Image Compression using Run Length Encoding and its Optimisation

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.

Virtual tour is one of the rapidly growing applications of multimedia technology which is used for various purposes, including the dissemination of information in an interesting way. The education sector is also not spared from using virtual tour media for promotional purposes, and campuses are no exception to this rule. Large virtual tour content causes high access speed, ultimately reducing the level of comfort experienced by users. This study aims to compress panoramic images displayed on a campus virtual tour using a lossless compression method and the Run Length Encoding (RLE) algorithm. First, panoramic images are combined into one, then individual images are compressed. When recreating a virtual campus tour, compressed images are used so that the amount of data transferred is smaller. The load access speed index increases from 7,233 seconds to 3,789 seconds when images are compressed from 64 bits to 8 bits, with a compression percentage of 27%. The findings from this research are that the RLE algorithm has not been able to compress large files effectively even though it is quite successful in increasing the load access of the virtual tour website.

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. Storage limitations on any device mean that only a small number of such images can be stored. Moreover, transmitting and uploading larger images takes up more time and bandwidth. Image compression has thus emerged as a vital part of image processing. The need of the hour is to reduce the size of the image as much as possible ,while maintaining a high level of quality and preserving all the details in the image. Compressed images can also be transmitted faster and require less bandwidth. In this paper we will be discussing two compression techniques- Lempel–Ziv–Welch (LZW) and Run length Encoding (RLE)) and how to implement them. We will then compare them based on certain parameters.

Dynamic simulations based on time-varying inputs are extremely I/O intensive. This is shown by industrial appli cations generating environmental projections based on seasonal-to-interannual climate forecasts that have a compute to data access ratio of O(n) leading to significant performance degradation. Exploitation of compression techniques such as run length encoding (RLE) signifi cantly reduces the I/O bottleneck and storage require ments. Unfortunately, traditional RLE algorithms do not perform well in a parallel vector platform such as the Cray architecture. This paper describes the design and imple mentation of a new RLE algorithm based on data chunking and packing that exploits the Cray gather-scatter vector hardware and multiple processors. This approach reduces I/O and file storage requirements on average by an order of magnitude. Data intensive applications such as the integration of environmental and global climate models now become practical in a realistic time frame.

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

A real-time data processing algorithm based on run-length encoding (RLE) for an intelligent racing vehicle is introduced in this paper. In order to improve the achievement of the intelligent racing vehicle's running in the second loop by recoding route information, a new method based on RLE is provided by setting an optimal calculus threshold. Simulated by Matlab/Simulink, the route memorization algorithm shows the obvious advantage in the data compression, which makes the compression ratio up to 22.3. In the meantime, the calculus threshold can be constructed in a certain range, which qualifies the algorithm with a good robustness. Compared with the optimal results achieved by Matlab's genetic algorithm and direct search toolbox, the RLE algorithm can satisfy the requirements very well. When the embedded system has to face the flood for the data increasing geometrically, this high-quality real-time algorithm has been demonstrated with great practical potential.

Compression is the specialty of presenting the data in a conservative structure as opposed to its unique or uncompressed structure. Moreover, utilizing information compression, the size of a specific document can be decreased. This is extremely helpful when preparing, putting away, or moving a gigantic document, which needs huge resources. The speed of transmission relies on the number of pieces sent, the time needed for the encoder to create the coded message, and the time needed for the decoder to recoup the primary ensemble. In an information storage application, the level of compression is the essential concern. Compression can be named either lossless or lossy. Lossless compression methods remake the primary data from the compacted record with no loss of information. In this manner, the data does not alter during the decompression and compression measures. These sorts of compression calculations are called reversible compressions since the primary message is recreated by the decompression cycle. This paper analyzes the exhibition of the Huffman Encoding Algorithm, Lempel Zev Welch Algorithm, Arithmetic Encoding Algorithm, Adaptive Huffman Encoding Algorithm, Shannon Fano Algorithm, and Run Length Encoding Algorithm. Specifically, the efficiency of these calculations in compacting text information is compressed and assessed.

This study presents a new approach for lossy medical image compression using vector quantization. Recently, the digital image has been a reliable replacement for a hard copy of medical images, therefore, an effort has been made to ensure maintaining high-quality images to use for archiving, classification, or automated diagnostics support. Although the medical application contains all sorts of the images like microscopic, X-rays, tomography, and fiber optics imaging by angioplasty, all of this comes at the cost of using digital storage that needs to be regularly backed up and maintained and to help minimize the need for larger storage media, this study is focusing on applying Non-Decimated Wavelet Transform (NDWT) and combined lossy and lossless compression techniques that will allow the images to take much smaller storage space while maintaining the high level of quality for these images. This study is focusing on chest X-ray images compression using a combination of lossy compression techniques using two Vector Quantization (VQ) algorithms such as k-means clustering and Linde, Buzo, and Gray (LBG) algorithm, and three lossless compression techniques such as Arithmetic Coding (AC), Run Length Encoding (RLE) and Huffman Coding (HC) and choose the optimum combination of them. Then, the performance is measured using Compression Ratio (CR), processing time, or called run time, Peak Signal to Noise Ratio (PSNR), and Bit Rate.

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.

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.

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.

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.

In the area of digital image compression, computer algorithms are used to perform processing of images and compression. It deals with developing a digital system that perform operations on digital image. It has many advantages using in digital camera, film, Satellite, X-ray and many more applications. Image compression is a technique used to save the storage space normally used to compress images and videos. Number of compression algorithms are used like run length encoding, huffman coding, discrete cosine transform, vector quantization, fuzzy transform. This gives a brief idea on improved fuzzy technique to reduce noise in compressing image. There are so many techniques for compression but in this only present the techniques of improved fuzzy method to reduce noise and compressed the image by using edge detection. The main idea behind applying this is we have to preserve the well significant edges as Jpeg is the popular standard but at low bit rate Jpeg exhibits blocking artifacts means noisy effects that affect the visual image quality so to produce high visual quality image at low bit rate ,the algorithm is efficient and simple. The proposed algorithm consists of three steps. First, image is preprocessed using competitive fuzzy edge detection. Second, based on edge information image is compressed and decompressed using improved fuzzy transform. Third, reconstructed image is post processed using hybrid median filter for artifact reduction. Analysis proves the superiority of proposed algorithm. The results of different number of coefficients are compared with the value of PSNR, MSE of algorithm. After comparison of techniques it is found to be efficient for visualization.

Digital imaging in endoscopy

In the big data era, image compression is of significant importance in today’s world. Importantly, compression of large sized images is required for everyday tasks; including electronic data communications and internet transactions. However, two important measures should be considered for any compression algorithm: the compression factor and the quality of the decompressed image. In this paper, we use Frei-Chen bases technique and the Modified Run Length Encoding (RLE) to compress images. The Frei-Chen bases technique is applied at the first stage in which the average subspace is applied to each 3 × 3 block. Those blocks with the highest energy are replaced by a single value that represents the average value of the pixels in the corresponding block. Even though Frei-Chen bases technique provides lossy compression, it maintains the main characteristics of the image. Additionally, the Frei-Chen bases technique enhances the compression factor, making it advantageous to use. In the second stage, RLE is applied to further increase the compression factor. The goal of using RLE is to enhance the compression factor without adding any distortion to the resultant decompressed image. Integrating RLE with Frei-Chen bases technique, as described in the proposed algorithm, ensures high quality decompressed images and high compression rate. The results of the proposed algorithms are shown to be comparable in quality and performance with other existing methods.