Haar Wavelet Transform Image Compression Using Various Run Length Encoding Schemes

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.

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.

This paper presents a detailed and comprehensive review of image compression methods, emphasizing their role in optimizing both storage and transmission efficiency across various domains, from everyday use in social media to specialized applications like medical imaging and satellite data processing. We systematically explore both traditional and contemporary image compression techniques, categorizing them into lossless and lossy methods, transform-based approaches, and the latest advancements in machine learning-based compression. Lossless compression techniques, including Run-Length Encoding (RLE), Huffman Coding, Lempel-Ziv-Welch (LZW), and the Portable Network Graphics (PNG) format, are discussed for their ability to preserve image quality perfectly, albeit at the cost of relatively lower compression ratios. Conversely, lossy compression methods, such as JPEG and fractal compression, offer significant file size reduction by discarding non-essential data, while still maintaining acceptable visual quality for many practical applications. We further delve into transform-based approaches like Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT), which form the backbone of popular standards such as JPEG and JPEG 2000, enabling more efficient data representation in the frequency domain. Additionally, the study highlights emerging machine learning and deep learning techniques, such as autoencoders and Generative Adversarial Networks (GANs), that are pushing the boundaries of image compression by achieving unprecedented compression ratios while minimizing perceptual loss in image quality. Through a comparative analysis, we evaluate these methods based on multiple performance metrics, including compression ratio, computational complexity, image fidelity (measured via Peak Signal-to-Noise Ratio, PSNR, and Structural Similarity Index, SSIM), and their practical applications across different industries. Our findings suggest that while traditional methods such as JPEG, PNG, and JPEG 2000 remain widely adopted due to their simplicity and efficiency, emerging techniques driven by deep learning show great potential in adapting to specific image characteristics, achieving higher compression ratios, and better preserving image quality under extreme compression. Finally, this paper identifies key challenges and trends in the field, such as the increasing computational demands of advanced techniques, the need for adaptive compression strategies, and the importance of standardization for broad industry adoption. We conclude that while traditional methods will continue to play a significant role, the future of image compression lies in the integration of machine learning and content-aware technologies that dynamically optimize compression performance across diverse image types and application contexts.

In the medical industry, the amount of data that can be collected and kept is currently increasing. As a result, in order to handle these large amounts of data efficiently, compression methods must be re-examined while taking the algorithm complexity into account. An image processing strategy should be explored to eliminate the duplication image contents, so boosting the capability to retain or transport data in the best possible manner. Image Compression (IC) is a method of compressing images as they are being stored and processed. The information is preserved in a lossless image compression technique which allows for exact image reconstruction from compressed data with retain the quality of image to higher possible extend but it does not significantly decrease the size of the image. In this research work, the encoding algorithm is applied to various medical images such as brain image, dental x-ray image, hand x ray images, breast mammogram images and skin image can be used to minimize the bit size of the image pixels based on the different encoding algorithm such as Huffman, Lempel-Ziv-Welch (LZW) and Run Length Encoding (RLE) for effective compression and decompression without any quality loss to reconstruct the image. The image processing toolbox is used to apply the compression algorithms in MATLAB. To assess the compression efficiency of various medical images using different encoding techniques and performance indicators such as Compression Ratio (CR) and Compression Factor (CF). The LZW technique compresses binary images; however, it fails to generate a lossless image in this implementation. Huffman and RLE algorithms have a lower CR value, which means they compress data more efficiently than LZW, although RLE has a larger CF value than LZW and Huffman. When fewer CR and more CF are recorded, RLE coding becomes more viable. Finally, using state-of-the-art methodologies for the sample medical images, performance measures such as PSNR and MSE is retrieved and assessed.

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.

Although JPEG technique is considered as the most popular image compression standard, it behaves high visual degradation at low bit rates. In this paper, an efficient DCT–based image compression technique is proposed to achieve high Compression Ratio (CR) with high quality at both high and low bit rates. This technique uses switching between JPEG compression technique at high bit rates and a novel Adaptive Lossy Image Compression (ALIC) technique at low bit rates. ALIC is proposed to overcome the drawbacks of JPEG technique at low bitrates. The performance of the proposed technique is analysed at low and high bit rates on both grey and colour images. Performances of both JPEG and ALIC techniques are analysed and compared. The experimental results reveal that the proposed ALIC technique achieves better CR with acceptable SNR in comparison with JPEG technique. Also, the resultant CR of ALIC technique can be considerably increased with a slight decrease of its PSNR. This decrease in PSNR does not result in a noticeable visual degradation of the compressed image. On the other hand, increasing the CR of JPEG technique results in a noticeable visual degradation due to the appearance of blocking effect in the reconstructed image. Thus, it is greatly recommended to use ALIC technique in the applications that require high CR with stable PSNR. ALIC is a general purpose technique that can be applied, not only on images, but also on any data source which uses Huffman coding to achieve better CR. Therefore, it is suitable for compression of text, image and video.

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.

Digital chest radiography: quality assurance.

Image compression techniques have been widespreadly used in abundant of applications. In the consideration of image quality and compression ratio, an efficient compression technique is expected. Transform coding is known to be generally superior. In this paper, we will present a novel method based on adaptive classification and coefficient diffusion techniques to improve image compression quality while maintaining compression ratio. Experiments are conducted on a wide variety of images. Experimental results reveal that both of the image quality and the compression ratio are retained by applying the proposed method.

In the recent days, the importance of image compression techniques is exponentially increased due to the generation of massive amount of data which needs to be stored or transmitted. Numerous approaches have been presented for effective image compression by the principle of representing images in its compact form through the avoidance of unnecessary pixels. Vector quantization (VA) is an effective method in image compression and the construction of quantization table is an important process is an important task. The compression performance and the quality of reconstructed data are based on the quantization table, which is actually a matrix of 64 integers. The quantization table selection is a complex combinatorial problem which can be resolved by the evolutionary algorithms (EA). Presently, EA became famous to resolve the real world problems in a reasonable amount of time. This chapter introduces Firefly (FF) with Teaching and learning based optimization (TLBO) algorithm termed as FF-TLBO algorithm for the selection of quantization table. As the FF algorithm faces a problem when brighter FFs are insignificant, the TLBO algorithm is integrated to it to resolve the problem. This algorithm determines the best fit value for every bock as local best and best fitness value for the entire image is considered as global best. When these values are found by FF algorithm, compression process takes place by efficient image compression algorithm like Run Length Encoding and Huffman coding. The proposed FF-TLBO algorithm is evaluated by comparing its results with existing FF algorithm using a same set of benchmark images in terms of Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity index (SSIM), Compression Ratio (CR) and Compression Time (CT). The obtained results ensure the superior performance of FF-TLBO algorithm over FF algorithm and make it highly useful for real time applications.

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.

Research in the field of image compression has grown significantly with the increasing need to publish images in computer and mobile environments. Image abstraction plays an important role in digital image processing, which is crucial for efficient image transmission and storage. Without degrading the image quality, the image size in byte is minimized by using image compression technique. Many different ways are used in this process to compress the input image. Therefore, this research study proposes hybrid compression technique includes Lifting Wavelet Transform (LWT) with discrete cosine transform (DCT) to provide the best properties for an image that is performed in JPEG compression method. To get the high quality compression ratio, lossy and lossless methods are used by this technique and also used to maintain the reconstructed image quality. A high compression ratio is produced by lossy compression, where reconstructed images high quality is brought by lossless compression and later it will be decompressed with same results. MATLAB software tool is used to implement the proposed compression process and to verify its performance in terms of various parameters such as PSNR, MSE and SNR.

Edge preservance and block effect reduction by block coefficient diffusion method

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.

The main aim of this study is to decrease the amount of storage as much as possible and the decoded image seen on the monitor should be as close as possible to the original image. The main goal of this study is to design a fully hybrid system for medical image compression. For this purpose, a hybrid techniques were used to enhance the compression performance, decreasing the computational complexity level and raising the CR (Compression Ratio, the proposed system is adopted on these tools: to design a new fully hybrid image compression system to compress a medical image (Brain Tumour disease type). Furthermore, a new reliable algorithm was proposed in order to identify the ROI (Region of Interest) and NROI (Non-Region of Interest) before compression process. In addition, this algorithm has less computational complexity and efficient, also develop new algorithms to compress the ROI and NROI regions. The first region, ROI, is compressed by cascading of SPIHT and BAT algorithms. Meanwhile, the second region (NROI) is compressed by the 2D-DWT algorithm, finally to design a new coding system by mixed the RLE (Run-Length Encoding) and Huffman coding algorithms to improve the CR. The results indicate that the SPIHT-BAT algorithm has increase the compression ratio better than SPIHT. Furthermore, the result of ROI region better than the result of NROI region. While the result of coding when used (RLE- Huffman) algorithm better than the result when used (RLE) alone or Huffman algorithm. The different parameters of compression process indicate that the proposed system is better than that of Traditional systems that described in literature.