were run, and the results compared with the compression techniques employing RLE, AHE, H-RLEAHE, and without the use of any compression approach for five distinct scenarios. The results demonstrate the RLE’s efficiency, as it surpasses alternative data compression methods in terms of energy efficiency, network speed, packet delivery rate, and residual energy throughout all iterations.

Fluid Data Compression and ROI Detection Using Run Length Method

Run length encoding can be found in numerous applications such as data transfer or image storing (Sayood, 2002). It is a well known, easy and efficient compression method based on the assumption of long data sequences without the change of content. These sequences can be described by their position and length of appearance. Implementations using dedicated logic are optimised for parallel data processing. Here, images are transferred in blocks of multiple pixels in parallel. A compression of these streams into a run length code requires an encoder with a parallel input. This run length encoder has to compress the sequence at a minimum of clock cycles to avoid long inhibit intervals at the input. This paper describes a hardware algorithm performing a high performance run length encoding for binary images using a parallel input.

The compression performance of grammar-based codes is revisited from a new perspective. Previously, the compression performance of grammar-based codes was evaluated against that of the best arithmetic coding algorithm with finite contexts. In this correspondence, we first define semifinite-state sources and finite-order semi-Markov sources. Based on the definitions of semifinite-state sources and finite-order semi-Markov sources, and the idea of run-length encoding (RLE), we then extend traditional RLE algorithms to context-based RLE algorithms: RLE algorithms with k contexts and RLE algorithms of order k, where k is a nonnegative integer. For each individual sequence x, let r/sup *//sub sr,k/(x) and r/sup *//sub sr|k/(x) be the best compression rate given by RLE algorithms with k contexts and by RLE algorithms of order k, respectively. It is proved that for any x, r/sup *//sub sr,k/ is no greater than the best compression rate among all arithmetic coding algorithms with k contexts. Furthermore, it is shown that there exist stationary, ergodic semi-Markov sources for which the best RLE algorithms without any context outperform the best arithmetic coding algorithms with any finite number of contexts. Finally, we show that the worst case redundancies of grammar-based codes against r/sup *//sub sr,k/(x) and r/sup *//sub sr|k/(x) among all length- n individual sequences x from a finite alphabet are upper-bounded by d/sub 1/loglogn/logn and d/sub 2/loglogn/logn, respectively, where d/sub 1/ and d/sub 2/ are constants. This redundancy result is stronger than all previous corresponding results.

The Run Length Encoding (RLE) algorithm substitutes long runs of identical symbols with the value of that symbol followed by the binary representation of the frequency of occurrences of that value. This lossless technique is effective for encoding images where many consecutive pixels have similar intensity values. One of the major problems of RLE for encoding runs of bits is that the encoded runs have their lengths represented as a fixed number of bits in order to simplify decoding. The number of bits assigned is equal to the number required to encode the maximum length run, which results in the addition of padding bits on runs whose lengths do not require as many bits for representation as the maximum length run. Due to this, the encoded output sometimes exceeds the size of the original input, especially for input data where in the runs can have a wide range of sizes. In this paper, we propose VaFLE, a general-purpose lossless data compression algorithm, where the number of bits allocated for representing the length of a given run is a function of the length of the run itself. The total size of an encoded run is independent of the maximum run length of the input data. In order to exploit the inherent data parallelism of RLE, VaFLE was also implemented in a multithreaded OpenMP environment. Our algorithm guarantees better compression rates of upto 3X more than standard RLE. The parallelized algorithm attains a speedup as high as 5X in grayscale and 4X in color images compared to the RLE approach.

Map exporting plays a crucial role in the realization of spatial data visualization. However, this process will be slow and high memory required when dealing with high precision map or enormous data. A fast cartography approach based on run length coding is proposed in this paper. The main idea is to express map symbol using run length set rather than dot matrix, thus the disposal of various form of symbol, linetype or label in cartography pattern can achieve through the operation of run length set, which will largely cut down redundant information emerged in the process of map rendering. The core of our scheme is geographical element expression with run-length set and run length overlay calculation of multiple layers, specifically to explore how map symbol and cartographic products are created by using run-length code. A detailed description about design of dot-form symbol is presented in this paper. We argue that dot-form symbol is a block of different size run-length segment, which records the complete color attribute. The design of complex symbol is built through the operation of fundamental run-length block, which have been implemented in previous research. In addition, other styles of map symbol can create in the same pattern. Based on this representation, it is actually plug in a block of run-length when insert a symbol into map layer. Thus, the map document can be taken as a set of run-length in different layers. While the map product is the final superposition effect of layers, combination of run-length block in each layer is necessary for the ultimate cartography pattern. The procedure is as follows: (1) progressive scan cartography space; (2) compare each layer with uppermost layer to determine merge or resolve run-length blocks to form ultimate run-length set, which is executed by adding operation of run-length for layers. The rules of disposing run-length block or not is discussed in detail. Thereby, all cartography features are contained in the final run-length set and map export will be realized in a short time for smaller memory space. The advantages of the scheme in speed and memory consumption are illustrated by comparative analysis with existing cartography system. Simultaneously, performance analysis concerning thematic map series is made at the end of this paper.

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.

Data Compression can save some storage space and accelerate data transfer. Among many compression algorithm, Run Length Encoding (RLE) is a simple and fast algorithm. RLE can be used to compress many types of data. However, RLE is not very effective for image lossless compression because there are many little differences between neighboring pixels. This research proposes a new lossless compression algorithm called YRL that improve RLE using the idea of Relative Encoding. YRL can treat the value of neighboring pixels as the same value by saving those little differences / relative value separately. The test done by using various standard image test shows that YRL have an average compression ratio of 75.805% for 24-bit bitmap and 82.237% for 8-bit bitmap while RLE have an average compression ratio of 100.847% for 24-bit bitmap and 97.713% for 8-bit bitmap.

The objective of this paper is to prove the significance of the optimized run length coding algorithm for biomedical imaging technology and open source the idea behind the optimized algorithm in a comprehensive way. An optimized scheme for entropy encoding part of JPEG image compression by modifying the run length encoding method has been provided by the authors for a Space Research Program at Institute of Space technology (IST). The same has been observed to produce a large amount of saving in terms of memory required for Biomedical compressed images. In JPEG (Joint Photographic Experts Group) image compression algorithm run length coding performs the actual compression by removing the redundancy from transformed and quantized image data. Using the fact that the preceding processes of run length coding produces a large number of zeros the original run length coding uses an ordered pair (a, b), where `a' is the length of consecutive zeros preceding the ASCII character `b'. The proposed run length encoding scheme removes the unintended redundancy by using an ordered pair only when a zero occurs. The proposed encoding scheme does not alter the PSNR value for the algorithm. Using Matlab simulation, the proposed scheme has been tested on various biomedical images over a range of quantization (quality) factor and the results confirmed the effectiveness of the new run length encoding scheme in reducing the run length encoded data size and processing time delay.

This work aims to present an optimized scheme for entropy encoding part of JPEG image compression by modifying the run length encoding method. In JPEG (Joint Photographic Experts Group) image compression algorithm run length coding performs the actual compression by removing the redundancy from transformed and quantized image data. Using the fact that the preceding processes of run length coding, in JPEG compression algorithm, produces a large number of zeros, the original run length coding uses an ordered pair (a,b), where ‘a’ is the length of consecutive zeros preceding the ASCII character ‘b’. It has been observed the occurrence of consecutive ASCII characters at the input introduce another redundancy to the encoded data, i.e. ‘a = 0’ before each consecutive ASCII character ‘b’. The proposed run length encoding scheme removes the unintended redundancy by using an ordered pair only when a zero occurs and using the same EOB (End of Block) parameter at the end of each block. The proposed encoding scheme does not alter the PSNR value for the algorithm. Using Matlab simulation, the proposed scheme has been tested on various images over a range of quantization (quality) factor and the results confirmed the effectiveness of the new run length encoding scheme in reducing the run length encoded data.

Binary morphology on large images is compute intensive, in particular for large structuring elements. Run-length encoding is a compact and space-saving technique for representing images. This paper describes how to implement binary morphology directly on run-length encoded binary images for rectangular structuring elements. In addition, it describes efficient algorithm for transposing and rotating run-length encoded images. The paper evaluates and compares run length morphologial processing on page images from the UW3 database with an efficient and mature bit blit-based implementation and shows that the run length approach is several times faster than bit blit-based implementations for large images and masks. The experiments also show that complexity decreases for larger mask sizes. The paper also demonstrates running times on a simple morphology-based layout analysis algorithm on the UW3 database and shows that replacing bit blit morphology with run length based morphology speeds up performance approximately two-fold.

This study proposes a data hiding method based on run length encoding. This proposed method uses the location of accumulated run length values, where the cover data run length are compared with the secret data run length. The run length matching (RLM) method uses the run length table which is constructed from the cover and secret data. The experimental results demonstrated that the RLM has advantages with respect to different types of data and run length encoding value match.Keywordssteganographydata hidingrun length encodingembedded data

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.

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.