To see the other types of publications on this topic, follow the link: Image compression.
Journal articles on the topic 'Image compression'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Image compression.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Saudagar, Abdul Khader Jilani. "Biomedical Image Compression Techniques for Clinical Image Processing." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 12 (October 19, 2020): 133. http://dx.doi.org/10.3991/ijoe.v16i12.17019.
Full textAbstract:
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 compression using neural network, fuzzy logic and neuro-fuzzy logic to preserve higher spectral representation to maintain finer edge information’s, and relational coding for inter band coefficients to achieve high compressions. The developed image coding model is evaluated over various quality factors. From the simulation results it is observed that the proposed image coding system can achieve efficient compression performance compared with existing block coding and JPEG coding approaches, even under resource constraint environments.
2
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 (October 19, 2019): 1532–44. http://dx.doi.org/10.1177/0020294019877508.
Full textAbstract:
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 presents an approach of Haar wavelet transform, discrete cosine transforms, and run length encoding techniques for advanced manufacturing processes with high image compression rates. These techniques work by converting an image (signal) into half of its length which is known as “detail levels”; then, the compression process is done. For simulation purposes of the proposed research, the images are segmented into 8 × 8 blocks and then inversed (decoded) operation is performed on the processed 8 × 8 block to reconstruct the original image. The same experiments were done on two other algorithms, that is, discrete cosine transform and run length encoding schemes. The proposed system is tested by comparing the results of all the three algorithms based on different images. The comparison among these techniques is drawn on the basis of peak signal to noise ratio and compression ratio. The results obtained from the experiments show that the Haar wavelet transform outperforms very well with an accuracy of 97.8% and speeds up the compression and decompression process of the image with no loss of information and quality of image. The proposed study can easily be implemented in industries for the compression of images. These compressed images are suggested for multiple purposes like image compression for metrology as measurement materials in advanced manufacturing processes, low storage and bandwidth requirements, and compressing multimedia data like audio and video formats.
3
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 (February 5, 2018): 126. http://dx.doi.org/10.14419/ijet.v7i1.7.10631.
Full textAbstract:
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.
4
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 (January 1, 1997): 43–50. http://dx.doi.org/10.1155/dte.4.43.
Full textAbstract:
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 widely used and representative method for compressing color still images, in order to determine the relationship between the degree of image compression and the color information in electronic endoscopic images. The acquired images were transferred to an image processor using an offline system. A total of 10 regions of interest (ROls) were selected, and red (R), green (G), and blue (B) images were obtained using different compression modes. From histograms generated for these images, mean densities of R, G, and B in each ROI were measured and analyzed. The results revealed that color changes were greater for B, which had the lowest density, than for R or G as the degree of compression was increased.
5
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 (March 31, 2018): 9–11. http://dx.doi.org/10.21276/ijircst.2018.6.2.1.
Full text
6
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 (February 2, 2012): 20–27. http://dx.doi.org/10.24297/ijct.v2i1.2608.
Full textAbstract:
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 very simpletype of iterative fractal. In our algorithm awavelet transform (quadrature mirror filterpyramid) is used to decompose an image intobands containing information from differentscales (spatial frequencies) and orientations. Theconditional probabilities between these differentscale bands are then determined, and used as thebasis for a predictive coder.We undertake a study of the performance offractal image compression. This paper focusesimportant features of compression of still images,including the extent to which the quality of imageis degraded by the process of compression anddecompression.The numerical experiment is doneby considering various types of images and byapplying fractal Image compression to compressan image. It was found that fractal yields betterresult as compared to other compressiontechniques. It provide better peak signal to noiseratio as compare to other techniques, but it takehigher encoding time.The numerical results arecalculated in Matlab.
7
Cardone, Barbara, Ferdinando Di Martino, and Salvatore Sessa. "Fuzzy Transform Image Compression in the YUV Space." Computation 11, no. 10 (October 1, 2023): 191. http://dx.doi.org/10.3390/computation11100191.
Full textAbstract:
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 compression is accomplished in the YUV space, so that better-quality compressed images can be obtained without increasing the execution time. The results of tests performed on a set of color images show that our color image compression method improves the quality of the decoded images with respect to the image compression algorithms JPEG, F1-transform on the RGB color space and F-transform on the YUV color space, regardless of the selected compression rate and with comparable CPU times.
8
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 (October 30, 2010): 105–18. http://dx.doi.org/10.31642/jokmc/2018/010209.
Full textAbstract:
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 and these results are obtained in fraction of seconds and thus to improve thequality of the reconstructed image. The promising results obtained concerning reconstructed images quality as well as preservation of significant image details, while, on the other hand achieving highcompression rates and better image quality while DB4 Wavelet Transformation higher compression rates ratio without kept for image quality .
9
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.
Full textAbstract:
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 important part of an image with more quality while the rest part of the image is not significantly degraded. The proposed algorithm shown to be successful in digital image compression where images are represented in the spatial and transform domains. This new algorithm were recommended for use in image compression.
10
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 (December 1, 2021): 012080. http://dx.doi.org/10.1088/1742-6596/2114/1/012080.
Full textAbstract:
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 Welch LZW. The proposed methodology maintains the quality of the reconstructed image. Macroscopic and quantitative experimental results on technical color images show that the proposed methodology gives reconstructed images with a high PSNR value compared to standard image compression techniques.
11
Di Martino, Ferdinando, and Salvatore Sessa. "A Multilevel Fuzzy Transform Method for High Resolution Image Compression." Axioms 11, no. 10 (October 13, 2022): 551. http://dx.doi.org/10.3390/axioms11100551.
Full textAbstract:
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 tiles, each of which is individually compressed using MF-tr; thereafter, the image is reconstructed by merging the decompressed tiles. Comparative tests performed on remote sensing images show that the proposed method provides better performance than MF-tr in terms of compression rate and CPU time. Moreover, comparison tests show that our method reconstructs the image with CPU times that are at least two times less than those obtained using the MF-tr algorithm.
12
Singh Samra, Hardeep. "Image Compression Techniques." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 2, no. 2 (April 30, 2012): 49–52. http://dx.doi.org/10.24297/ijct.v2i1.2616.
Full textAbstract:
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.
13
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 (January 1, 2022): 1–6. http://dx.doi.org/10.1166/jmihi.2022.3848.
Full textAbstract:
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 can be introduced to affect the BCS coefficients, degrading the reconstructed image’s quality. The performance of BCS at low compression ratios is also poor. To overcome these limitations, without dividing the image into blocks, the image matrix is considered as a whole and compressively sensed by segment based compressive sensing (SBCS). This is a novel strategy that is offered in this article, for efficient compression of digital color images at low compression ratios. Metrics of performance The peak signal to noise ratio (PSNR), the mean structural similarity index (MSSIM), and the colour perception metric delta E are computed and compared to those obtained using block-based compressive sensing (BBCS). The results show that SBCS performs better than BBCS.
14
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.
Full textAbstract:
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.
15
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 (June 30, 2022): 161–66. http://dx.doi.org/10.37391/ijeer.100219.
Full textAbstract:
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 optimization algorithm. Medical images are pre-processed using hybrid median filter to discard noise and then decomposed using integer wavelet transform. The proposed method employed modified grasshopper optimization algorithm to select the optimal coefficients for efficient compression and decompression. Four different imaging techniques, particularly magnetic resonance imaging, computed tomography, ultrasound, and X-ray, were used in a series of tests. The suggested method's compressing performance is proven by comparing it to well-known approaches in terms of mean square error, peak signal to noise ratio, and mean structural similarity index at various compression ratios. The findings showed that the proposed approach provided effective compression with high decompression image quality.
16
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 (October 1996): 1188–92. http://dx.doi.org/10.1177/154193129604002309.
Full textAbstract:
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 corresponding uncompressed images, as well as rated the acceptability of the compressed images for diagnostic purposes. The results indicate that subjective image quality and acceptability decreased with increasing compression levels; however, all images remained acceptable for diagnostic purposes. At high compression ratios, JPEG compressed images were judged less acceptable for diagnostic purposes than the wavelet compressed images. These results contribute to emerging system design guidelines for digital imaging workstations.
17
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 (February 9, 2018): 7–15. http://dx.doi.org/10.31598/sintechjournal.v1i1.179.
Full textAbstract:
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 decompression will be compared to its performance in the objective assessment such as ratio, compression time, MSE (Mean Square Error) and PNSR (Peak Signal to Noise Ratio). MSE and PSNR are used for quantitative image quality measurement for subjective assessment assisted by three experts who will compare the original image with the decompression image. Based on the results obtained from the objective assessment of compression performance of RLE algorithm showed the best performance by yielding ratio, time, MSE and PSNR respectively 86,92%, 3,11ms, 0 and 0db. For Huffman, the results can be 12.26%, 96.94ms, 0, and 0db respectively. While LZW results can be in sequence -63.79%, 160ms, 0.3 and 58.955db. For the results of the subjective assessment, the experts argued that all images can be analyzed well.
18
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 (July 8, 2018): 1332. http://dx.doi.org/10.14419/ijet.v7i3.12707.
Full textAbstract:
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 pseudo random number and pseudo random permutation. Then, encrypted images are compressed evaluated by the SVD method which encompasses the corresponding singular values and singular vectors. The performance evaluated on several images and the experimental results and security evaluation is given to validate the explained goals of high security and good compression performance.
19
Kaur, Harjit. "Image Compression Techniques with LZW method." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 1773–77. http://dx.doi.org/10.22214/ijraset.2022.39999.
Full textAbstract:
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. This review paper compares the different compression techniques. Keywords: lossy, lossless, image formats, compression techniques.
20
Dr. R. B. Dubey, Dr R. B. Dubey, and Parul Parul. "Visually Lossless JPEG2000 Image Compression." Indian Journal of Applied Research 3, no. 9 (October 1, 2011): 211–16. http://dx.doi.org/10.15373/2249555x/sept2013/66.
Full text
21
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.
Full textAbstract:
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, the deep network model is segmented into two parts: the encoding network and the decoding network. The weight parameters of the encoding network are deployed to the edge device for the compressed image in the sensor network. For high-quality reconstructed images, the weight parameters of the decoding network are deployed to the cloud system. Experimental results demonstrate that the proposed MS-CAE obtains a high signal-to-noise ratio (PSNR) for the details of the image, and the compression ratio at the same bit per pixel (bpp) is significantly higher than that of the compared image compression algorithms. It also indicates that the MS-CAE not only greatly relieves the pressure of the hardware system in sensor network but also effectively improves image transmission efficiency and solves the deployment problem of image monitoring in remote and energy-poor areas.
22
Sawaneh, Ibrahim Abdulai. "DWT Based Image Compression for Health Systems." Journal of Advance Research in Medical & Health Science (ISSN: 2208-2425) 4, no. 9 (September 30, 2018): 01–67. http://dx.doi.org/10.53555/nnmhs.v4i9.603.
Full textAbstract:
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 presents data compression implementation for healthcare systems using a proposed scheme of discrete wavelet transform (DWT), Fourier transform (FT) and Fast Fourier transform with capacity of compressing and recovering medical image data without data loss. Healthcare images such as those of human heart and brain need fast transmission for reliable and efficient result. Using DWT which has optimal reconstruction quality greatly improves compression. A representation of enabling innovations in communication technologies with big data for health monitoring is achievable through effective data compression techniques. Our experimental implementation shows that using Haar wavelet with parametric determination of MSE and PSNR solve our aims. Many imaging techniques were also deployed to further ascertain DWT method’s efficiency such as image compression and image de-noising. The proposed compression of medical image was excellent. It is essential to reduce the size of data sets by employing compression procedures to shrink storage space, reduce transmission rate, and limit massive energy usage in health monitoring systems. The motivation for this work was to implement compression method to modify traditional healthcare platform to lower file size, and reduce cost of operation. Image compression aims at reconstructing images from extensively lesser estimations than were already thought necessary in relations with non-zero coefficients. Rationally, fewer well-chosen interpretations is adequate to reproduce the new sample exactly as the source image. We look at DWT to implement our compression method.
23
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.
Full textAbstract:
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 image compression, including the developed coding algorithm, with the JPEG method and the method based on the wavelet transform is carried out.
24
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.
Full textAbstract:
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 techniques still need improvement to overcome issues, challenges and its limitations. Hence in this research work a hybrid method which combines securing image using RSA, hill cipher and 2bit rotation and compressing of images using lossless compression algorithm has been proposed. This method compared to execution time of existing method. This method secures the image and reduces the size of the image for data communication over the internet. This method is suitable for various applications uses images like remote sensing, medical and Spatio-temporal.
25
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.
Full textAbstract:
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 improvement by applying anisotropic interpolation to edge enhancement for its ability to satisfy the dispersed data of the propagation process, which leads to faster compression due to concern for optimum quality rather than fast algorithms. The test images for these patterns showed a discrepancy in the image resolution when the compression coefficient was increased, as the results using three types of image compression methods proved a clear superiority when using “partial differential equations (PDE)”.
26
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 (April 3, 2023): 11–16. http://dx.doi.org/10.36023/ujrs.2023.10.1.231.
Full textAbstract:
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 quality of a compressed image is closer to the corresponding noise-free image than the quality of the original image according to a chosen quality metric. In this case, it is worth compressing this image in the OOP or its closest neighborhood. These peculiarities have been earlier studied and their positive impact on image quality improvement has been demonstrated. Filtering of noisy images due to lossy compression is not perfect. Because of this, it is worth checking can additional quality improvement be reached using such an approach as post-filtering. In this study, we attempt to answer the questions: “is it worth to post-filter an image after lossy compression, especially in OOP’s neighborhood? And what benefit can it bring in the sense of image quality?”. The study is carried out for better portable graphics (BPG) coder and the DCT-based filter focusing mainly on one-component (grayscale) images. The quality of images is characterized by several metrics such as PSNR, PSNR-HVS-M, and FSIM. Possible image quality increasing via post-filtering is demonstrated and the recommendations for filter parameter setting are given.
27
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.
Full textAbstract:
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, and visually simple images primarily composed of homogeneous regions; establishing image quality evaluation metrics and assessing their performance across different encoder compression parameters; choosing one or multiple metrics that distinctly identify the position of the optimal operational point; and providing recommendations based on the attained results regarding the compression of medical images corrupted by Poisson noise using a BPG encoder, with the aim of maximizing the restored image’s quality resemblance to the original. The employed methods encompass image quality assessment techniques employing MSE, PSNR, MSSIM, and PSNR-HVS-M metrics, as well as software modeling in Python without using the built-in Poisson noise generator. The ensuing results indicate that optimal operational points (OOP) can be discerned for all these metrics when the compressed image quality surpasses that of the corresponding original image, accompanied by a sufficiently high compression ratio. Moreover, striking a suitable balance between the compression ratio and image quality leads to partial noise reduction without introducing notable distortions in the compressed image. This study underscores the significance of employing appropriate metrics for evaluating the quality of compressed medical images and provides insights into determining the compression parameter Q to attain the BPG encoder’s optimal operational point for specific images. Conclusions. The scientific novelty of the findings encompasses the following: 1) the capability of all metrics to determine the OOP for images of moderate visual complexity or those dominated by homogeneous areas; MSE and PSNR metrics demonstrating superior results for images rich in textures and edges; 2) the research highlights the dependency of Q in the OOP on the average image intensity, which can be reasonably established for a given image earmarked for compression based on our outcomes. The compression ratios for images compressed at the OOP are sufficiently high, further substantiating the rationale for compressing images in close proximity to the OOP.
28
Ma, Shaowen. "Comparison of image compression techniques using Huffman and Lempel-Ziv-Welch algorithms." Applied and Computational Engineering 5, no. 1 (June 14, 2023): 793–801. http://dx.doi.org/10.54254/2755-2721/5/20230705.
Full textAbstract:
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 are analysed. The results illustrated that when the source image files are less than 300kb, the Compression Ratio (CR) of Huffman algorithm was better than that of LZW algorithm. However, for Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Bits Per Pixel (BPP), which are used to represent the compressed images qualities, LZW algorithm gave more satisfactory results.
29
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 (February 28, 2022): 171–77. http://dx.doi.org/10.22214/ijraset.2022.40207.
Full textAbstract:
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 genuine form, conveys big extent of data which requiress no longer finest large quantity of memory provisions for its garage but moreover causes difficult transmission over limited bandwidth channel. So, one of the acute factors for picture storage space or transmission over any exchange media is Image Compression. Image Compression makes it possible for increasing file sizes of practicable, storable and communicable dimensions. Keywords: Image Compression; segmentation based image compression component; formatting; Lossless compression; Lossy compression; techniques.
30
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.
Full textAbstract:
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 of lossy compression techniques to images obtained from a confocal microscope while retaining the qualitative and quantitative image integrity under certain criteria.A fluorescent pollen specimen was imaged using ODYSSEY, a real-time laser scanning confocal microscope from NORAN Instruments, Inc. The images (128 by 128) consisted of a single frame (scanned in 33ms), a 4-frame average, a 64-frame average and an edge-preserving smoothed image of the single frame.
31
Syuhada, Ibnu. "Implementasi Algoritma Arithmetic Coding dan Sannon-Fano Pada Kompresi Citra PNG." TIN: Terapan Informatika Nusantara 2, no. 9 (February 25, 2022): 527–32. http://dx.doi.org/10.47065/tin.v2i9.1027.
Full textAbstract:
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 ratio of 62.88% and a Shannon-Fano compression ratio of 61.73%, then Arithmetic Coding the average speed in image compression is 0.072449 seconds and Shannon-Fano 0.077838 second. Then the Shannon-Fano algorithm has an average speed for decompression of 0.028946 seconds and the Arithmetic Coding algorithm 0.034169 seconds. The decompressed image on the Arithmetic Coding and Shannon-Fano algorithm is in accordance with the original image. It can be concluded from the test results that the Arithmetic Coding algorithm is more efficient in compressing *.png images than the Shannon-Fano algorithm, although in terms of decompression Shannon-Fanose is a little faster compared to Arithmetic Coding.
32
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 (September 30, 2021): 99. http://dx.doi.org/10.24843/jik.2021.v14.i02.p04.
Full textAbstract:
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 is in the form of an analysis of JPEG compression techniques with various types of images. The method of measuring the compression results uses the MSE and PSNR methods. Meanwhile, to determine the percentage level of compression using the compression ratio calculation. The average ratio for JPEG compression was 0.08605, the compression rate was 91.39%. The average compression ratio for the DWT method was 0.133090833, the compression rate was 86.69%. The average compression ratio of the SVD method was 0.101938833 and the compression rate was 89.80%.
33
Barrios, Yubal, Alfonso Rodríguez, Antonio Sánchez, Arturo Pérez, Sebastián López, Andrés Otero, Eduardo de la Torre, and Roberto Sarmiento. "Lossy Hyperspectral Image Compression on a Reconfigurable and Fault-Tolerant FPGA-Based Adaptive Computing Platform." Electronics 9, no. 10 (September 26, 2020): 1576. http://dx.doi.org/10.3390/electronics9101576.
Full textAbstract:
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 raising the abstraction level. The proposed lossy compression solution is deployed onto ARTICo3, a dynamically reconfigurable multi-accelerator architecture, obtaining a run-time adaptive solution that enables user-selectable performance (i.e., load more hardware accelerators to transparently increase throughput), power consumption, and fault tolerance (i.e., group hardware accelerators to transparently enable hardware redundancy). The whole compression solution is tested on a Xilinx Zynq UltraScale+ Field-Programmable Gate Array (FPGA)-based MPSoC using different input images, from multispectral to ultraspectral. For images acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), the proposed implementation renders an execution time of approximately 36 s when 8 accelerators are compressing concurrently at 100 MHz, which in turn uses around 20% of the LUTs and 17% of the dedicated memory blocks available in the target device. In this scenario, a speedup of 15.6× is obtained in comparison with a pure software version of the algorithm running in an ARM Cortex-A53 processor.
34
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.
Full textAbstract:
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 of the CCN model as regularization; Therefore, the CCN convolution kernels learned by training over the VOC and COCO image set can be used for data embedding and image compression. By reusing the convolution operation, no extra computational overhead is required for image compression. As a result, the CCN is 3.1 to 5.0 fold more efficient than the conventional approaches. In our experiments, the CCN achieved 78.1 mAP for object detection and 3.0 dB to 5.2 dB higher PSNR for image compression than the examined compressive sensing approaches.
35
MARTIN, C. E., and S. A. CURTIS. "Fractal image compression." Journal of Functional Programming 23, no. 6 (November 2013): 629–57. http://dx.doi.org/10.1017/s095679681300021x.
Full textAbstract:
AbstractThis paper describes some experiences of using fractal image compression as the subject of an assignment for a functional programming course using Haskell. The students were fascinated by the reproduction of images from their encodings and engaged well with the exercise which involved only elementary functional programming techniques.
36
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 (February 1, 2022): 311. http://dx.doi.org/10.11591/ijece.v12i1.pp311-319.
Full textAbstract:
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 recover the entire satellite image from compressive sensing samples. The compression results obtained are compared with the compression results from conventional satellite image compression algorithms. The results demonstrate the increase in reconstruction accuracy as well as higher compression rate in case of compressive sensing-based compression technique.
37
Mateika, Darius, and Romanas Martavicius. "ANALYSIS OF THE COMPRESSION RATIO AND QUALITY IN AERIAL IMAGES." Aviation 11, no. 4 (December 31, 2007): 24–28. http://dx.doi.org/10.3846/16487788.2007.9635973.
Full textAbstract:
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 for the aerial image compression problem is proposed and the compression format most suitable for aerial images is specified.
38
Khaleel, Shahbaa. "Image Compression Using Swarm Intelligence." International Journal of Intelligent Engineering and Systems 14, no. 1 (February 28, 2021): 257–69. http://dx.doi.org/10.22266/ijies2021.0228.25.
Full textAbstract:
As a result of the development in multimedia technology and direct dealing with it in social media, it has led to interest in the techniques of compacting color images because of their importance at present. Since image compression enables the representation of color image data with the fewest number of bits, which reduces transmission time in the network and increases transmission speed. To ensure the compression process is performed without loss of data, the lossless compression methods are used because no data is lost during the compression process. In this research, a new system was presented to compress the color images with efficiency and high quality. Where the swarm intelligent methods were used, as well as hybridizing it with fuzzy using the Gustafson kessel fuzzy method to improve the clustering process and create new clustering methods with fuzzy swarm intelligence to obtain the best results. Swarm algorithms were used to perform the process of clustering the image data to be compressed and then obtaining a clustered data for this image data. In contrast, a lossless compression method was used to perform the encoding of this clustered data where the huffman method was used for encoding. Four methods were applied in this research to different color and lighting images. The PSO swarm intelligent was used, which in turn was hybridized with the Gustafson kessel fuzzy method to produce a new method for fuzzy particle swarm (FPSO), as well as the grey wolf optimization method GWO, which was hybridized with Gustafson kessel and obtained a new method, which is the fuzzy grey wolf optimizer FGWO, and the results were graded efficiently from the first to the fourth method, where the FGWO method with the huffman was the most efficient depending on the standards measurement that were calculated for all methods, the compression ratio was high in this new method, in addition to the standards of MSE, RMSE, PSNR, etc. among the important measurements of the compressing process.
39
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 (October 2017): 1750038. http://dx.doi.org/10.4015/s1016237217500387.
Full textAbstract:
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 then a compression algorithm is applied to the enhanced image. The algorithm is tested using 322 mammogram images from the online MIAS database. Three parameters are used to evaluate the performance of the compression techniques; compression ratio (CR), Peak Signal to Noise Ratio (PSNR) and processing time. According to the results, Haar wavelet-based compression for enhanced images is better in terms of CR of 26.25% and PSNR of 47.27[Formula: see text]dB.
40
Azeez, Nassr, and Inas Al-Taie. "Color Image Compression Using Wavelet Compression and Zero-MeanCoding." Iraqi Journal of Science 53, no. 4Appendix (April 26, 2024): 924–29. http://dx.doi.org/10.24996/iraqijournalofscience.v53i4appendix.12945.
Full textAbstract:
Thousands pictures require a very large amount of storage. So differenttechniques of digital image compression is used to reduce the storage requirementsfor these images. In this paper an adaptive compression method applied on colorimage. Firstly the color image is transformed to the RGB system, and then convertsthe color data to a luminance/chrominance color space (YIQ).The adaptivecompression method is applied on Y. The image may contain a uniform region andedge region; the uniform regions compress depending on zero-mean coding and theedge region compress depending on Daubechies wavelet transform, then returnedthe final image to RGB system. The proposed algorithms has many advantageswhich make it very efficient, these are, low bit rate, low computational complexity,fast processing and edge preservation with good reconstructed image quality.
41
Bulkunde, Mr Vaibhav Vijay, Mr Nilesh P. Bodne, and Dr Sunil Kumar. "Implementation of Fractal Image Compression on Medical Images by Different Approach." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (June 30, 2019): 398–400. http://dx.doi.org/10.31142/ijtsrd23768.
Full text
42
Brysina, Iryna Victorivna, and Victor Olexandrovych Makarichev. "DISCRETE ATOMIC COMPRESSION OF DIGITAL IMAGES: ALMOST LOSSLESS COMPRESSION." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 1 (March 23, 2019): 29–36. http://dx.doi.org/10.32620/reks.2019.1.03.
Full textAbstract:
In this paper, we consider the problem of digital image compression with high requirements to the quality of the result. Obviously, lossless compression algorithms can be applied. Since lossy compression provides a higher compression ratio and, hence, higher memory savings than lossless compression, we propose to use lossy algorithms with settings that provide the smallest loss of quality. The subject matter of this paper is almost lossless compression of full color 24-bit digital images using the discrete atomic compression (DAC) that is an algorithm based on the discrete atomic transform. The goal is to investigate the compression ratio and the quality loss indicators such as uniform (U), root mean square (RMS) and peak signal to noise ratio (PSNR) metrics. We also study the distribution of the difference between pixels of the original image and the corresponding pixels of the reconstructed image. In this research, the classic test images and the classic aerial images are considered. U-metric, which is highly dependent on even minor local changes, is considered as the major metric of quality loss. We solve the following tasks: to evaluate memory savings and loss of quality for each test image. We use the methods of digital image processing, atomic function theory, and approximation theory. The computer program "Discrete Atomic Compression: User Kit" with the mode "Almost Lossless Compression" is used to obtain results of the DAC processing of test images. We obtain the following results: 1) the difference between the smallest and the largest loss of quality is minor; 2) loss of quality is quite stable and predictable; 3) the compression ratio depends on the smoothness of the color change (the smallest and the largest values are obtained when processing the test images with the largest and the smallest number of small details in the image, respectively); 4) DAC provides 59 percent of memory savings; 5) ZIP-compression of DAC-files, which contain images compressed by DAC, is efficient. Conclusions: 1) the almost lossless compression mode of DAC provides sufficiently stable values of the considered quality loss metrics; 2) DAC provides relatively high compression ratio; 3) there is a possibility of further optimization of the DAC algorithm; 4) further research and development of this algorithm are promising.
43
Fidler, A., B. Likar, F. Pernus, and U. Skaleric. "Impact of JPEG lossy image compression on quantitative digital subtraction radiography." Dentomaxillofacial Radiology 31, no. 2 (March 2002): 106–12. http://dx.doi.org/10.1038/sj/dmfr/4600670.
Full textAbstract:
OBJECTIVES The aim of the study was to evaluate the impact of JPEG lossy image compression on the estimation of alveolar bone gain by quantitative digital subtraction radiography (DSR). METHODS Nine dry domestic pig mandible posterior segments were radiographed three times ('Baseline', 'No change', and 'Gain') with standardized projection geometry. Bone gain was simulated by adding artificial bone chips (1, 4, and 15 mg). Images were either compressed before or after registration. No change areas in compressed and subtracted 'No change-Baseline' images and bone gain volumes in compressed and subtracted 'Gain-Baseline' images were calculated and compared to the corresponding measurements performed on original subtracted images. RESULTS Measurements of no change areas ('No change-Baseline') were only slightly affected by compressions down to JPEG 50 (J50) applied either before or after registration. Simulated gain of alveolar bone ('Gain-Baseline') was underestimated when compression before registration was performed. The underestimation was bigger when small bone chips of 1 mg were measured and when higher compression rates were used. Bone chips of 4 and 15 mg were only slightly underestimated when using J90, J70, and J50 compressions before registration. CONCLUSIONS Lossy JPEG compression does not affect the measurements of no change areas by DSR. Images undergoing subtraction should be registered before compression and if so, J90 compression with a compression ratio of 1:7 can be used to detect and measure 4 mg and larger bone gain.
44
LOW, YIN FEN, and ROSLI BESAR. "WAVELET-BASED MEDICAL IMAGE COMPRESSION USING EZW: OBJECTIVE AND SUBJECTIVE EVALUATIONS." Journal of Mechanics in Medicine and Biology 04, no. 01 (March 2004): 93–110. http://dx.doi.org/10.1142/s0219519404000795.
Full textAbstract:
Recently, the wavelet transform has emerged as a cutting edge technology within the field of image compression research. Wavelet methods involve overlapping transforms with varying-length basis functions. This overlapping nature of the transform alleviates blocking artifacts, while the multi-resolution character of the wavelet decomposition leads to superior energy compaction and perceptual quality of the decompressed image. Embedded zerotree wavelet (EZW) coder is the first algorithm to show the full power of wavelet-based image compression. The main purpose of this paper is to investigate the impact and quality of orthogonal wavelet filter in compressing medical image by using EZW. Meanwhile, we also look into the effect of the level of wavelet decomposition towards compression efficiency. The wavelet filters used are Haar and Daubechies. The compression simulations are done on three modalities of medical images. The objective (based on PSNR) and subjective (perceived image quality) results of these simulations are presented.
45
Jittawiriyanukoon, Chanintorn, and Vilasinee Srisarkun. "Evaluation of graphic effects embedded image compression." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (December 1, 2020): 6606. http://dx.doi.org/10.11591/ijece.v10i6.pp6606-6617.
Full textAbstract:
A fundamental factor of digital image compression is the conversion processes. The intention of this process is to understand the shape of an image and to modify the digital image to a grayscale configuration where the encoding of the compression technique is operational. This article focuses on an investigation of compression algorithms for images with artistic effects. A key component in image compression is how to effectively preserve the original quality of images. Image compression is to condense by lessening the redundant data of images in order that they are transformed cost-effectively. The common techniques include discrete cosine transform (DCT), fast Fourier transform (FFT), and shifted FFT (SFFT). Experimental results point out compression ratio between original RGB images and grayscale images, as well as comparison. The superior algorithm improving a shape comprehension for images with grahic effect is SFFT technique.
46
Sanderson, H. "Image segmentation for compression of images and image sequences." IEE Proceedings - Vision, Image, and Signal Processing 142, no. 1 (1995): 15. http://dx.doi.org/10.1049/ip-vis:19951681.
Full text
47
Sridhar, Chethana, Piyush Kumar Pareek, R. Kalidoss, Sajjad Shaukat Jamal, Prashant Kumar Shukla, and Stephen Jeswinde Nuagah. "Optimal Medical Image Size Reduction Model Creation Using Recurrent Neural Network and GenPSOWVQ." Journal of Healthcare Engineering 2022 (February 26, 2022): 1–8. http://dx.doi.org/10.1155/2022/2354866.
Full textAbstract:
Medical diagnosis is always a time and a sensitive approach to proper medical treatment. Automation systems have been developed to improve these issues. In the process of automation, images are processed and sent to the remote brain for processing and decision making. It is noted that the image is written for compaction to reduce processing and computational costs. Images require large storage and transmission resources to perform their operations. A good strategy for pictures compression can help minimize these requirements. The question of compressing data on accuracy is always a challenge. Therefore, to optimize imaging, it is necessary to reduce inconsistencies in medical imaging. So this document introduces a new image compression scheme called the GenPSOWVQ method that uses a recurrent neural network with wavelet VQ. The codebook is built using a combination of fragments and genetic algorithms. The newly developed image compression model attains precise compression while maintaining image accuracy with lower computational costs when encoding clinical images. The proposed method was tested using real-time medical imaging using PSNR, MSE, SSIM, NMSE, SNR, and CR indicators. Experimental results show that the proposed GenPSOWVQ method yields higher PSNR SSIMM values for a given compression ratio than the existing methods. In addition, the proposed GenPSOWVQ method yields lower values of MSE, RMSE, and SNR for a given compression ratio than the existing methods.
48
Ghodhbani, Refka, Taoufik Saidani, Layla Horrigue, Asaad M. Algarni, and Muteb Alshammari. "An FPGA Accelerator for Real Time Hyperspectral Images Compression based on JPEG2000 Standard." Engineering, Technology & Applied Science Research 14, no. 2 (April 2, 2024): 13118–23. http://dx.doi.org/10.48084/etasr.6853.
Full textAbstract:
Lossless hyperspectral images have the advantage of reducing the data size, hence saving on storage and transmission costs. This study presents a dynamic pipeline hardware design for compressing and decompressing images using the Joint Photographic Experts Group-Lossless (JPEG2000) algorithm. The proposed architecture was specifically tailored for implementation on a Field Programmable Gate Array (FPGA) to accomplish efficient image processing. The introduction of a pipeline pause mechanism effectively resolves the issue of coding errors deriving from parameter modifications. Bit-plane coding was employed to enhance the efficacy of image coding calculations, leading to a reduction of parameter update delays. However, the context and decision creation procedure were streamlined, resulting in a significant enhancement in throughput. A hardware module utilizing the parallel block compression architecture was developed for JPEG2000 compression/decompression, allowing for configurable block size and bringing about enhanced image, compression/decompression, throughput, and reduced times. Verification results were obtained by implementing the proposed JPEG 2000 compression on a Zynq-7000 system-on-chip. The purpose of this system was to enable on-board satellite processing of hyperspectral image cubes with a specific focus on achieving lossless compression. The proposed architecture outperformed previous approaches by using fewer resources and achieving a higher compression ratio and clock frequency.
49
Zhang, Milin, and Amine Bermak. "CMOS Image Sensor with On-Chip Image Compression: A Review and Performance Analysis." Journal of Sensors 2010 (2010): 1–17. http://dx.doi.org/10.1155/2010/920693.
Full textAbstract:
Demand for high-resolution, low-power sensing devices with integrated image processing capabilities, especially compression capability, is increasing. CMOS technology enables the integration of image sensing and image processing, making it possible to improve the overall system performance. This paper reviews the current state of the art in CMOS image sensors featuring on-chip image compression. Firstly, typical sensing systems consisting of separate image-capturing unit and image-compression processing unit are reviewed, followed by systems that integrate focal-plane compression. The paper also provides a thorough review of a new design paradigm, in which image compression is performed during the image-capture phase prior to storage, referred to as compressive acquisition. High-performance sensor systems reported in recent years are also introduced. Performance analysis and comparison of the reported designs using different design paradigm are presented at the end.
50
Agarwal, Ruchi, C. S. Salimath, and Khursheed Alam. "Multiple Image Compression in Medical Imaging Techniques using Wavelets for Speedy Transmission and Optimal Storage." Biomedical and Pharmacology Journal 12, no. 1 (March 19, 2019): 183–98. http://dx.doi.org/10.13005/bpj/1627.
Full textAbstract:
Multiple image compression using wavelet based methods including Discrete Wavelet Transform (DWT) through sub band coding (SBC) and decoding are reviewed for their comparative study. True color image compression measuring parameters like compression ratio (CR), peak to signal noise ratio (PSNR), mean square error (MSE), bits per pixel (BPP) are computed using MATLAB code for each algorithm employed. Gray scale image like Magnetic Resonance Imaging (MRI) is chosen for wavelet transform to achieve encoding and decoding using multiple wavelet families and resolutions to examine their relative merits and demerits. Our main objective is to establish advantages of multiple compression techniques (compressions using multiresolution) helpful in transmitting bulk of compressed medical images via different gadgets facilitating early detection and diagnosis followed by treatments or referrals to specialists residing in different parts of the world. Contemporary compression techniques based on wavelet transform can serve as revolutionary idea in medical field for the overall benefit of humanity.