A hardware implementation of a run length encoding compression algorithm with parallel inputs

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.

Improvement of data compression technology for power dispatching based on 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.

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.

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.

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.

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

Multimedia is highly competitive world, one of the properties that is reflected is speed of download and upload of multimedia elements: text, sound, pictures, animation. This paper presents CRUSH algorithm which is a lossless compression algorithm. CRUSH algorithm can be used to compress files. CRUSH method is fast and simple with time complexity O(n) where n is the number of elements being compressed.Furthermore, compressed file is independent from algorithm and unnecessary data structures. As the paper will show comparison with other compression algorithms like Shannon–Fano code, Huffman coding, Run Length Encoding, Arithmetic Coding, Lempel-Ziv-Welch (LZW), Run Length Encoding (RLE), Burrows-Wheeler Transform.Move-to-Front (MTF) Transform, Haar, wavelet tree, Delta Encoding, Rice &Golomb Coding, Tunstall coding, DEFLATE algorithm, Run-Length Golomb-Rice (RLGR).

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.

Multimedia is highly competitive world, one of the properties that is reflected is speed of download and upload of multimedia elements: text, sound, pictures, animation. This paper presents CRUSH algorithm which is a lossless compression algorithm. CRUSH algorithm can be used to compress files. CRUSH method is fast and simple with time complexity O(n) where n is the number of elements being compressed.Furthermore, compressed file is independent from algorithm and unnecessary data structures. As the paper will show comparison with other compression algorithms like Shannon–Fano code, Huffman coding, Run Length Encoding, Arithmetic Coding, Lempel-Ziv-Welch (LZW), Run Length Encoding (RLE), Burrows-Wheeler Transform.Move-to-Front (MTF) Transform, Haar, wavelet tree, Delta Encoding, Rice &Golomb Coding, Tunstall coding, DEFLATE algorithm, Run-Length Golomb-Rice (RLGR).

It is proposed that an efficient and fast image compression scheme based on all level curvelet coefficients with SPIHT (Set Partitioning in Hierarchical Trees). For images with textures, the high frequency wavelet coefficients are likely to become significant after several code passes of SPIHT, which degrades the coding performance. The basic flaw that wavelet transform exhibits, is its inability to represent edge discontinuities along curves. Less number of coefficients is required in compression process but several wavelet coefficients are used to reconstruct edges properly along the curves. This is due to the reason that in a map of large wavelet coefficients, edges repeat at scale after scale. There was a need of a transform that handles two dimensional singularities along the curves sparsely. This led to the birth of new multi-resolution curvelet transform. Curvelet basis elements possess wavelet basis function qualities but these also oriented at a variety of directions and so represent edge discontinuities and other singularities well than wavelet transform. In the proposed method, a curvelet transform of an image is taken and selected all level curvelet coefficients information. Then, it has been applied with SPIHT encoding. The SPIHT encoded output is stored as a bit stream. Run Length Encoding has been applied to the bit stream. It produces further compressed bit stream. Then run length decoding and SPIHT decoding have been applied and inverse curvelet transform has been taken to reconstruct the image. Images of different sizes have been tested in the experiment and the results are listed in the tables.

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.

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.

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.