EEG Lossless Signal Compression Based on Magnitude Classification and Run Length Encoding

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.

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.

Image compression is a very important useful technique for efficient transmission as well as storage of images. The demand for communication of multimedia data through the telecommunication network and accessing the multimedia data through internet by utilizing less bandwidth for communication is growing explosively. Basically the image data comprise of significant portion of multimedia data and they occupy maximum portion of communication bandwidth for multimedia communication. Therefore the development of efficient image compression technique is quite necessary. The 2D Haar wavelet transform along with Hard Thresholding and Run Length Encoding is one of the efficient proposed image compression technique. JPEG2000 is a standard image compression method capable of producing very high quality compressed images. Conventional Run Length Encoding(CRLE),Optimized Run Length Encoding(ORLE),Enhanced Run Length Encoding(ERLE) are different types of RLES applied on both proposed method of compression and JPEG2000. Conventional Run Length Encoding produces efficient result for proposed method whereas Enhanced Run Length Encoding produces efficient result in JPEG2000 compression. This is the novel approach that the authors have proposed for compression of image using compression ratio (CR) without losing the PSNR, quality of image using lesser bandwidth.

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.

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 paper presents results of compression using Run Length Encoding (RLE) scheme on speech signals of International Phonetic Alphabet (IPA) database. These speech signals are compressed with no noise being added then they are compressed after adding some noise to them. It observed that RLE scheme gives high Compression Ratio (CR) for noisy speech signal compared to non noisy speech signal. The performance of RLE scheme on standard speech signal as well as noisy speech signal is compared with compression by Huffman coding. The obtained results indicate that RLE scheme gives high CR compared to CR by Huffman coding.

Methods such as splat hierarchies, indexing and lists have been presented by the research society in recently years, to accelerate the splatting, a popular volume rendering algorithm. In this paper, a run length encoding (RLE) accelerated, pre-classification and pre-shade sheet buffer volume splatting algorithm is presented, which can enhance the speed of splatting without trading off image quality. This new technique saves rendering time by employing RLE mechanism so that only voxels of interest are processed in splatting. RLE based data structures are defined to exploit spatial coherence of volume and intermediate rendering images. A fast and accurate sheet buffer splatting method is used in the rendering process, which accelerates the splatting by traversing both the voxel scanline and the image scanline in sheet buffer simultaneously. Experiments practice proves that RLE can efficiently skip over transparent voxels in splatting and high speedup can be obtained by using the proposed algorithm.

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.

Splatting is a one of the most important object-order volume rendering algorithm. In this paper, a new run length encoding (RLE) accelerated, pre-classification and pre-shade volume splatting algorithm is presented, which enhances the speed of splatting without trading off image quality. This new technique saves rendering time by employing RLE mechanism so that only voxels of interest are processed in splatting. Data structures are defined to fully exploit spatial coherence of volume, including a slice scanline pointer array, a data pointer array, a scanline RLE array and an array storing all data of the non-transparent voxels. And a much fast and accurate sheet buffer splatting method is used in the rendering process, which accelerates the splatting by traversing both the voxel scanline and the image scanline in sheet buffer simultaneously. Experiments practice proves that RLE can efficiently skip over transparent voxels and high speedup can be obtained by using the proposed algorithm. Analysis on speed and memory cost of the algorithm is also conducted. This algorithm may be particularly used in situation where transfer function seldom changes.

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 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.

Two strings x and y are said to be Abelian equivalent if x is a permutation of y, or vice versa. If a string z satisfies \(z = xy\) with x and y being Abelian equivalent, then z is said to be an Abelian square. If a string w can be factorized into a sequence \(v_1, \ldots , v_s\) of strings such that \(v_1\), ..., \(v_{s-1}\) are all Abelian equivalent and \(v_s\) is a substring of a permutation of \(v_1\), then w is said to have a regular Abelian period (p, t) where \(p = |v_1|\) and \(t = |v_s|\). If a substring \(w_1[i..i+\ell -1]\) of a string \(w_1\) and a substring \(w_2[j..j+\ell -1]\) of another string \(w_2\) are Abelian equivalent, then the substrings are said to be a common Abelian factor of \(w_1\) and \(w_2\) and if the length \(\ell \) is the maximum of such then the substrings are said to be a longest common Abelian factor of \(w_1\) and \(w_2\). We propose efficient algorithms which compute these Abelian regularities using the run length encoding (RLE) of strings. For a given string w of length n whose RLE is of size m, we propose algorithms which compute all Abelian squares occurring in w in O(mn) time, and all regular Abelian periods of w in O(mn) time. For two given strings \(w_1\) and \(w_2\) of total length n and of total RLE size m, we propose an algorithm which computes all longest common Abelian factors in \(O(m^2n)\) time.

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.

In this paper we present a new lossless audio coding algorithm using Burrows-Wheeler Transform (BWT) and Run Length Encoding (RLE).Audio signals used are assumed to be of floating point values. The BWT is applied to the audio signals to get the transformed coefficients and then these resulting coefficients are better compressed using Run Length Encoding. Two entropy coding are used which are Run Length Encoding and Huffman coding. Proposed compression algorithm is experimented and analyzed for two different stereo type audio signals. Compression ratio and Bit rate for audio coding has been used as a comparison parameter for proposed audio coding algorithm. Experimental result shows that the lossless audio coding algorithm outperforms other lossless audio coding methods; using combined Burrows Wheeler Transform & Move to front coding method ,using combined Burrows Wheeler Transform and Huffman coding method, and using Burrows Wheeler Transform ,Move to front coding method & Run Length Encoding method.

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