VaFLE: Value Flag Length Encoding for Images in a Multithreaded Environment

ObjectiveData compression is a useful process in tele-monitoring applications, in which lesser number of bits are needed to represent the same data. In this work, a run-time lossless compression of single-channel Electrocardiogram (ECG) and Photoplethysmogram (PPG) signals is proposed, maintaining all dominant features. MethodsThe single-channel data are first quantized using optimal quantization level, so that fewer number of bits are needed to represent it, maintaining low quantization error. Then, second order delta encoding and run-length encoding (RLE) based data compression are proposed in this work. A new approach of using ‘buffer array’ along with RLE is also introduced, so that minimum bits are needed to store. ResultsThis algorithm was tested on various single-lead ECG and PPG signals available in Physionet. An average compression ratio (CR) was achieved of 6.52, 3.82, and 2.49 for 547 PTBDB ECG records, 48 MITDB ECG records, and 53 MIMIC-II PPG records, respectively. This algorithm was also performed on single-channel ECG, collected from 10 healthy volunteers using AD8232 ECG module, with 125 Hz sampling frequency and 10-bit data resolution, which resulted in average CR of 2.34. ConclusionThis algorithm was also performed on a smartphone device that provided user-friendly operation. The low computational complications and standalone operation of data collection, compression, and transmission encouraged its implementation for run-time operation. SignificanceA comparative study of the proposed work with previously published works proved this fact that this algorithm provided better performance in the area of run-time patient health monitoring applications.

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).

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).

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.

Lossless data compression algorithms can use statistical redundancy to represent data using a fewer number of bits in comparison to the original uncompressed data. Run-Length Encoding (RLE) is one of the simplest lossless compression algorithms in terms of understanding its principles and software implementation, as well as in terms of temporal and spatial complexity. If this principle is applied to individual bits of original uncompressed data without respecting the byte boundaries, this approach is referred to as bit-level Run-Length Encoding. Lightweight algorithm for lossless data compression proposed in this paper optimizes bit-level RLE data compression, uses special encoding of repeating data blocks, and, if necessary, combines it with delta data transformation or representation of data in its original form intending to increase compression efficiency compared to a conventional bit-level RLE approach. The advantage of the algorithm proposed in this paper is in its low time and memory consumption which are basic features of RLE, along with the simultaneous increase of compression ratio, compared to the classical bit-level RLE approach.

Image compression is currently a prominent topic for both military and commercial researchers. Due to rapid growth of digital media and the subsequent need for reduced storage and to transmit the image in an effective manner Image compression is needed. Image compression attempts to reduce the number of bits required to digitally represent an image while maintaining its perceived visual quality. This study concentrates on the lossless compression of image using approximate matching technique and run length encoding. The performance of this method is compared with the available jpeg compression technique over a wide number of images, showing good agreements.

The limited available storage and bandwidth required for successful transmission of large images make image compression a key component in digital image transmission. Digital image application in various industries, such as entertainment and advertising, has brought image processing to the fore of these industries. However, the entire image processing is faced with the problem of data redundancy, which is mitigated through image compression. This is simply the art and science of reducing the number of bits/data of an image before it is transmitted and stored easily while the quality of image is maintained. Thus, through an exploratory study, this paper examines image compression as discussed in extant literature and emphasises on different methods used in image compression. The paper reviewed relevant literature from Elsevier, Emerald, IEEE, ProQuest and Google scholar databases. Specific methods are lossy and lossless techniques, which are further divided into run length encoding, and entropy encoding. In conclusion, the paper recommends compression techniques to adopt depending on the industry’s’ goals. Preferably, lossy compression is used to compress multimedia data which includes audio, video and images, while lossless compression technique is used to compress text and data files.

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.

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.

Technology is developing very quickly and will continue to increase, so it plays an important role in the process of sending information or data from one device to another. The speed of transmission depends on the size of the data to be sent. Data with a larger size requires a longer delivery time. The amount of storage space required increases as more files are stored. This has led to the development of file shrinking techniques, also known as data compression techniques, with the aim of minimizing the loss of data quality after transmission and reducing the amount of storage space required. Compression techniques have several algorithms that can be used to reduce file size. As in this research, the compression process is done with the run length encoding algorithm and the fixed length binary encoding algorithm. Both algorithms have different compression results, so it is necessary to make a comparison. To make the comparison, 6 grayscale image files with *.jpg extension are used with different resolutions and compare their performance according to predetermined parameters. The compression comparison results of one image data resolution of 300 x 300 in the Run Length Encoding algorithm has a Ratio of Compression (RC) 1.038792, Compression Ratio (CR) 96.266%, Redundancy (Rd) 3.734%, Compression time 399ms, and Decompression time 297ms. While the Fixed Length Binary Encoding algorithm has a Ratio of Compression (RC) of 1.37, Compression Ratio (CR) of 73.248%, Redundancy (Rd) of 26.752%, Compression time of 3258ms, and Decompression time of 1047ms. So from these results it can be said that the better performance in compressing images is the Fixed Length Binary Encoding algorithm compared to Run Length Encoding.

Many bandwidth compression techniques which have been applied to imagery can be characterized as low pass filters. Higher compression rates yield images with reduced resolution or sharpness. In general, the sharpness in an image is a function of high contrast edges. Several studies have shown that, although the presence of these edges is important to the overall subjective quality of the image, their fidelity is not. High contrast edge information can be isolated in the upper bit plane (the most significant bit) of most types of imagery. Simple run length encoding of this bit plane can be used to preserve the location and approximate peak amplitude of the edge information at an overhead cost of less than 0.1 bit/pixel. When upper bit plane run length encoding is combined with standard transform or DPCM coding: the resultant hybrid technique provides images with subjective quality improvements of better than two to one. This hybrid approach has been demonstrated on several types of imagery. Current activity is centered on an automatic intensity remapping function which guarantees the upper bit plane contains the optimal amount of information to ensure maximum run length encoding efficiency.

Exploiting variable-length padding bits for decoder performance improvement with its application to compressed video transmission

In this study combination of lossless compression techniques and Vigenere cipher was used in text steganography that makes use of email addresses to be the keys to reconstruct the secret message which has been embedded into the email text. After selecting the cover text that has highest repetition pattern regarding to the secret message the distance matrix was formed. The members of distance matrix were compressed by following lossless compression algorithms as in written sequence; Run Length Encoding (RLE) + Burrows Wheeler Transform (BWT) + Move to Forward (MTF) + Run Length Encoding + Arithmetic Encoding (AE). Later on Latin Square was used to form stego key 1and then Vigenere table was used to increase complexity of extracting stego key 1. Final step was to choose e-mail addresses by using stego key 1 and stego key 2to embed secret message into forward e-mail platform. The experimental results showed that proposed method has reasonable performance with high complexity.

This paper considers a new lossless image compression algorithm that provides reduction of data redundancy in video processing systems. To achieve the high compression level group encoding approach is proposed. The experimental research shows that proposed algorithm provides 1.33 times better compression quality index than the Run Length Encoding (RLE) algorithm.

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