Готові списки джерел за темами / Image compression level

Добірка наукової літератури з теми "Image compression level"

Автор: Grafiati
Опубліковано: 30 травня 2022
Оновлено: 31 травня 2022

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Статті в журналах з теми "Image compression level"

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Celik, Mehmet Utku, Gaurav Sharma, and A. Murat Tekalp. "Gray-level-embedded lossless image compression." Signal Processing: Image Communication 18, no. 6 (July 2003): 443–54. http://dx.doi.org/10.1016/s0923-5965(03)00023-7.

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2

Di Martino, Ferdinando, and Salvatore Sessa. "Multi-level fuzzy transforms image compression." Journal of Ambient Intelligence and Humanized Computing 10, no. 7 (August 17, 2018): 2745–56. http://dx.doi.org/10.1007/s12652-018-0971-4.

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3

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.

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Анотація:
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%.
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4

Marcelo, Alvin, Paul Fontelo, Miguel Farolan, and Hernani Cualing. "Effect of Image Compression on Telepathology." Archives of Pathology & Laboratory Medicine 124, no. 11 (November 1, 2000): 1653–56. http://dx.doi.org/10.5858/2000-124-1653-eoicot.

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Abstract Context.—For practitioners deploying store-and-forward telepathology systems, optimization methods such as image compression need to be studied. Objective.—To determine if Joint Photographic Expert Group (JPG or JPEG) compression, a lossy image compression algorithm, negatively affects the accuracy of diagnosis in telepathology. Design.—Double-blind, randomized, controlled trial. Setting.—University-based pathology departments. Participants.—Resident and staff pathologists at the University of Illinois, Chicago, and University of Cincinnati, Cincinnati, Ohio. Intervention.—Compression of raw images using the JPEG algorithm. Main Outcome Measures.—Image acceptability, accuracy of diagnosis, confidence level of pathologist, image quality. Results.—There was no statistically significant difference in the diagnostic accuracy between noncompressed (bit map) and compressed (JPG) images. There were also no differences in the acceptability, confidence level, and perception of image quality. Additionally, rater experience did not significantly correlate with degree of accuracy. Conclusions.—For providers practicing telepathology, JPG image compression does not negatively affect the accuracy and confidence level of diagnosis. The acceptability and quality of images were also not affected.
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5

Gollu, Vimala Kumari, Ganta Usha Sravani, Mandru Sunil Prakash, and Ganta Srikanth. "Pipeline of Optimization Techniques for Multi-Level Thresholding in Medical Image Compression Using 2D Histogram." Traitement du Signal 38, no. 4 (August 31, 2021): 993–1006. http://dx.doi.org/10.18280/ts.380409.

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In recent times, medical scan images are crucial for accurate diagnosis by medical professionals. Due to the increasing size of the medical images, transfer and storage of images require huge bandwidth and storage space, and hence needs compression. In this paper, multilevel thresholding using 2-D histogram is proposed for compressing the images. In the proposed work, hybridization of optimization techniques viz., Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Symbiotic Organisms Search (SOS) is used to optimize the multilevel thresholding process by assuming the Renyi entropy as an objective function. Meaningful clusters are possible with optimal threshold values, which lead to better image compression. For performance evaluation, the proposed work has been examined on six Magnetic Resonance (MR) images of brain and compared with individual optimization techniques as well as with 1-D histogram. Recent study reveals that peak signal to noise ratio (PSNR) fail in measuring the visual quality of reconstructed image because of mismatch with the objective mean opinion scores (MOS). So, we incorporate weighted PSNR (WPSNR) and visual PSNR (VPSNR) as performance measuring parameters of the proposed method. Experimental results reveal that hGAPSO-SOS method can be accurately and efficiently used in problem of multilevel thresholding for image compression.
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Moëll, Mattias K., and Minoru Fujita. "FOURIER TRANSFORM METHODS IN IMAGE ANALYSIS OF COMPRESSION WOOD AT THE CELLULAR LEVEL." IAWA Journal 25, no. 3 (2004): 311–24. http://dx.doi.org/10.1163/22941932-90000368.

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Анотація:
Compression wood affects the overall quality of construction timber and paper quality. We have investigated the microscopic features of lumen shape and tracheid shape for compression wood studies and detection in softwoods. In this paper, we describe a method for directly analyzing tracheid and lumen shape over an entire image. The method uses the Fast Fourier Transform (FFT) and reduces the two-dimensional image data to one-dimensional data, from which lumen and tracheid shape can be evaluated. We illustrate the method by comparison of compression wood images to normal wood images. The results of detecting severe compression wood were successful, while the detection of weak compression wood was not satisfactory.
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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.

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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 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.
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SinghKatre, Surjeet. "Image Compression based on 4 Level AMBTC." International Journal of Computer Applications 95, no. 4 (June 18, 2014): 7–9. http://dx.doi.org/10.5120/16580-6275.

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Sayed, Mohamed H., and Talaat M. Wahby. "Multi-Level Image Steganography Using Compression Techniques." International Journal of Computer Applications Technology and Research 6, no. 11 (November 4, 2017): 441–50. http://dx.doi.org/10.7753/ijcatr0611.1001.

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Li Donghui, 李东晖. "Context-Based Bi-Level Speckle Image Compression." Laser & Optoelectronics Progress 55, no. 12 (2018): 121010. http://dx.doi.org/10.3788/lop55.121010.

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Дисертації з теми "Image compression level"

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Bejile, Brian. "Bi-level lossless compression techniques." Diss., Connect to the thesis, 2004. http://hdl.handle.net/10066/1481.

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Guo, Jianghong. "Analysis and Design of Lossless Bi-level Image Coding Systems." Thesis, University of Waterloo, 2000. http://hdl.handle.net/10012/845.

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Анотація:
Lossless image coding deals with the problem of representing an image with a minimum number of binary bits from which the original image can be fully recovered without any loss of information. Most lossless image coding algorithms reach the goal of efficient compression by taking care of the spatial correlations and statistical redundancy lying in images. Context based algorithms are the typical algorithms in lossless image coding. One key probelm in context based lossless bi-level image coding algorithms is the design of context templates. By using carefully designed context templates, we can effectively employ the information provided by surrounding pixels in an image. In almost all image processing applications, image data is accessed in a raster scanning manner and is treated as 1-D integer sequence rather than 2-D data. In this thesis, we present a quadrisection scanning method which is better than raster scanning in that more adjacent surrounding pixels are incorporated into context templates. Based on quadrisection scanning, we develop several context templates and propose several image coding schemes for both sequential and progressive lossless bi-level image compression. Our results show that our algorithms perform better than those raster scanning based algorithms, such as JBIG1 used in this thesis as a reference. Also, the application of 1-D grammar based codes in lossless image coding is discussed. 1-D grammar based codes outperform those LZ77/LZ78 based compression utility software for general data compression. It is also effective in lossless image coding. Several coding schemes for bi-level image compression via 1-D grammar codes are provided in this thesis, especially the parallel switching algorithm which combines the power of 1-D grammar based codes and context based algorithms. Most of our results are comparable to or better than those afforded by JBIG1.
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3

Курлан, О. О., та С. В. Омельченко. "Аналіз методів компресії зображень формату JPEG для підвищення рівня стиснення". Thesis, ХНУРЕ, 2021. https://openarchive.nure.ua/handle/document/16486.

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Анотація:
The fundamental theoretical techniques of image compression are considered. Their comparative analysis was performed. Approaches of images compression level increasing presented in JPEG format will be investigated. In the practical part, there will be a software implementation of the investigated approaches and a comparative characteristic.
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4

Trisiripisal, Phichet. "Image Approximation using Triangulation." Thesis, Virginia Tech, 2003. http://hdl.handle.net/10919/33337.

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An image is a set of quantized intensity values that are sampled at a finite set of sample points on a two-dimensional plane. Images are crucial to many application areas, such as computer graphics and pattern recognition, because they discretely represent the information that the human eyes interpret. This thesis considers the use of triangular meshes for approximating intensity images. With the help of the wavelet-based analysis, triangular meshes can be efficiently constructed to approximate the image data. In this thesis, this study will focus on local image enhancement and mesh simplification operations, which try to minimize the total error of the reconstructed image as well as the number of triangles used to represent the image. The study will also present an optimal procedure for selecting triangle types used to represent the intensity image. Besides its applications to image and video compression, this triangular representation is potentially very useful for data storage and retrieval, and for processing such as image segmentation and object recognition.
Master of Science
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5

Husberg, Björn. "A Portable DARC Fax Service." Thesis, Linköping University, Department of Electrical Engineering, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1373.

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DARC is a technique for data broadcasting over the FM radio network. Sectra Wireless Technologies AB has developed a handheld DARC receiver known as the Sectra CitySurfer. The CitySurfer is equipped with a high-resolution display along with buttons and a joystick that allows the user to view and navigate through various types of information received over DARC.

Sectra Wireless Technologies AB has, among other services, also developed a paging system that enables personal message transmission over DARC. The background of this thesis is a wish to be able to send fax documents using the paging system and to be able to view received fax documents in the CitySurfer.

The presented solution is a central PC-based fax server. The fax server is responsible for receiving standard fax transmissions and converting the fax documents before redirecting them to the right receiver in the DARC network. The topics discussed in this thesis are fax document routing, fax document conversion and fax server system design.

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Grah, Joana Sarah. "Mathematical imaging tools in cancer research : from mitosis analysis to sparse regularisation." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/273243.

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This dissertation deals with customised image analysis tools in cancer research. In the field of biomedical sciences, mathematical imaging has become crucial in order to account for advancements in technical equipment and data storage by sound mathematical methods that can process and analyse imaging data in an automated way. This thesis contributes to the development of such mathematically sound imaging models in four ways: (i) automated cell segmentation and tracking. In cancer drug development, time-lapse light microscopy experiments are conducted for performance validation. The aim is to monitor behaviour of cells in cultures that have previously been treated with chemotherapy drugs, since atypical duration and outcome of mitosis, the process of cell division, can be an indicator of successfully working drugs. As an imaging modality we focus on phase contrast microscopy, hence avoiding phototoxicity and influence on cell behaviour. As a drawback, the common halo- and shade-off effect impede image analysis. We present a novel workflow uniting both automated mitotic cell detection with the Hough transform and subsequent cell tracking by a tailor-made level-set method in order to obtain statistics on length of mitosis and cell fates. The proposed image analysis pipeline is deployed in a MATLAB software package called MitosisAnalyser. For the detection of mitotic cells we use the circular Hough transform. This concept is investigated further in the framework of image regularisation in the general context of imaging inverse problems, in which circular objects should be enhanced, (ii) exploiting sparsity of first-order derivatives in combination with the linear circular Hough transform operation. Furthermore, (iii) we present a new unified higher-order derivative-type regularisation functional enforcing sparsity of a vector field related to an image to be reconstructed using curl, divergence and shear operators. The model is able to interpolate between well-known regularisers such as total generalised variation and infimal convolution total variation. Finally, (iv) we demonstrate how we can learn sparsity promoting parametrised regularisers via quotient minimisation, which can be motivated by generalised Eigenproblems. Learning approaches have recently become very popular in the field of inverse problems. However, the majority aims at fitting models to favourable training data, whereas we incorporate knowledge about both fit and misfit data. We present results resembling behaviour of well-established derivative-based sparse regularisers, introduce novel families of non-derivative-based regularisers and extend this framework to classification problems.
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Tseng, Chi-Hung, and 曾吉宏. "A VQ-Based Image Compression for Grey-Level Image Sequences." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/29017028723748615469.

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Анотація:
碩士
大葉大學
資訊工程學系碩士班
93
Abstract A number of methods have been proposed for the compression of continuous image sequences. However, they only deal with binary images, which greatly limit their popularity in applications. In this thesis, we proposed a VQ-based method for compressing continuous grey-level images which have great similarity between two adjacent images. Four sets of continuous image sequences, each consists of 9 images with image size of 256x256 pixels, were used for testing the performance of the proposed method. Each image was first segmented into a number of 3x3 or 4x4 blocks, and then LBG algorithm was used for training a set of codebook consisting of 512 codewords capable of delineating features of the continuous image sequence. For further increasing the compression performance, JPEG-LS algorithm was applied to compress the codebook and index images of the sequential images. The results show that the compression ratio achieved by using the proposed method is significantly higher than AVI, while the image quality of the reconstructed images has been hold at a satisfied level. Future works will expand the method to application of lossless compression in medical image sequences. Keywords - Vector quantization, continuous image, image compression, AVI.
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Ho, Yu-An, and 何玉安. "A Study on Bi-Level Image Data Hiding and Image Compression." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/69085995316289719990.

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Анотація:
博士
國立中興大學
資訊科學與工程學系所
97
Data hiding, as the term itself suggests, means the hiding of secret data in a cover image, and the result is a so-called stego-image. Reversible data hiding is a kind of data hiding technique where not only the secret data can be extracted from the stego-image but the cover image can be completely rebuilt after the extraction of the secret data. Therefore, reversible data hiding is the choice in cases of secret data hiding where the recovery of the cover image is required. In this dissertation, we propose a high-capacity reversible data hiding scheme based on pattern substitution (PS). It gathers statistical data about the occurrence frequencies of different patterns and quantifies how the frequency of occurrence differs from pattern to pattern. This way, on top of the pattern occurrence frequency information, some pattern exchange relationships can be established, and PS can thus be used to do the data hiding. Then, in the extraction stage, we can reverse these patterns to their original forms and rebuild an undistorted cover image. Binary image is one of the commonly used image formats, such as FAX and document images. This dissertation proposes a binary image compression method, called QLS compression method, which uses BFT linear quadtree and logic-spectra techniques to losslessly compress a binary image. This method employs a breadth first traversal linear quadtree to divide the image into blocks, and then uses logic functions and spectral techniques to encode the blocks. This dissertation also presents a QLS hiding-compression method to encode the cover image and embed the secret data in the cover image during the encoding of the cover image. The stego-image created by the QLS hiding-compression method is quite similar to the cover image. Halftone image is commonly used by low memory space devices such as printers, fax machines, cell phones, etc. In this dissertation, a novel reversible data hiding scheme for halftone images is represented. After rendering the multi-tone image into a halftone image by error diffusion, the proposed scheme classifies blocks according to pixel permutation in the halftone image and then generates two patterns to hide secret data. The new scheme not only can securely conceal secret information, but it also can fully recover the original halftone image after the extraction of the secret information.
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Wang, Her-Fa, and 王和發. "A Refined VQ Gray-Level Image Compression Method and A Low Lossy Color Image Compression Method." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/58533890694910759475.

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Анотація:
碩士
朝陽科技大學
資訊管理系碩士班
92
This paper proposes two image compression methods. The first one is called A Refined VQ Gray-level image compression method which modifies the traditional VQ-based image compression method for encoding a gray-level image. It further lossless encodes the compression data which are generated by the traditional VQ-based image compression method. Although the PSNRs of the images decompressed by the proposed method and the traditional VQ-based image compression method are the same, the a refined VQ gray-level image compression method is more efficient in memory storage. The second method integrates the techniques of quadtree, standard deviation, and quadratic regression equation to compress a color image. Let f be a YIQ-formatted color image. This method employs a set of quadratic regression equations to portray the relationships between the color components Y and I, as well as Y and Q of the pixels in f. Then, it only marks down the coefficients of the quadratic regression equations and the Y component values of all the pixels in f so as to reduce the memory space required to hold f. Generally speaking, when the decompressed image is required for a high quality, this proposed method is better, compared with the JPEG method. Moreover, the proposed method usually provides a better performance for compressing an image with slighter variation among the colors of adjacent pixels too. The blocking and Gibbs effects occurring on the image decoded by the proposed method are much less than those appearing on the image decoompressed by JPEG method.
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10

Xia, Wen Nan, and 夏文南. "Image compression with classified interpolative multi-level block truncation coding." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/25322635484554056715.

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Книги з теми "Image compression level"

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Toivanen, Pekka. New distance transforms for gray-level image compression. Lappeenranta, Finland: Lappeenranta University of Technology, 1996.

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Частини книг з теми "Image compression level"

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Froment, Jacques. "Image Compression Through Level Lines and Wavelet Packets." In Computational Imaging and Vision, 305–39. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-015-9715-9_11.

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Nandi, Utpal, Biswajit Laya, Anudyuti Ghorai, and Moirangthem Marjit Singh. "Three-Level Hierarchical Classification Scheme: Its Application to Fractal Image Compression Technique." In Advances in Intelligent Systems and Computing, 123–32. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5679-1_12.

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Nobuhara, Hajime, Eduardo Masato Iyoda, Kaoru Hirota, and Witold Pedrycz. "Optimization of Image Compression Method Based on Fuzzy Relational Equations by Overlap Level of Fuzzy Sets." In Computational Intelligence for Modelling and Prediction, 163–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/10966518_12.

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Baraskar, Trupti, and Vijay R. Mankar. "True Color Image Compression and Decompression Using Fusion of Three-Level Discrete Wavelet Transform—Discrete Cosine Transforms and Arithmetic Coding Technique." In Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB), 469–81. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-00665-5_47.

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García-Ordás, Diego, Laura Fernández-Robles, Enrique Alegre, María Teresa García-Ordás, and Oscar García-Olalla. "Automatic Tampering Detection in Spliced Images with Different Compression Levels." In Pattern Recognition and Image Analysis, 416–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38628-2_49.

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Chaudhary, Ankit, and Sandeep Kumar. "Utilizing Image Color Channels for High Payload Embedding." In Handbook of Research on Emerging Perspectives in Intelligent Pattern Recognition, Analysis, and Image Processing, 116–26. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-8654-0.ch006.

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Анотація:
Steganography is the technique which has been used in many fields for hiding information and many different versions for each application are available in the literature. This chapter demonstrates how to increase the security level and to improve the storage capacity of hidden data, with compression techniques. The security level is increased by randomly distributing the text message over the entire image instead of clustering within specific image portions. The degradation of the images can be minimized by changing only one least significant bit per color channel for hiding the message. Using steganography alone with simple LSB has a potential problem that the secret message is easily detectable from the histogram analysis method. To improve the security as well as the image embedding capacity indirectly, a compression based scheme is introduced. Various tests have been done to check the storage capacity and message distribution.
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Tsai, Chwei-Shyong, and Chin-Chen Chang. "Embedding Robust Gray-Level Watermark in an Image Using Discrete Cosine Transformation." In Distributed Multimedia Databases, 206–23. IGI Global, 2002. http://dx.doi.org/10.4018/978-1-930708-29-7.ch013.

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Digital watermarking is an effective technique to protect the intellectual property rights of digital images. In general, a gray-level image can provide more perceptual information; moreover, the size of each pixel in the gray-level image is bigger. Commonly, gray-level digital watermarks are more robust. In this chapter, the proposed watermarking scheme adopts a gray-level image as the watermark. In addition, discrete cosine transformation (DCT) technique and quantization method are applied to strengthen the robustness of the watermarking system. Both original image and digital watermark, processed by DCT transformation, can build a quantization table to reduce the information size of the digital watermark. After quantized watermark is embedded into the middle frequency bands of the transformed original image, the quality of the watermarked image is always visually acceptable because of the effectiveness of the quantization technique. The experimental results show that the embedded watermark can resist image cropping, JPEG lossy compression, and destructive processes such as image blurring and sharpening.
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Geetha, P. "Survey of Medical Image Compression Techniques and Comparative Analysis." In Research Developments in Computer Vision and Image Processing, 327–56. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4558-5.ch016.

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Анотація:
Today digital imaging is widely used in every application around us like Internet, High Definition TeleVision (HDTV), satellite communications, fax transmission, and digital storage of movies and more, because it provide superior resolution and quality. Recently, medical imaging has begun to take advantage of digital technology, opening the way for advanced medical imaging and teleradiology. However, medical imaging requires storing, communicating and manipulating large amounts of digital data. Applying image compression reduces the storage requirements, network traffic, and therefore improves efficiency. This chapter provides the need for medical image compression; different approaches to image compression, emerging wavelet based lossy-lossless compression techniques, how the existing recent compression techniques work and also comparison of results. After completing this chapter, the reader should have an idea of how to increase the compression ratio and at the same time maintain the PSNR level compared to the existing techniques, desirable features of standard compression techniques such as embededness and progressive transmission, how these are very useful and much needed in the interactive teleradiology, telemedicine and telebrowsing applications.
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9

Geetha, P. "Survey of Medical Image Compression Techniques and Comparative Analysis." In Medical Imaging, 1165–98. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0571-6.ch048.

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Анотація:
Today digital imaging is widely used in every application around us like Internet, High Definition TeleVision (HDTV), satellite communications, fax transmission, and digital storage of movies and more, because it provide superior resolution and quality. Recently, medical imaging has begun to take advantage of digital technology, opening the way for advanced medical imaging and teleradiology. However, medical imaging requires storing, communicating and manipulating large amounts of digital data. Applying image compression reduces the storage requirements, network traffic, and therefore improves efficiency. This chapter provides the need for medical image compression; different approaches to image compression, emerging wavelet based lossy-lossless compression techniques, how the existing recent compression techniques work and also comparison of results. After completing this chapter, the reader should have an idea of how to increase the compression ratio and at the same time maintain the PSNR level compared to the existing techniques, desirable features of standard compression techniques such as embededness and progressive transmission, how these are very useful and much needed in the interactive teleradiology, telemedicine and telebrowsing applications.
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10

Bassou, Abdesselam. "The Discrete Quincunx Wavelet Packet Transform." In Wavelet Theory [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.94970.

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Анотація:
This chapter aims to present an efficient compression algorithm based on quincunx wavelet packet transform that can be applied on any image of size 128 × 128 or bigger. Therefore, a division process into sub-images of size 128 × 128 was applied on three gray-scale image databases, then pass each sub-image through the wavelet transform and a bit-level encoder, to finally compress the sub-image with respect to a fixed bit rate. The quality of the reconstructed image is evaluated using several parameters at a given bit rate. In order to improve the quality in sense of the evaluation quality, an exhaustive search has led to the best packet decomposition base. Two versions of the proposed compression scheme were performed; the optimal version is able to decrease the effect of block boundary artifacts (caused by the image division process) by 27.70 % considering a natural image. This optimal version of the compression scheme was compared with JPEG standard using the quality evaluation parameters and visual observation. As a result, the proposed compression scheme presents a competitive performance to JPEG standard; where the proposed scheme performs a peak signal to noise ratio of 0.88 dB over JPEG standard at a bit rate of 0.50 bpp for a satellite image.
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Тези доповідей конференцій з теми "Image compression level"

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Nguyen-Phi, K., and H. Weinrichter. "Bi-level image compression using adaptive tree model." In Proceedings DCC '97. Data Compression Conference. IEEE, 1997. http://dx.doi.org/10.1109/dcc.1997.582122.

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2

Zhu, Chunbiao, Yuanqi Chen, Yiwei Zhang, Shan Liu, and Ge Li. "ResGAN: A Low-Level Image Processing Network to Restore Original Quality of JPEG Compressed Images." In 2019 Data Compression Conference (DCC). IEEE, 2019. http://dx.doi.org/10.1109/dcc.2019.00128.

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3

Veisi, Hadi, and Mansour Jamzad. "Image Compression with Neural Networks Using Complexity Level of Images." In 2007 5th International Symposium on Image and Signal Processing and Analysis. IEEE, 2007. http://dx.doi.org/10.1109/ispa.2007.4383706.

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4

Raguram, Rahul, Michael W. Marcellin, and Ali Bilgin. "Improved Resolution Scalability for Bi-Level Image Data in JPEG2000." In 2007 Data Compression Conference (DCC'07). IEEE, 2007. http://dx.doi.org/10.1109/dcc.2007.48.

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5

Le Moan, Steven, Marius Pedersen, and Aladine Chetouani. "High-Level Visual Masking of Image Compression Artefacts." In 2020 IEEE International Conference on Image Processing (ICIP). IEEE, 2020. http://dx.doi.org/10.1109/icip40778.2020.9190877.

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6

Zhong, Zhisheng, Hiroaki Akutsu, and Kiyoharu Aizawa. "Channel-Level Variable Quantization Network for Deep Image Compression." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/65.

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Анотація:
Deep image compression systems mainly contain four components: encoder, quantizer, entropy model, and decoder. To optimize these four components, a joint rate-distortion framework was proposed, and many deep neural network-based methods achieved great success in image compression. However, almost all convolutional neural network-based methods treat channel-wise feature maps equally, reducing the flexibility in handling different types of information. In this paper, we propose a channel-level variable quantization network to dynamically allocate more bitrates for significant channels and withdraw bitrates for negligible channels. Specifically, we propose a variable quantization controller. It consists of two key components: the channel importance module, which can dynamically learn the importance of channels during training, and the splitting-merging module, which can allocate different bitrates for different channels. We also formulate the quantizer into a Gaussian mixture model manner. Quantitative and qualitative experiments verify the effectiveness of the proposed model and demonstrate that our method achieves superior performance and can produce much better visual reconstructions.
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7

Ai, Wei-hua, Yun-xian Huang, Xiang Li, and Chao-ling Shen. "Lossless and lossy compression for MODIS level 0 data." In Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Jayaram K. Udupa, Nong Sang, Laszlo G. Nyul, and Hengqing Tong. SPIE, 2009. http://dx.doi.org/10.1117/12.834021.

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8

Wen Sun, Yan Lu, Feng Wu, and Shipeng Li. "Level embedded medical image compression based on value of interest." In 2009 16th IEEE International Conference on Image Processing ICIP 2009. IEEE, 2009. http://dx.doi.org/10.1109/icip.2009.5414553.

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9

Anil, D., K. V. Karthik, and K. Sudhir Kumar. "A modified three level Block Truncation Coding for image compression." In 2011 International Conference on Pattern Analysis and Intelligent Robotics (ICPAIR). IEEE, 2011. http://dx.doi.org/10.1109/icpair.2011.5976907.

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Prasad, Venkata Rama, Vaddella, Ramesh Babu, and Inampudi. "Adaptive Gray Level Difference to Speed Up Fractal Image Compression." In 007 International Conference on Signal Processing, Communications and Networking. IEEE, 2007. http://dx.doi.org/10.1109/icscn.2007.350741.

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Звіти організацій з теми "Image compression level"

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Libert, John M., Shahram Orandi, Michael D. Garris, and John D. Grantham. Effects of Decomposition Levels and Quality Layers with JPEG 2000 Compression of 1000 ppi Fingerprint Images. National Institute of Standards and Technology, August 2013. http://dx.doi.org/10.6028/nist.ir.7939.

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