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Journal articles on the topic 'Image compression'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Er., Neha Saini Mr. Naveen Dhillion Mr. Manit Kapoor. "A PAPER ON A COMPARATIVE STUDY BLOCK TRUNCATING CODING, WAVELET, FRACTAL IMAGE COMPRESSION & EMBEDDED ZERO TREE." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 7 (2016): 1052–61. https://doi.org/10.5281/zenodo.57987.

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Many different image compression techniques currently exist for the compression of different types of images. Image compression is fundamental to the efficient and cost-effective use of digital imaging technology and applications. In this study Image compression was applied to compress and decompress image at various compression ratios. Compressing an image is significantly different than compressing raw binary data. For this different compression algorithm are used to compress images. Fractal image compression has been widely used to compress the image.  We undertake a study of the perfo

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Saudagar, Abdul Khader Jilani. "Biomedical Image Compression Techniques for Clinical Image Processing." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 12 (2020): 133. http://dx.doi.org/10.3991/ijoe.v16i12.17019.

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Image processing is widely used in the domain of biomedical engineering especially for compression of clinical images. Clinical diagnosis receives high importance which involves handling patient’s data more accurately and wisely when treating patients remotely. Many researchers proposed different methods for compression of medical images using Artificial Intelligence techniques. Developing efficient automated systems for compression of medical images in telemedicine is the focal point in this paper. Three major approaches were proposed here for medical image compression. They are image compres

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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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Khan, Sulaiman, Shah Nazir, Anwar Hussain, Amjad Ali, and Ayaz Ullah. "An efficient JPEG image compression based on Haar wavelet transform, discrete cosine transform, and run length encoding techniques for advanced manufacturing processes." Measurement and Control 52, no. 9-10 (2019): 1532–44. http://dx.doi.org/10.1177/0020294019877508.

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Image compression plays a key role in the transmission of an image and storage capacity. Image compression aims to reduce the size of the image with no loss of significant information and no loss of quality in the image. To reduce the storage capacity of the image, the image compression is proposed in order to offer a compact illustration of the information included in the image. Image compression exists in the form of lossy or lossless. Even though image compression mechanism has a prominent role for compressing images, certain conflicts still exist in the available techniques. This paper pre

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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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Walaa, M. Abd-Elhafiez, Gharibi Wajeb, and Heshmat Mohamed. "An efficient color image compression technique." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 5 (2020): 2371~2377. https://doi.org/10.12928/TELKOMNIKA.v18i5.8632.

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We present a new image compression method to improve visual perception of the decompressed images and achieve higher image compression ratio. This method balances between the compression rate and image quality by compressing the essential parts of the image-edges. The key subject/edge is of more significance than background/non-edge image. Taking into consideration the value of image components and the effect of smoothness in image compression, this method classifies the image components as edge or non-edge. Low-quality lossy compression is applied to non-edge components whereas high-quality l

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Katayama, O., S. Ishihama, K. Namiki, and I. Ohi. "Color Changes in Electronic Endoscopic Images Caused by Image Compression." Diagnostic and Therapeutic Endoscopy 4, no. 1 (1997): 43–50. http://dx.doi.org/10.1155/dte.4.43.

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In recent years, recording of color still images into magneto–optical video disks has been increasingly used as a method for recording electronic endoscopic images. In this case, image compression is often used to reduce the volume and cost of recording media and also to minimize the time required for image recording and playback. With this in mind, we recorded 8 images into a magneto-optical video disk in 4 image compression modes (no compression, weak compression, moderate compression, and strong compression) using the Joint Photographic Image Coding Experts Group (JPEG) system, which is a w

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Cardone, Barbara, Ferdinando Di Martino, and Salvatore Sessa. "Fuzzy Transform Image Compression in the YUV Space." Computation 11, no. 10 (2023): 191. http://dx.doi.org/10.3390/computation11100191.

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This research proposes a new image compression method based on the F1-transform which improves the quality of the reconstructed image without increasing the coding/decoding CPU time. The advantage of compressing color images in the YUV space is due to the fact that while the three bands Red, Green and Blue are equally perceived by the human eye, in YUV space most of the image information perceived by the human eye is contained in the Y band, as opposed to the U and V bands. Using this advantage, we construct a new color image compression algorithm based on F1-transform in which the image compr

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Kaur, Gaganpreet, Hitashi Hitashi, and Dr Gurdev Singh. "PERFORMANCE EVALUATION OF IMAGE QUALITY BASED ON FRACTAL IMAGE COMPRESSION." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 2, no. 1 (2012): 20–27. http://dx.doi.org/10.24297/ijct.v2i1.2608.

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Fractal techniques for image compression haverecently attracted a great deal of attention. Fractalimage compression is a relatively recenttechnique based on the representation of animage by a contractive transform, on the space ofimages, for which the fixed point is close to theoriginal image. This broad principle encompassesa very wide variety of coding schemes, many ofwhich have been explored in the rapidly growingbody of published research.Unfortunately, littlein the way of practical algorithms or techniqueshas been published. Here present a technique forimage compression that is based on a

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Kryvenko, Sergii, Vladimir Lukin, Boban Bondžulić, and Nenad Stojanović. "COMPRESSION OF NOISY GRAYSCALE IMAGES WITH COMPRESSION RATIO ANALYSIS." Advanced Information Systems 9, no. 2 (2025): 68–74. https://doi.org/10.20998/2522-9052.2025.2.09.

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The object of the study is the process of compressing noisy images in a lossy manner by better portable graphics (BPG) encoder. The subject of the study is the method for adaptive selection of the coder parameter Q depending on noise intensity and image complexity. The goal of the study is to consider the basic characteristics of lossy compression of remote sensing images contaminated by additive white Gaussian noise with giving recommendations of preferable Q setting. Methods used: numerical simulation, verification for test images. Results obtained: 1) the dependencies of compression ratio o

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Khatun, Shamina, and Anas Iqbal. "A Review of Image Compression Using Fractal Image Compression with Neural Network." International Journal of Innovative Research in Computer Science & Technology 6, no. 2 (2018): 9–11. http://dx.doi.org/10.21276/ijircst.2018.6.2.1.

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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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Paul, Okuwobi Idowu, and Yong Hua Lu. "A New Approach in Digital Image Compression Using Unequal Error Protection (UEP)." Applied Mechanics and Materials 704 (December 2014): 403–7. http://dx.doi.org/10.4028/www.scientific.net/amm.704.403.

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This paper proposes a new algorithms for compression of digital images especially at the encoding stage of compressive sensing. The research consider the fact that a certain region of a given imagery is more important in most applications. The first algorithm proposed for the encoding stage of Compressive Sensing (CS) exploits the known structure of transform image coefficients. The proposed algorithm makes use of the unequal error protection (UEP) principle, which is widely used in the area of error control coding. The second algorithm which exploits the UEP principle to recover the more impo

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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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Di Martino, Ferdinando, and Salvatore Sessa. "A Multilevel Fuzzy Transform Method for High Resolution Image Compression." Axioms 11, no. 10 (2022): 551. http://dx.doi.org/10.3390/axioms11100551.

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The Multilevel Fuzzy Transform technique (MF-tr) is a hierarchical image compression method based on Fuzzy Transform, which is successfully used to compress images and manage the information loss of the reconstructed image. Unlike other lossy image compression methods, it ensures that the quality of the reconstructed image is not lower than a prefixed threshold. However, this method is not suitable for compressing massive images due to the high processing times and memory usage. In this paper, we propose a variation of MF-tr for the compression of massive images. The image is divided into tile

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Mohammed, Hind Rostom, and Ameer Abd Al-Razaq. "SWF Image Compression by Evaluating objects compression ratio." Journal of Kufa for Mathematics and Computer 1, no. 2 (2010): 105–18. http://dx.doi.org/10.31642/jokmc/2018/010209.

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This work discusses the compression objects ratio for Macromedia Flash File (SWF) Image by Wavelet functions for compression and there effect for Macromedia Flash File (SWF) Images compression . We discusses classification objects in Macromedia Flash (SWF) image in to nine types objects Action, Font,Image, Sound, Text, Button, Frame, Shape and Sprite. The work is particularly targeted towards wavelet image compression best case by using Haar Wavelet Transformation with an idea to minimize the computational requirements by applying different compression thresholds for the waveletcoefficients an

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Mohammed, Sajaa G., Safa S. Abdul-Jabbar, and Faisel G. Mohammed. "Art Image Compression Based on Lossless LZW Hashing Ciphering Algorithm." Journal of Physics: Conference Series 2114, no. 1 (2021): 012080. http://dx.doi.org/10.1088/1742-6596/2114/1/012080.

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Abstract Color image compression is a good way to encode digital images by decreasing the number of bits wanted to supply the image. The main objective is to reduce storage space, reduce transportation costs and maintain good quality. In current research work, a simple effective methodology is proposed for the purpose of compressing color art digital images and obtaining a low bit rate by compressing the matrix resulting from the scalar quantization process (reducing the number of bits from 24 to 8 bits) using displacement coding and then compressing the remainder using the Mabel ZF algorithm

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Lalithambigai, B., and S. Chitra. "Segment Based Compressive Sensing (SBCS) of Color Images for Internet of Multimedia Things Applications." Journal of Medical Imaging and Health Informatics 12, no. 1 (2022): 1–6. http://dx.doi.org/10.1166/jmihi.2022.3848.

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Telemedicine is one of the IoMT applications transmitting medical images from hospital to remote medical centers for diagnosis and treatment. To share this multimedia content across internet, storage and transmission become a challenge because of its huge volume. New compression techniques are being continuously introduced to circumvent this issue. Compressive sensing (CS) is a new paradigm in signal compression. Block based compressive sensing (BCS) is a standard and commonly used technique in color image compression. However, BCS suffers from block artifacts and during transmission, mistakes

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Wang, Yan Wei, and Hui Li Yu. "Wavelet Transforms of Image Reconstruction Based on Compressed Sampling." Applied Mechanics and Materials 58-60 (June 2011): 1920–25. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1920.

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A compressive sensing technique for image signal to cope with image compression and restoration is adopted in this paper. First of all wavelet transforms method is applied in image compressing to preserve the constructive, Secondly, sparse matrix is available by required wavelet ratio. Thirdly, the compressing image is used to restoration the original image. Experimental results show that the proposed algorithm is effective and compares favorably with existing techniques.

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Mansyuri, Umar. "KOMPRESI DATA TEKS DENGAN METODE RUN LENGTH ENCODING." Jurnal Ilmiah Sistem Informasi 1, no. 2 (2021): 102–9. http://dx.doi.org/10.46306/sm.v1i2.13.

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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 ap

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Shyamala, N., and Dr S. Geetha. "Compression of Medical Images Using Wavelet Transform and Metaheuristic Algorithm for Telemedicine Applications." International Journal of Electrical and Electronics Research 10, no. 2 (2022): 161–66. http://dx.doi.org/10.37391/ijeer.100219.

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Medical image compression becomes necessary to efficiently handle huge number of medical images for storage and transmission purposes. Wavelet transform is one of the popular techniques widely used for medical image compression. However, these methods have some limitations like discontinuity which occurs when reducing image size employing thresholding method. To overcome this, optimization method is considered with the available compression methods. In this paper, a method is proposed for efficient compression of medical images based on integer wavelet transform and modified grasshopper optimi

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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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Hatem, Hiyam, Raed Majeed, and Jumana Waleed. "A singular value decomposition based approach for the compression of encrypted images." International Journal of Engineering & Technology 7, no. 3 (2018): 1332. http://dx.doi.org/10.14419/ijet.v7i3.12707.

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Image compression is a process which supplies a good solution to the current problems of data storage by reducing redundancy, and irrelevance within images. This paper provides effective encryption then compression technique applied for compressing images within the entire domain of encryption. The Singular Value Decomposition (SVD) application has been described for the results of compression from an image encrypted based on Discrete wavelet transforms (DWT). Initially, the original image has been decomposed into a pyramid of wavelet by utilizing DWT. The DWT subbands are enciphered via a pse

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Bao, Xuecai, Chen Ye, Longzhe Han, and Xiaohua Xu. "Image Compression for Wireless Sensor Network: A Model Segmentation-Based Compressive Autoencoder." Wireless Communications and Mobile Computing 2023 (October 25, 2023): 1–12. http://dx.doi.org/10.1155/2023/8466088.

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Aiming at the problems of image quality, compression performance, and transmission efficiency of image compression in wireless sensor networks (WSN), a model segmentation-based compressive autoencoder (MS-CAE) is proposed. In the proposed algorithm, we first divide each image in the dataset into pixel blocks and design a novel deep image compression network with a compressive autoencoder to form a compressed feature map by encoding pixel blocks. Then, the reconstructed image is obtained by using the quantized coefficients of the quantizer and splicing the decoded feature maps in order. Finally

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Singh Samra, Hardeep. "Image Compression Techniques." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 2, no. 2 (2012): 49–52. http://dx.doi.org/10.24297/ijct.v2i1.2616.

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Digital images required large number of bits to represent them and in their canonical representation, generally contain significant amount of redundancy. Image compression techniques reduce the number of bits required to represent an image by taking advantage of these redundancies.To overcome this redundancy several image compression techniques are discussed in this paper along with their benefits.

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Wen, Cathlyn Y., and Robert J. Beaton. "Subjective Image Quality Evaluation of Image Compression Techniques." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 40, no. 23 (1996): 1188–92. http://dx.doi.org/10.1177/154193129604002309.

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Image compression reduces the amount of data in digital images and, therefore, allows efficient storage, processing, and transmission of pictorial information. However, compression algorithms can degrade image quality by introducing artifacts, which may be unacceptable for users' tasks. This work examined the subjective effects of JPEG and wavelet compression algorithms on a series of medical images. Six digitized chest images were processed by each algorithm at various compression levels. Twelve radiologists rated the perceived image quality of the compressed images relative to the correspond

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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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Gashnikov, M. "Statistical encoding for image compression based on hierarchical grid interpolation." Computer Optics 41, no. 6 (2017): 905–12. http://dx.doi.org/10.18287/2412-6179-2017-41-6-905-912.

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Algorithms of statistical encoding for image compression are investigated. An approach is proposed to increase the efficiency of variable-length codes when compressing images with losses. An algorithm of statistical encoding is developed for use as part of image compression methods that encode a de-correlated signal with an uneven probability distribution. An experimental comparison of the proposed algorithm with the algorithms ZIP and ARJ is performed while encoding the specific data of the hierarchical compression method. In addition, an experimental comparison of the hierarchical method of

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Anitha, C. Yoga. "Performance Evaluation of Hybrid Method for Securing and Compressing Images." International Journal of Computing Algorithm 9, no. 1 (2020): 1–9. http://dx.doi.org/10.20894/ijcoa.101.009.001.001.

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Security is a most important field of research work for sending and receiving of data in secret way over the network. Cryptographyis a method for securing transformation like image, audio, video, text without any hacking problem. Encryption and Decryption are two methods used to secure the data. Image compression technique used to reducing the size of an image for effective data communication. There are variety of algorithms has been proposed in the literature for securing images using encryption/decryption techniques and reduce the size of images using image compression techniques. These tech

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Sawaneh, Ibrahim Abdulai. "DWT Based Image Compression for Health Systems." Journal of Advance Research in Medical & Health Science (ISSN: 2208-2425) 4, no. 9 (2018): 01–67. http://dx.doi.org/10.53555/nnmhs.v4i9.603.

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There are calls for enhancing present healthcare sectors when it comes to handling huge data size of patients’ records. The huge files contain lots of duplicate copies. Therefore, the ideal of compression comes into play. Image data compression removes redundant copies (multiple unnecessary copies) that increase the storage space and transmission bandwidth. Image data compression is pivotal as it helps reduce image file size and speeds up file transmission rate over the internet through multiple wavelet analytics methods without loss in the transmitted medical image data. Therefore this report

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Al-Saleem, Riyadh M., Yasameen A. Ghani, and Shihab A. Shawkat. "Improvement of Image Compression by Changing the Mathematical Equation Style in Communication Systems." International Journal of Digital Multimedia Broadcasting 2022 (November 4, 2022): 1–7. http://dx.doi.org/10.1155/2022/3231533.

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Compression is an essential process to reduce the amount of information by reducing the number of bits; this process is necessary for uploading images, audio, video, storage services, and TV transmission. In this paper, image compressions with losses from this action will be shown for some common patterns. The compression process uses different mathematical equations that have different methods and efficiencies, so some common mathematical methods for each style are presented taking into consideration the pros and cons of each method. In this paper, it is demonstrated that there is a quality i

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Kovalenko, Bogdan, Volodymyr Rebrov, and Volodymyr Lukin. "Analysis of the potential efficiency of post-filtering noisy images after lossy compression." Ukrainian journal of remote sensing 10, no. 1 (2023): 11–16. http://dx.doi.org/10.36023/ujrs.2023.10.1.231.

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An increase in the number of images and their average size is the general trend nowadays. This increase leads to certain problems with data storage and transfer via communication lines. A common way to solve this problem is to apply lossy compression that provides sufficiently larger compression ratios compared to lossless compression approaches. However, lossy compression has several peculiarities, especially if a compressed image is corrupted by quite intensive noise. First, a specific noise-filtering effect is observed. Second, an optimal operational point (OOP) might exist where the qualit

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Naumenko, Victoriia, Bogdan Kovalenko, and Volodymyr Lukin. "BPG-based compression analysis of Poisson-noisy medical images." Radioelectronic and Computer Systems, no. 3 (September 29, 2023): 91–100. http://dx.doi.org/10.32620/reks.2023.3.08.

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The subject matter is lossy compression using the BPG encoder for medical images with varying levels of visual complexity, which are corrupted by Poisson noise. The goal of this study is to determine the optimal parameters for image compression and select the most suitable metric for identifying the optimal operational point. The tasks addressed include: selecting test images sized 512x512 in grayscale with varying degrees of visual complexity, encompassing visually intricate images rich in edges and textures, moderately complex images with edges and textures adjacent to homogeneous regions, a

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Ma, Shaowen. "Comparison of image compression techniques using Huffman and Lempel-Ziv-Welch algorithms." Applied and Computational Engineering 5, no. 1 (2023): 793–801. http://dx.doi.org/10.54254/2755-2721/5/20230705.

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Image compression technology is very popular in the field of image analysis because the compressed image is convenient for storage and transmission. In this paper, the Huffman algorithm and Lempel-Ziv-Welch (LZW) algorithm are introduced. They are widely used in the field of image compression, and the compressed image results of the two algorithms are calculated and compared. Based on the four dimensions of Compression Ratio (CR), Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Bits Per Pixel (BPP), the applicable conditions of the two algorithms in compressing small image files

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Cahya Dewi, Dewa Ayu Indah, and I. Made Oka Widyantara. "Usage analysis of SVD, DWT and JPEG compression methods for image compression." Jurnal Ilmu Komputer 14, no. 2 (2021): 99. http://dx.doi.org/10.24843/jik.2021.v14.i02.p04.

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Through image compression, can save bandwidth usage on telecommunication networks, accelerate image file sending time and can save memory in image file storage. Technique to reduce image size through compression techniques is needed. Image compression is one of the image processing techniques performed on digital images with the aim of reducing the redundancy of the data contained in the image so that it can be stored or transmitted efficiently. This research analyzed the results of image compression and measure the error level of the image compression results. The analysis to be carried out i

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Mahdi, Reyadh, Faisal Ghazi Abdiwi, and Abd Abrahim Mosslah. "Enhanced Compression Medical Image Speed for Computed Radiography." Wasit Journal for Pure sciences 3, no. 2 (2024): 146–49. http://dx.doi.org/10.31185/wjps.298.

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In recent years, there has been a significant surge in the volume of medical imaging data. This surge poses challenges for the functioning of PACS communication systems and image archiving. The most effective solution to address this issue involves compressing images through digital encryption, which optimally utilizes storage space. This process involves reformatting the imaging data by reducing redundancy, leading to image compression. While this reduction in redundancy is readily apparent in individual images, there is a vulnerability in these methods that tends to overlook a source of repe

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Zhou, Xichuan, Lang Xu, Shujun Liu, Yingcheng Lin, Lei Zhang, and Cheng Zhuo. "An Efficient Compressive Convolutional Network for Unified Object Detection and Image Compression." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5949–56. http://dx.doi.org/10.1609/aaai.v33i01.33015949.

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This paper addresses the challenge of designing efficient framework for real-time object detection and image compression. The proposed Compressive Convolutional Network (CCN) is basically a compressive-sensing-enabled convolutional neural network. Instead of designing different components for compressive sensing and object detection, the CCN optimizes and reuses the convolution operation for recoverable data embedding and image compression. Technically, the incoherence condition, which is the sufficient condition for recoverable data embedding, is incorporated in the first convolutional layer

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Haque, Ershadul, Manoranjan Paul, Faranak Tohidi, and Anwaar Ulhaq. "An Overview of Quantum Circuit Design Focusing on Compression and Representation." Electronics 14, no. 1 (2024): 72. https://doi.org/10.3390/electronics14010072.

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Quantum image computing has attracted attention due to its vast storage capacity and faster image data processing, leveraging unique properties such as parallelism, superposition, and entanglement, surpassing classical computers. Although classical computing power has grown substantially over the last decade, its rate of improvement has slowed, struggling to meet the demands of massive datasets. Several approaches have emerged for encoding and compressing classical images on quantum processors. However, a significant limitation is the complexity of preparing the quantum state, which translates

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Syuhada, Ibnu. "Implementasi Algoritma Arithmetic Coding dan Sannon-Fano Pada Kompresi Citra PNG." TIN: Terapan Informatika Nusantara 2, no. 9 (2022): 527–32. http://dx.doi.org/10.47065/tin.v2i9.1027.

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The rapid development of technology plays an important role in the rapid exchange of information. In sending information in the form of images, there are still problems, including because of the large size of the image so that the solution to this problem is to perform compression. In this thesis, we will implement and compare the performance of the Arithmetic Coding and Shannon-Fano algorithms by calculating the compression ratio, compressed file size, compression and decompression process speed. Based on all test results, that the Arithmetic Coding algorithm produces an average compression r

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Mateika, Darius, and Romanas Martavicius. "ANALYSIS OF THE COMPRESSION RATIO AND QUALITY IN AERIAL IMAGES." Aviation 11, no. 4 (2007): 24–28. http://dx.doi.org/10.3846/16487788.2007.9635973.

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In modern photomap systems, images are stored in centralized storage. Choosing a proper compression format for the storage of an aerial image is an important problem. This paper analyses aerial image compression in popular compression formats. For the comparison of compression formats, an image quality evaluation algorithm based on the calculation of the mean exponent error value is proposed. An image quality evaluation experiment is presented. The distribution of errors in aerial images and explanation of the causes for worse than usual compression effect are analysed. An integrated solution

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Dr. R. B. Dubey, Dr R. B. Dubey, and Parul Parul. "Visually Lossless JPEG2000 Image Compression." Indian Journal of Applied Research 3, no. 9 (2011): 211–16. http://dx.doi.org/10.15373/2249555x/sept2013/66.

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Gunasheela, Keragodu Shivanna, and Shreenivasamurthy Prasantha Haranahalli. "Two-dimensional satellite image compression using compressive sensing." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (2022): 311–19. https://doi.org/10.11591/ijece.v12i1.pp311-319.

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Compressive sensing is receiving a lot of attention from the image processing research community as a promising technique for image recovery from very few samples. The modality of compressive sensing technique is very useful in the applications where it is not feasible to acquire many samples. It is also prominently useful in satellite imaging applications since it drastically reduces the number of input samples thereby reducing the storage and communication bandwidth required to store and transmit the data into the ground station. In this paper, an interior point-based method is used to recov

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Mohamed, Basma A., and Heba M. Afify. "MAMMOGRAM COMPRESSION TECHNIQUES USING HAAR WAVELET AND QUADTREE DECOMPOSITION-BASED IMAGE ENHANCEMENT." Biomedical Engineering: Applications, Basis and Communications 29, no. 05 (2017): 1750038. http://dx.doi.org/10.4015/s1016237217500387.

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Biomedical image compression plays an important role in the medical field. Mammograms are medical images used in the early detection of breast cancer. Mammogram image compression is a challenging task because these images contain information that occupies huge size for storage. The aim of image compression is to reduce the image size and the time taken for recovering the original image without any loss. In this paper, two different techniques of mammogram compression are introduced. The proposed algorithm includes two main steps. First, a preprocessing step is applied to enhance the image, and

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Malevé, Nicolas. "Lost in Compression." Media Theory 8, no. 1 (2024): 205–28. http://dx.doi.org/10.70064/mt.v8i1.1074.

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Recent developments of image generators have introduced a new point of contention in the already contested field of artificial intelligence: the ownership of images. In 2023 Getty Images sued the company Stability AI, accusing it of illegally appropriating photographs for the purpose of training its models. Analysing image generators and stock agencies as probabilistic systems, this text argues that their significant difference lies in their model of appropriation. Where Stability AI proceeds through direct appropriation, the stock agency proceeds through contractual appropriation using its do

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Avinash, Gopal B. "Image compression and data integrity in confocal microscopy." Proceedings, annual meeting, Electron Microscopy Society of America 51 (August 1, 1993): 206–7. http://dx.doi.org/10.1017/s0424820100146874.

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In confocal microscopy, one method of managing large data is to store the data in a compressed form using image compression algorithms. These algorithms can be either lossless or lossy. Lossless algorithms compress images without losing any information with modest compression ratios (memory for the original / memory for the compressed) which are usually between 1 and 2 for typical confocal 2-D images. However, lossy algorithms can provide higher compression ratios (3 to 8) at the expense of information content in the images. The main purpose of this study is to empirically demonstrate the use

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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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Chaturvedi, Soumya. "Different Type of Image Compression using Various techniques, Highlighting Segmentation based image Compression." International Journal for Research in Applied Science and Engineering Technology 10, no. 2 (2022): 171–77. http://dx.doi.org/10.22214/ijraset.2022.40207.

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Abstract: Image compression (IC) plays an important part in Digital Image Processing (DIP), it is as well very very essential for effective transmission and storing of images. Image Compression (IC), is basically recusing the size of an image and that too without adjusting the quality of the picture. It is kind of software with records pressure on digital Image. The objective is to lessen reiteration of the picture info for you to be accomplished of store or transmit information in a proficient shape. This paper gives review of kinds of images and its compression strategies. An image, in its g

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Shivanna, Gunasheela Keragodu, and Haranahalli Shreenivasamurthy Prasantha. "Two-dimensional satellite image compression using compressive sensing." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (2022): 311. http://dx.doi.org/10.11591/ijece.v12i1.pp311-319.

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Abstract:

Compressive sensing is receiving a lot of attention from the image processing research community as a promising technique for image recovery from very few samples. The modality of compressive sensing technique is very useful in the applications where it is not feasible to acquire many samples. It is also prominently useful in satellite imaging applications since it drastically reduces the number of input samples thereby reducing the storage and communication bandwidth required to store and transmit the data into the ground station. In this paper, an interior point-based method is used to recov

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Dr., Aziz Makandar Ms. Rekha Biradar. "Performance analysis of Medical Image Compression using DCT and FFT Transforms." LC International Journal of STEM 3, no. 4 (2023): 51–60. https://doi.org/10.5281/zenodo.7607120.

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There is a high demand for image compression since it reduces the computational time, which in turn reduces the storage and transmission costs. Image compression involves reducing excessive and irrelevant data while maintaining reasonable image quality. Image compression techniques such as the Discrete Cosine Transform (DCT) and Fast Fourier Transform (FFT) are the focus of this study. These tools were selected because of their wide application in image processing; one example is JPEG (Joint Photographic Experts Group), which uses DCT for compression. A comparison is made between DCT and FFT,

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Barrios, Yubal, Alfonso Rodríguez, Antonio Sánchez, et al. "Lossy Hyperspectral Image Compression on a Reconfigurable and Fault-Tolerant FPGA-Based Adaptive Computing Platform." Electronics 9, no. 10 (2020): 1576. http://dx.doi.org/10.3390/electronics9101576.

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This paper describes a novel hardware implementation of a lossy multispectral and hyperspectral image compressor for on-board operation in space missions. The compression algorithm is a lossy extension of the Consultative Committee for Space Data Systems (CCSDS) 123.0-B-1 lossless standard that includes a bit-rate control stage, which in turn manages the losses the compressor may introduce to achieve higher compression ratios without compromising the recovered image quality. The algorithm has been implemented using High-Level Synthesis (HLS) techniques to increase design productivity by raisin

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