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Journal articles on the topic 'Lossless Compression Technique'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Kaur, Harjit. "Image Compression Techniques with LZW method." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (2022): 1773–77. http://dx.doi.org/10.22214/ijraset.2022.39999.

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Abstract: Image compression is a technique which is used to reduce the size of the data. In other words, it means to remove the extra data from the available by applying some techniques and tricks which makes the data easy for storing and transmitting it over the transmission medium. The compression techniques are broadly divided into two categories. First one is Lossy Compression in which some of the data is lost while compressing it and second technique is lossless technique in which data is not lost after compressing it. These compression techniques can be applied on different image formats

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Salman, Ghalib Ahmed, Ahmed Ahmed, and HAREER MOAIAD HUSSEN. "Medical Image Compression Utilizing The Serial Differences and ‎Coding Techniques." InfoTech Spectrum: Iraqi Journal of Data Science 2, no. 1 (2025): 26–36. https://doi.org/10.51173/ijds.v2i1.13.

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Different medical devices for imaging used by centers and clinics produce an increasing number of sequential medical images. ‎Different imaging techniques such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and Fluoroscopy ‎produce a set of series for the same patient. Within these images, most of the image parts are fixed against noticeable changes ‎in the remaining part. This consumes non-ignorable storage space. This paper proposes a near-lossless compression ‎technique that considers the fixed image parts to focus on changing parts for a higher compression ratio. In some app

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Emy, Setyaningsih, and Harjoko Agus. "Survey of Hybrid Image Compression Techniques." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 4 (2017): 2206–14. https://doi.org/10.11591/ijece.v7i4.pp2206-2214.

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A compression process is to reduce or compress the size of data while maintaining the quality of information contained therein. This paper presents a survey of research papers discussing improvement of various hybrid compression techniques during the last decade. A hybrid compression technique is a technique combining excellent properties of each group of methods as is performed in JPEG compression method. This technique combines lossy and lossless compression method to obtain a highquality compression ratio while maintaining the quality of the reconstructed image. Lossy compression technique

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Magar, Satyawati, and Bhavani Sridharan. "Comparative analysis of various Image compression techniques for Quasi Fractal lossless compression." International Journal of Computer Communication and Informatics 2, no. 2 (2020): 30–45. http://dx.doi.org/10.34256/ijcci2024.

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The most important Entity to be considered in Image Compression methods are Paek to signal noise ratio and Compression ratio. These two parameters are considered to judge the quality of any Image.and they a play vital role in any Image processing applications. Biomedical domain is one of the critical areas where more image datasets are involved for analysis and biomedical image compression is very, much essential. Basically, compression techniques are classified into lossless and lossy. As the name indicates, in the lossless technique the image is compressed without any loss of data. But in th

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Altamimi, Amal, and Belgacem Ben Youssef. "Lossless and Near-Lossless Compression Algorithms for Remotely Sensed Hyperspectral Images." Entropy 26, no. 4 (2024): 316. http://dx.doi.org/10.3390/e26040316.

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Rapid and continuous advancements in remote sensing technology have resulted in finer resolutions and higher acquisition rates of hyperspectral images (HSIs). These developments have triggered a need for new processing techniques brought about by the confined power and constrained hardware resources aboard satellites. This article proposes two novel lossless and near-lossless compression methods, employing our recent seed generation and quadrature-based square rooting algorithms, respectively. The main advantage of the former method lies in its acceptable complexity utilizing simple arithmetic

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P., Renukadevi. "Lossless Medical Image Compression by Multi Oriented Prediction Technique." International Journal of Psychosocial Rehabilitation 24, no. 5 (2020): 1277–96. http://dx.doi.org/10.37200/ijpr/v24i5/pr201800.

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Ko, Yousun, Alex Chadwick, Daniel Bates, and Robert Mullins. "Lane Compression." ACM Transactions on Embedded Computing Systems 20, no. 2 (2021): 1–26. http://dx.doi.org/10.1145/3431815.

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This article presents Lane Compression, a lightweight lossless compression technique for machine learning that is based on a detailed study of the statistical properties of machine learning data. The proposed technique profiles machine learning data gathered ahead of run-time and partitions values bit-wise into different lanes with more distinctive statistical characteristics. Then the most appropriate compression technique is chosen for each lane out of a small number of low-cost compression techniques. Lane Compression’s compute and memory requirements are very low and yet it achieves a comp

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Patel, Jigar. "Enhanced Encoding Technique for Lossless Image Compression." RESEARCH HUB International Multidisciplinary Research Journal 9, no. 1 (2022): 01–09. http://dx.doi.org/10.53573/rhimrj.2022.v09i01.001.

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This paper proposes new encoding technique for lossless image compression. Here the pixel color information is stored in the file using efficient and sophisticated encoding technique which will be further compressed by existing compression algorithms and coding techniques. The final file with proposed extension generated by this scheme gives the better compression ratios than the existing image formats. The file is further decoded and image is recovered without any loss of information. The paper also discusses results and comparative analysis using popular image set compression ratio compariso

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Simanjuntak, Sari Magdalena. "Analisis Perbandingan Kompresi File Audio Menggunakan Algoritma Shannon Fano Dengan Algoritma Fibonacci Code." Jurnal Kajian Ilmiah Teknologi Informasi dan Komputer 2, no. 1 (2024): 1–10. http://dx.doi.org/10.62866/jutik.v2i1.110.

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Compression is a technique used to reduce the size of the data that serves to reduce memory capacity and speed up the data transfer process. In this study, the method used is the Shannon Fano method and the Fibonacci Code. In the Shannon Fano method the compression process is carried out by knowing the frequency of occurrence of each symbol which will then be sorted from the largest to the smallest frequency, while the Fibonacci Code method uses the Fibonacci integer series to encode the bit value of the data or file to be compressed. The file to be compressed is an audio file with an MP3 exte

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Setyaningsih, Emy, and Agus Harjoko. "Survey of Hybrid Image Compression Techniques." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 4 (2017): 2206. http://dx.doi.org/10.11591/ijece.v7i4.pp2206-2214.

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A compression process is to reduce or compress the size of data while maintaining the quality of information contained therein. This paper presents a survey of research papers discussing improvement of various hybrid compression techniques during the last decade. A hybrid compression technique is a technique combining excellent properties of each group of methods as is performed in JPEG compression method. This technique combines lossy and lossless compression method to obtain a high-quality compression ratio while maintaining the quality of the reconstructed image. Lossy compression technique

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Raghavendra, C., S. Sivasubramanian, and A. Kumaravel. "Improved image compression using effective lossless compression technique." Cluster Computing 22, S2 (2018): 3911–16. http://dx.doi.org/10.1007/s10586-018-2508-1.

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David S, Alex, Almas Begum, and Ravikumar S. "Content clustering for MRI Image compression using PPAM." International Journal of Engineering & Technology 7, no. 1.7 (2018): 126. http://dx.doi.org/10.14419/ijet.v7i1.7.10631.

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Image compression helps to save the utilization of memory, data while transferring the images between nodes. Compression is one of the key technique in medical image. Both lossy and lossless compressions where used based on the application. In case of medical imaging each and every components of pixel is very important hence its nature to chose lossless compression medical images. MRI images are compressed after processing. Here in this paper we have used PPMA method to compress the MRI image. For retrieval of the compressed image content clustering method used.

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Vamsikrishna, Mangalapalli, Oggi Sudhakar, Bhagya Prasad Bugge, et al. "Region based lossless compression for digital images using entropy coding." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 3 (2025): 1870. https://doi.org/10.11591/ijeecs.v38.i3.pp1870-1879.

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Image compression is a method for reducing video and image storage space. Moreover, enhancing the performance of the transmission and storage processes is important. The region based coding technique is important for compressing and sending medical images. In the medical field, lossless compression can help telemedicine applications achieve high efficiency. It affects image quality and takes a long time to encode. As a result, this study proposes region-based lossless compression for digital images using entropy coding. The best performance is achieved by segmenting these areas. In this case,

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Hussain, S. K., and G. Raja. "A JPEG 2000 BASED HYBRID IMAGE COMPRESSION TECHNIQUE FOR MEDICAL IMAGES." Nucleus 48, no. 4 (2011): 287–93. https://doi.org/10.71330/thenucleus.2011.822.

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Use of lossy compression for medical images could result in compression error that may be considered as diagnostic problem by medical doctor. Hybrid schemes, a combination of lossy and lossless compression are used to achieve higher compression ratio without compromising the subjective quality of medical images. This paper proposes a new hybrid compression method for medical images. Different combinations of lossy and lossless compression schemes: RLE, LZW, JPEG LS, JPEG and JPEG2000 are implemented to find out the best hybrid compression combination by keeping subjective quality of medical im

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Khaitu, Shree Ram, and Sanjeeb Prasad Panday. "Fractal Image Compression Using Canonical Huffman Coding." Journal of the Institute of Engineering 15, no. 1 (2020): 91–105. http://dx.doi.org/10.3126/jie.v15i1.27718.

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Image Compression techniques have become a very important subject with the rapid growth of multimedia application. The main motivations behind the image compression are for the efficient and lossless transmission as well as for storage of digital data. Image Compression techniques are of two types; Lossless and Lossy compression techniques. Lossy compression techniques are applied for the natural images as minor loss of the data are acceptable. Entropy encoding is the lossless compression scheme that is independent with particular features of the media as it has its own unique codes and symbol

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Wijaya, Bayu Angga, Sarwando Siboro, Mahendra Brutu, and Yelita Kristiani Lase. "Application of Huffman Algorithm and Unary Codes for Text File Compression." SinkrOn 7, no. 3 (2022): 1000–1007. http://dx.doi.org/10.33395/sinkron.v7i3.11567.

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Technique in carrying out data compression is an important point in technological developments. With compression in data in the form of text can include many uses, including for data transfer, copying and for backing up data. From its uses, this aspect is important for data security. There are many compression techniques on the data, including using huffman algorithms and unary code. One of its applications will be implemented on a text data that is widely used by digital actors in storing important data. The data must not be known by unauthorized parties in accessing the data. Therefore, huff

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Kanimozhirajasekaran*1, &. P. D. Sathya2. "AN EFFICIENT OPTIMIZATION TECHNIQUE FOR FRACTAL IMAGE COMPRESSION OF MEDICAL IMAGE." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 5, no. 2 (2018): 97–103. https://doi.org/10.5281/zenodo.1404141.

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Medical images plays a vital role in the area of medicine. It is important to store the medical images for future reference. So, there is a need for compressing of medical images for storage and communication purpose. Over the last few decades, many image compression methods have been introduced. They gives high compression ratio with loss of quality of image. Medical imagesshould always be stored in lossless format.There are several lossless compression techniques using which, original images can be restored.The objective of image compression is to reduce the redundancy of the 

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Maireles-González, Òscar, Joan Bartrina-Rapesta, Miguel Hernández-Cabronero, and Joan Serra-Sagristà. "Efficient Lossless Compression of Integer Astronomical Data." Publications of the Astronomical Society of the Pacific 135, no. 1051 (2023): 094502. http://dx.doi.org/10.1088/1538-3873/acf6e0.

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Abstract Each new generation of telescope produces increasingly larger astronomical data volumes, which are expected to reach the order of exabytes in the next decade. Effective and fast data compression methods are paramount to help the scientific community contain storage costs and improve transmission times. Astronomical data differs significantly from natural and Earth-observation images, asking for specifically tailored compression approaches. This paper presents a novel lossless compression technique that employs the discrete Haar wavelet transform within the JPEG 2000 standard. Its perf

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Mishra, Amit Kumar. "Versatile Video Coding (VVC) Standard: Overview and Applications." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 10, no. 2 (2019): 975–81. http://dx.doi.org/10.17762/turcomat.v10i2.13578.

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Information security includes picture and video compression and encryption since compressed data is more secure than uncompressed imagery. Another point is that handling data of smaller sizes is simple. Therefore, efficient, secure, and simple data transport methods are created through effective data compression technology. Consequently, there are two different sorts of compression algorithm techniques: lossy compressions and lossless compressions. Any type of data format, including text, audio, video, and picture files, may leverage these technologies. In this procedure, the Least Significant

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AYENI, Ayobami Gabriel, Sunday, AGHOLOR, and Godwin Oluseyi ODULAJA. "PERFORMANCE EVALUATION OF LOSSLESS COMPRESSION FOR IMAGES IN MOBILE RESPONSIVE WEB USING PARTICIPANTS’ OBSERVATION AND THURSTON RATING." Lagos Journal of Contemporary Studies in Education 2, no. 01 (2024): 24–32. https://doi.org/10.36349/lajocse.2024.v02i01.003.

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Compressing graphic-related content and multimedia elements like logos, objects, banners, and image data is undoubtedly necessary for a mobile responsive website. In image compression, redundant and/or irrelevant information is eliminated whilst the leftover is resourcefully encoded. However, selecting the compression technique or choice of appropriate method in handling image compression requires caution so as not to toss away non-redundant information and relevant fragments of image files in a bid to compress. This study investigates the functional performance and variation of a lossless met

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Tahouri, Mohammad Ali, Alin Adrian Alecu, Leon Denis, and Adrian Munteanu. "Lossless and Near-Lossless L-Infinite Compression of Depth Video Data." Sensors 25, no. 5 (2025): 1403. https://doi.org/10.3390/s25051403.

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The acquisition of depth information sensorial data is critically important in medical applications, such as the monitoring of the elderly or the extraction of human biometrics. In such applications, compressing the stream of depth video data plays an important role due to bandwidth constraints on transmission channels. This paper introduces a novel lightweight compression system that encodes the semantics of the input depth video and can operate in both lossless and L-infinite near-lossless compression modes. A quantization technique that targets the L-infinite norm for sparse distributions a

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Alarabeyyat, A., S. Al-Hashemi, T. Khdour, et al. "Lossless Image Compression Technique Using Combination Methods." Journal of Software Engineering and Applications 05, no. 10 (2012): 752–63. http://dx.doi.org/10.4236/jsea.2012.510088.

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Yousif, Muneera, Riham Jassim, Noor M. Abdullah, et al. "Hybrid Algorithm Compression Technique for Color Images." Migration Letters 20, S5 (2023): 442–51. http://dx.doi.org/10.59670/ml.v20is5.4023.

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The importance of different data compression algorithms is highlighted in the process of storing big data in data warehouses and archives and in reducing the volume of big data during its transmission through communication channels, so many compression algorithms have emerged that are designed to process different data. Images are used in different fields in our daily lives, such as social networks, medical diagnoses, remote sensing, and other fields, and since the size and accuracy of images are constantly evolving, the issue of storing and transferring images requires a compressing pro

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Jasim, Sarah. "MEDICAL IMAGES COMPRESSION BASED ON SPIHT AND BAT INSPIRED ALGORITHMS." Iraqi Journal for Computers and Informatics 45, no. 1 (2019): 1–5. http://dx.doi.org/10.25195/ijci.v45i1.43.

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There is a significant necessity to compress the medical images for the purposes of communication and storage.Most currently available compression techniques produce an extremely high compression ratio with a high-quality loss. Inmedical applications, the diagnostically significant regions (interest region) should have a high image quality. Therefore, it ispreferable to compress the interest regions by utilizing the Lossless compression techniques, whilst the diagnostically lessersignificant regions (non-interest region) can be compressed by utilizing the Lossy compression techniques. In this

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Abu Hilal, Tariq, Hasan Abu Hilal, and Ala Abu Hilal. "Multistage Arabic and Turkish Text Compression via Characters Encoding and 7-Zip." Journal of Ubiquitous Systems and Pervasive Networks 15, no. 01 (2021): 11–15. http://dx.doi.org/10.5383/juspn.15.01.002.

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Turkish lossless text compression was proposed by converting the character’s from UTF-8 to ANSI system for space-preserving. Likewise, we present a decoding method that transforms the encoded ANSI string back to its original format. Unlike the one-byte ANSI characters, some of the Turkish alphabets are being stored in 2 bytes size. All that space comes at a price. The developed sequential encoding technique will reduce the size of the text file up to 9%. Moreover, the Turkish encoded text will retain its original form after decoding. According to our proposal, it is considered as a lossless te

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N., A. N. Azman, Ali Samura, A. Rashid Rozeha, Asraf Saparudin Faiz, and Adib Sarijari Mohd. "A hybrid predictive technique for lossless image compression." Bulletin of Electrical Engineering and Informatics 8, no. 4 (2019): 1289–96. https://doi.org/10.11591/eei.v8i4.1612.

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Compression of images is of great interest in applications where efficiency with respect to data storage or transmission bandwidth is sought.The rapid growth of social media and digital networks have given rise to huge amount of image data being accessed and exchanged daily. However, the larger the image size, the longer it takes to transmit and archive. In other words, high quality images require huge amount of transmission bandwidth and storage space. Suitable image compression can help in reducing the image size and improving transmission speed. Lossless image compression is especially cruc

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Huma and Taru Tavatia Mrs. "FILE COMPRESSION SOFTWARE." International Journal of Advances in Engineering & Scientific Research 5, no. 2 (2018): 55–69. https://doi.org/10.5281/zenodo.10780762.

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<strong>ABSTRACT</strong> <em> </em> <em>Compression is important because it helps reduce the consumption of expensive resources, such as disk space or connection bandwidth. However compression requires information processing power , which can also be expensive lossless type. The compressed data has to be decompressed before use. The decompression technique is just the reverse of compression. Use of the right compression tools yields more compact and loss-less data output.</em> <em>The Domain “File Compressor” lets you reduce the overall number of bits and bytes in a file so i

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Hayati, Anis Kamilah, and Haris Suka Dyatmika. "THE EFFECT OF JPEG2000 COMPRESSION ON REMOTE SENSING DATA OF DIFFERENT SPATIAL RESOLUTIONS." International Journal of Remote Sensing and Earth Sciences (IJReSES) 14, no. 2 (2018): 111. http://dx.doi.org/10.30536/j.ijreses.2017.v14.a2724.

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The huge size of remote sensing data implies the information technology infrastructure to store, manage, deliver and process the data itself. To compensate these disadvantages, compressing technique is a possible solution. JPEG2000 compression provide lossless and lossy compression with scalability for lossy compression. As the ratio of lossy compression getshigher, the size of the file reduced but the information loss increased. This paper tries to investigate the JPEG2000 compression effect on remote sensing data of different spatial resolution. Three set of data (Landsat 8, SPOT 6 and Pleia

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Gautam, Deepika, and Vipin Saxena. "A Smarter Way to Compress and Decompress Data for Cloud Storage." Journal of Advances in Mathematics and Computer Science 40, no. 4 (2025): 1–12. https://doi.org/10.9734/jamcs/2025/v40i41984.

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The present work contributes an adaptive lossless compression-decompression technique for different data types, such as text, image, audio, and video files. The primary objective is to optimize cloud storage by maximize space savings while maintaining data confidentiality and reducing data loss. A Comparative analysis of experimental research of various compression methods is presented in the paper. The proposed method used search-based compression techniques using linear, binary, and interpolation search, to get effective lossless compression. The performance of method is computed using a dat

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Campisi, P., D. Hatzinakos, and A. Neri. "A perceptually lossless, model-based, texture compression technique." IEEE Transactions on Image Processing 9, no. 8 (2000): 1325–36. http://dx.doi.org/10.1109/83.855428.

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Gupta, Rajarshi. "Lossless Compression Technique for Real-Time Photoplethysmographic Measurements." IEEE Transactions on Instrumentation and Measurement 64, no. 4 (2015): 975–83. http://dx.doi.org/10.1109/tim.2014.2362837.

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Miss., Nilam V. Kundlik*1 &. Mr. Manoj Kumar Singh2. "OSCILLATION AND DCT BASED BIOMEDICAL IMAGE COMPRESSION." OSCILLATION AND DCT BASED BIOMEDICAL IMAGE COMPRESSION 7, no. 6 (2018): 353–59. https://doi.org/10.5281/zenodo.1290477.

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Image compression technique for  biomedical  image  analysis has importance as because of time consuming process and also its storage .   It   becomes   necessary   to compress  the  image  to  reduce     processing   time   required   to retrieve  target  components from biomedical  images. Also biomedical images requires more space for storage and also management of these images is very difficult.  To compress the image, there are different techniqu

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Gdeeb, Rasha Talib, Ghadah Al-Khafaji, and Noor Ali J. "Development of Hybrid Based Lossless Iris Image Compression Technique." International Journal of Engineering Research and Advanced Technology 08, no. 07 (2022): 11–16. http://dx.doi.org/10.31695/ijerat.2022.8.7.2.

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The biometric iris recognition is the most popular method and is extended successfully in use, but unfortunately, a large number of data bits are exhausted when stored that implicitly affecting the transmission bandwidth and a compression solution is urgently required based on removing redundancy(s). In this paper a simple hybrid image compression scheme is proposed, it is based on using two techniques of polynomial coding and Fourier transform coding to efficiently encoded lossless. The test results of the suggested method showed that improved of compression performance is achieved with an id

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Bondžulić, Boban, Nenad Stojanović, Vladimir Lukin, and Sergii Kryvenko. "JPEG and BPG visually lossless image compression via KonJND-1k database." Vojnotehnicki glasnik 72, no. 3 (2024): 1214–41. http://dx.doi.org/10.5937/vojtehg72-50300.

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Introduction/purpose: This paper presents the results of the research on visually lossless image compression which is of particular interest because it achieves a high degree of compression, while the visual quality of the image is not impaired, i.e., end users are very satisfied with the image quality. The analysis was carried out using the publicly available large-scale picture-wise KonJND-1k database which contains the results of subjective tests on JPEG and BPG compressed images. Methods: Thanks to the availability of images from the KonJND-1k database, the dependence of objective assessme

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Abdullah A. Hussain and Ghadah K. AL-Khafaji. "A Pixel Based Method for Image Compression." Tikrit Journal of Pure Science 26, no. 1 (2022): 113–22. http://dx.doi.org/10.25130/tjps.v26i1.108.

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The basic solution to overcome difficult issues related to huge size of digital images is to recruited image compression techniques to reduce images size for efficient storage and fast transmission. In this paper, a new scheme of pixel base technique is proposed for grayscale image compression that implicitly utilize hybrid techniques of spatial modelling base technique of minimum residual along with transformed technique of Discrete Wavelet Transform (DWT) that also impels mixed between lossless and lossy techniques to ensure highly performance in terms of compression ratio and quality. The p

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Sandhu, Amanpreet Kaur. "An Efficient Lossless Compression algorithm for Medical images." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (2021): 3793–99. http://dx.doi.org/10.17762/turcomat.v12i3.1663.

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Medical image compression plays a vital role in diagnosis of diseases which allowing manipulation, efficient, transmission and storage of color, binary and grayscale image. Before transmission and storage, a medical image may be required to be compressed. The objective of the study is to develop an efficient and effective technique for digital medical images which alleviates the blocking artifacts from grayscale image while retaining all relevant structures. In this paper, we demonstrate a highly engineered postprocessing filtering approach has been designed to remove blocking effects from med

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Maharjan, Shyam, Sujan Poudel, and Dipesh Tandukar. "A Comparative Study of Text-Based Lossless Compression." American Journal of Smart Technology and Solutions 3, no. 2 (2024): 34–39. http://dx.doi.org/10.54536/ajsts.v3i2.3566.

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Lossless data compression is a critical technique used to reduce file sizes without any loss of information during the encoding and decoding processes. This study presents a comparative analysis of two widely-used lossless compression algorithms: Huffman Encoding and Lempel-Ziv-Welch (LZW). The primary objective is to evaluate the performance of these algorithms in terms of compression ratio, compression time, decompression time, and space savings. The analysis was conducted on 100 files of varying sizes. The results demonstrate that the LZW algorithm outperforms Huffman Encoding, offering sup

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Kanimozhi, Rajasekaran*1 &. Dr. P.D. Sathya2. "FRACTAL IMAGE COMPRESSION OF VARIOUS MEDICAL IMAGES USING A COHERENT OPTIMIZATION TECHNIQUE." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 6, no. 1 (2019): 30–37. https://doi.org/10.5281/zenodo.2537819.

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Nowadays, hospitals create more number of medical data's due to serious disease issues. Each and every application uses images in the form of data. But, images are not directly used in some applications, because of need of large amount of memory space to store images. So, it is important to compress the medical images for storage and communication purpose. Compression is one of the essential techniques to solve the increase demands in storage space. To compress the medical images, various techniques have been used.  Many compression methods give high compression ratio with loss of qua

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T, Sujatha, and Selvam K. "LOSSLESS IMAGE COMPRESSION USING DIFFERENT ENCODING ALGORITHM FOR VARIOUS MEDICAL IMAGES." ICTACT Journal on Image and Video Processing 12, no. 4 (2022): 2704–9. https://doi.org/10.21917/ijivp.2022.0384.

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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

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T., Kavitha, and Jayasankar K. "Ideal Huffman Code for Lossless Image Compression for Ubiquitous Access." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 2 (2018): 765–74. https://doi.org/10.11591/ijeecs.v12.i2.pp765-774.

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Compression technique is adopted to solve various big data problems such as storage and transmission. The growth of cloud computing and smart phone industries has led to generation of huge volume of digital data. Digital data can be in various forms as audio, video, images and documents. These digital data are generally compressed and stored in cloud storage environment. Efficient storing and retrieval mechanism of digital data by adopting good compression technique will result in reducing cost. The compression technique is composed of lossy and lossless compression technique. Here we consider

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Rahman, Md Atiqur, and Mohamed Hamada. "Burrows–Wheeler Transform Based Lossless Text Compression Using Keys and Huffman Coding." Symmetry 12, no. 10 (2020): 1654. http://dx.doi.org/10.3390/sym12101654.

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Text compression is one of the most significant research fields, and various algorithms for text compression have already been developed. This is a significant issue, as the use of internet bandwidth is considerably increasing. This article proposes a Burrows–Wheeler transform and pattern matching-based lossless text compression algorithm that uses Huffman coding in order to achieve an excellent compression ratio. In this article, we introduce an algorithm with two keys that are used in order to reduce more frequently repeated characters after the Burrows–Wheeler transform. We then find patter

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Anandita, Ida Bagus Gede, I. Gede Aris Gunadi, and Gede Indrawan. "Analisis Kinerja Dan Kualitas Hasil Kompresi Pada Citra Medis Sinar-X Menggunakan Algoritma Huffman, Lempel Ziv Welch Dan Run Length Encoding." SINTECH (Science and Information Technology) Journal 1, no. 1 (2018): 7–15. http://dx.doi.org/10.31598/sintechjournal.v1i1.179.

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Technological progress in the medical area made medical images like X-rays stored in digital files. The medical image file is relatively large so that the image needs to be compressed. The lossless compression technique is an image compression where the decompression results are the same as the original or no information lost in the compression process. The existing algorithms on lossless compression techniques are Run Length Encoding (RLE), Huffman, and Lempel Ziv Welch (LZW). This study compared the performance of the three algorithms in compressing medical images. The result of image decomp

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Umamageswari, A., and G. R. Suresh. "Analysis of Secure Medical Image Communication with Digital Signature and Reversible Watermarking." TELKOMNIKA Indonesian Journal of Electrical Engineering 15, no. 3 (2015): 544. http://dx.doi.org/10.11591/tijee.v15i3.1573.

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<p>Protection of Medical image contents becomes the important issue in computer network security. Digital Watermarking has becomes a promising technique for medical content authentication, it allows to embed relevant information with the image, which provides confidentiality, integrity and authentication by embedding Digital Signature (DS) with the Medical image. In this paper we focus on need for reversible watermarking, Medical Image Compression and security related problems in medical images, it comparing the performances of various lossless watermarking techniques for various medical

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Kavitha, T., and K. Jayasankar. "Ideal Huffman Code for Lossless Image Compression for Ubiquitous Access." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 2 (2018): 765. http://dx.doi.org/10.11591/ijeecs.v12.i2.pp765-774.

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Abstract:

<p>Compression technique is adopted to solve various big data problems such as storage and transmission. The growth of cloud computing and smart phone industries has led to generation of huge volume of digital data. Digital data can be in various forms as audio, video, images and documents. These digital data are generally compressed and stored in cloud storage environment. Efficient storing and retrieval mechanism of digital data by adopting good compression technique will result in reducing cost. The compression technique is composed of lossy and lossless compression technique. Here we

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Saad, Saad, and Tawfiq A. AL AL-Assadi. "Deep Learning-Based model for Medical Image Compression." Journal of Intelligent Systems and Internet of Things 13, no. 2 (2024): 192–201. http://dx.doi.org/10.54216/jisiot.130215.

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Efficient compression algorithms are required to handle the growing amount of medical picture data, ensuring that storage and transmission requirements are met without compromising diagnostic quality. This research presents a hybrid image compression framework that integrates deep learning alongside standard lossless compression techniques. A convolutional autoencoder (CAE) learns a compact representation of medical images, which are subsequently compressed using the Brotli algorithm. Our technique beats conventional approaches, like JPEG, JPEG2000, and wavelet-based ones, according to an anal

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Punam Bajaj, Punam Bajaj. "Affable Compression through Lossless Column-Oriented Huffman Coding Technique." IOSR Journal of Computer Engineering 11, no. 6 (2013): 89–96. http://dx.doi.org/10.9790/0661-1168996.

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Srinivasan, K., and M. Ramasubba Reddy. "Efficient preprocessing technique for real-time lossless EEG compression." Electronics Letters 46, no. 1 (2010): 26. http://dx.doi.org/10.1049/el.2010.2349.

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Gupta, Mohit, and Narendra D Iondhe. "Low Complexity near Lossless Image Compression Technique for Telemedicine." International Journal of Computer Applications 29, no. 7 (2011): 43–50. http://dx.doi.org/10.5120/3574-4931.

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Soegandi, Andreas. "Prototipe Kompresi Lossless Audio Codec Menggunakan Entropy Encoding." ComTech: Computer, Mathematics and Engineering Applications 1, no. 2 (2010): 749. http://dx.doi.org/10.21512/comtech.v1i2.2579.

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The purpose of this study was to perform lossless compression on the uncompress audio file audio to minimize file size without reducing the quality. The application is developed using the entropy encoding compression method with rice coding technique. For the result, the compression ratio is good enough and easy to be developed because the algorithm is quite simple.

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S, Devipriya. "An Assessment of Density Effects on MRI Brain Images Using Lossy and Lossless Coding." Shanlax International Journal of Arts, Science and Humanities 6, S1 (2018): 105–14. https://doi.org/10.5281/zenodo.1410999.

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The Project proposes the region based Image compression technique based on the clustering model and hybrid compression technique. The primary and secondary region of interest will be selected automatically by the clustering algorithm. The lifting based discrete wavelet and Curvelet is used to decorrelate the pixels into fine as well as redundant or noisy data and edge details. The regions are encoded by lossless and lossy technique to increase the compression ratio and preserve the image quality. These methods are useful to compress data for transmission and telem

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