English

This paper presents a lossless audio coding using Burrows-Wheeler Transform (BWT) and a combination of a Move-To-Front coding (MTF) and Run Length Encoding (RLE). Audio signals used are assumed to be of floating point values. The BWT is applied to this floating point values to get the transformed coefficients; and then these resulting coefficients are converted using the Move-to-Front coding to coefficients can be better compressed and then these resulting coefficients are compressed using a combination of the Run Length Encoding, and entropy coding. Two entropy coding are used which are Arithmetic and Huffman coding. Simulation results show that the proposed lossless audio coding method outperforms other lossless audio coding methods; using only Burrows-Wheeler Transform method, using combined Burrows-Wheeler Transform and Move-to-Front coding method, and using combined Burrows-Wheeler Transform and Run Length Encoding method.

One method of using data compression is by using a method called Run Length Encoding (RLE), especially image data. The RLE method is one of the simplest lossless types of data compression schemes and is based on the simple principle of data encoding. The RLE method is very suitable for compressing data containing repetitive characters such as simple graphic images. The compressed data are 28 RGB (Red, Green, Blue) images and 28 grayscale images in jpg, png, bmp, and tiff formats, respectively. Image data is compressed with an encoder and decoder program using the RLE algorithm in the matlab application. The RLE method is said to be effective in compressing image data if the compression ratio is less than 100% because it has a lot of color repetition in the pixels. The RLE method is said to be ineffective if the compression ratio is more than 100% because it has a little repetition of colors in the pixels. Of the 28 RGB images tested, it was found that the RLE method was effective on 1 image and not effective on 27 images. For the 28 grayscale images tested, it was found that the RLE method was effective on 6 images and not effective on 22 images

This paper presents a dataset-level evaluation of six lossless compression and data transformation techniques applied to visual-cryptographic (VC) shares derived from QR codes. We processed 40,000 QR samples, comprising 10,000 QR images (Versions 1-4, 2,500 per version), 10,000 QR images (Versions 1-10, 1,000 per version across ten application domains), and 20,000 augmented QR images (with noise, rotation, shear, cropping, and brightness variations). Each QR image is converted to VC share (share1), flattened to a bitstream, and evaluated under traditional compression techniques such as Run Length Encoding (RLE), Huffman Coding, Lempel Ziv-Welch (LZW), and data transformation techniques such as Binary-to-Integer, Base64 Encoding, and (BWT + MTF + Huffman Coding) Burrows Wheeler Transform (BWT), Move-To-Front (MTF), and Huffman Coding as a combined pipeline. Our experiments report Shannon entropy, compressed character count, compressed character count percentage, compression time, decompression time, memory usage, peak memory, lossless fidelity, metadata, payload size, storage size, and compression ratio. Empirical results show near-maximal entropy in QR-derived VC data (∼0.99), providing constraints on compression performance for traditional algorithms. Base64 consistently yields the best compression performance across both clean and augmented datasets, with an average compression rate of 499%. This work contributes a reproducible pipeline, a generalized dataset, and a benchmark reference for compression research on a highly randomized binary dataset.

In this paper two new highly efficient hybrid lossless audio coding techniques based on the Burrows-Wheeler Transform (BWT) and the distance transform (DT) are presented. In both techniques, floating point samples of the audio signal are first applied to the BWT and the resulting coefficients are then applied to the DT to obtain more suitable coefficients for the next step of lossless compression. In the first proposed method, two entropy-based lossless compression methods are considered, namely Arithmetic coding and Huffman coding. On the other hand, in the second proposed method the entropy coding is first preceded by Run Length Encoding (RLE).

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.

Summary form only given. A new stage for the Burrows-Wheeler compression algorithm (BWCA) is presented, called incremental frequency count (IFC), which, together with a run length encoding (RLE) stage, is located between the Burrows-Wheeler transform (BWT) and the entropy coding (EC) stage of the algorithm. The IFC stage offers a high throughput similar to a move-to-front (MTF) stage combined with good compression rates, similar to the strong, but slow, weighted frequency count (WFC) stage. A BWCA based on an IFC stage and a corresponding RLE stage achieves compression times twice as fast as one based on a WFC stage, while the compression rates are under the top values of the BWT based compression algorithms.

In the sort-last-sparse parallel volume rendering system on distributed memory multicomputers, as the number of processors increases, in the rendering phase, we can get a good speedup because each processor renders images locally without communicating with other processors. However, in the compositing phase, a processor has to exchange local images with other processors. When the number of processors is over a threshold, the image compositing time becomes a bottleneck. In this paper, we proposed three compositing methods, the binary-swap with bounding rectangle method, the binary-swap with run-length encoding and static load-balancing method, and the binary-swap with bounding rectangle and run-length encoding method, to efficiently reduce the compositing time in the sort-last-sparse parallel volume rendering system on distributed memory multicomputers. The proposed methods were implemented on an SP2 parallel machine along with the binary-swap compositing method. The experimental results show that the binary-swap with bounding rectangle and run-length encoding method has the best performance among the four methods.

An efficient and secure compression technique for data protection using burrows-wheeler transform algorithm

Data compression is needed so that the need for storage media and data transfer time becomes more efficient. This study compressed image data using the Run-Length Encoding (RLE) method. The test data is the original image (gray scale) and the image results of improving image quality (image enhancement) using contrast modification. Modification of contrast using contrast stretching methods. Through experiments wanting to know the extent to which the RLE method works less effectively for images with complex color intensity. The image of contrast modification results has a more complex color intensity or more varied pixel value. Obtained the number of pairs (p, q) RLE in the image of contrast modification results is less than the original image, with the pair ratio (p, q) RLE ranges from 0.64% to 1.59%. Although this image has a more varied pixel value than its original image, it can produce a compression ratio of the number of pairs (p, q) RLE.

The Burrows-Wheeler Transformation (BWT) is a text transformation algorithm originally designed to improve the coherence in text data. This coherence can be exploited by compression algorithms such as run-length encoding or arithmetic coding. However, there is still a debate on its performance on images. Motivated by a theoretical analysis of the performance of BWT and MTF, we perform a detailed empirical study on the role of MTF in compressing images with the BWT. This research studies the compression performance of BWT on digital images using different predictors and context partitions. The major interest of the research is in finding efficient ways to make BWT suitable for lossless image compression.;This research studied three different approaches to improve the compression of image data by BWT. First, the idea of preprocessing the image data before sending it to the BWT compression scheme is studied by using different mapping and prediction schemes. Second, different variations of MTF were investigated to see which one works best for Image compression with BWT. Third, the concept of context partitioning for BWT output before it is forwarded to the next stage in the compression scheme.;For lossless image compression, this thesis proposes the removal of the MTF stage from the BWT compression pipeline and the usage of context partitioning method. The compression performance is further improved by using MED predictor on the image data along with the 8-bit mapping of the prediction residuals before it is processed by BWT.;This thesis proposes two schemes for BWT-based image coding, namely BLIC and BLICx, the later being based on the context-ordering property of the BWT. Our methods outperformed other text compression algorithms such as PPM, GZIP, direct BWT, and WinZip in compressing images. Final results showed that our methods performed better than the state of the art lossless image compression algorithms, such as JPEG-LS, JPEG2000, CALIC, EDP and PPAM on the natural images.

The Burrows–Wheeler Transform (BWT) is a string transformation technique widely used in areas such as bioinformatics and file compression. Many applications combine a run-length encoding (RLE) with the BWT in a way which preserves the ability to query the compressed data efficiently. However, these methods may not take full advantage of the compressibility of the BWT as they do not modify the alphabet ordering for the sorting step embedded in computing the BWT. Indeed, any such alteration of the alphabet ordering can have a considerable impact on the output of the BWT, in particular on the number of runs. For an alphabet Σ containing σ characters, the space of all alphabet orderings is of size σ!. While for small alphabets an exhaustive investigation is possible, finding the optimal ordering for larger alphabets is not feasible. Therefore, there is a need for a more informed search strategy than brute-force sampling the entire space, which motivates a new heuristic approach. In this paper, we explore the non-trivial cases for the problem of minimizing the size of a run-length encoded BWT (RLBWT) via selecting a new ordering for the alphabet. We show that random sampling of the space of alphabet orderings usually gives sub-optimal orderings for compression and that a local search strategy can provide a large improvement in relatively few steps. We also inspect a selection of initial alphabet orderings, including ASCII, letter appearance, and letter frequency. While this alphabet ordering problem is computationally hard we demonstrate gain in compressibility.

Image compression plays a crucial role in optimising storage and transmission efficiency. This paper evaluates the performance of Run-Length Encoding (RLE), Huffman Coding, and Lempel-Ziv-Welch (LZW) algorithms for compressing grayscale PNG and JPG images. The study analyses their effectiveness using compression ratio, bits per pixel, and compression time as key performance metrics. Results indicate that LZW achieved the highest compression ratio, ranging from 1.0113 to 2.4020, making it the most efficient for file size reduction. RLE performed moderately, with compression ratios between 0.5456 and 2.3895, while Huffman Coding exhibited the lowest ratios, ranging from 0.2646 to 1.0680. In terms of bits per pixel, LZW recorded the lowest values, highlighting its ability to reduce data while preserving image quality. Compression time analysis revealed that RLE was the fastest, with processing times between 0.0019 and 0.0468 seconds, making it suitable for real-time applications. LZW and Huffman Coding demonstrated a trade-off between compression efficiency and speed. These findings establish LZW as the most effective algorithm for high compression with minimal quality loss, while RLE remains the best option for speed-critical applications.

A research in computational biology relies heavily on storage and manipulation of vast amount of data. Deoxyribonucleic Acid (DNA) sequences make up the majority of biological data. It needs 3221225472 bytes of memory to save a single person's DNA data. In addition, its size is gradually growing overtime with more number of sequences being added to public databases. Only effective DNA sequence compression can assist to increase the storage medium's capacity and make the sequences too compact to transmit via a channel. In this paper, key principles of several existing lossless DNA sequence compression algorithms are studied and based on it, an Index Based DNA Sequence Compression Algorithm (IBDNASCA) is proposed. The proposed algorithm uses two files namely, work file and index file. Initially, Run Length Encoding (RLE) method is applied to the bases. Here, the drawback is that, bases with less number of repetitions occupy more storage space. To pull out this problem, an index file is used and bases of run length less than or equal to five are written in the index file. Further, a modified RLE method is applied in the work file, and an improved Huffman coding method is applied in the index file to further improve the storage capacity. Standard datasets taken from different sources of the GenBank database is chosen to experiment the algorithm. Keys factors, such as compression ratio, compression gain and time taken to compress and decompress the sequences are considered. Experimental results depict that the proposed algorithm outperforms and proves its efficiency when compared with the existing algorithms.

We present a technique for proving lower bounds on the compression ratio of algorithms which are based on the Burrows-Wheeler Transform (BWT). We study three well known BWT-based compressors: the original algorithm suggested by Burrows and Wheeler; BWT with distance coding; and BWT with run-length encoding. For each compressor, we show a Markov source such that for asymptotically-large text generated by the source, the compression ratio divided by the entropy of the source is a constant greater than 1. This constant is 2 - e, 1.26, and 1.29, for each of the three compressors respectively. Our technique is robust, and can be used to prove similar claims for most BWT-based compressors (with a few notable exceptions). This stands in contrast to statistical compressors and Lempel-Ziv-style dictionary compressors, which are long known to be optimal, in the sense that for any Markov source, the compression ratio divided by the entropy of the source asymptotically tends to 1. We experimentally corroborate our theoretical bounds. Furthermore, we compare BWT-based compressors to other compressors and show that for realistic Markov sources they indeed perform bad and often worse than other compressors. This is in contrast with the well known fact that on English text, BWT-based compressors are superior to many other types of compressors.

Medical imaging is a presentment of human organ parts. Medical imaging is saved on a film; therefore, it needs a big saving quota. Compressing is a process to remove redundancy from a piece of information without reducing its quality. This study recommended compressed medical image with DWT (Discrete Wavelet Transform) with adaptive threshold added and entropy copying with the Run Length Encoding (RLE) coding. This study is comparing several parameters, such as compressed ratio and compressed image file size, and PSNR (Peak Signal to Noise Ratio) for analyzing the quality of reconstructive image. The study showed that the comparison of rate, compressed ratio, and PSNR tracing of Haar and Daubechies doesn’t have a significant difference. Comparison of rate, compressed ratio, and PSNR tracing on the hard and soft threshold is the rate of the sold threshold is lower than the hard threshold. The optimal outcome of this study is to use a soft threshold.