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Journal articles on the topic 'JPEG2000'

To see the other types of publications on this topic, follow the link: JPEG2000.

Author: Grafiati

Published: 4 June 2021

Last updated: 31 May 2022

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1

BALSTER, ERIC J., BENJAMIN T. FORTENER, and WILLIAM F. TURRI. "POST-COMPRESSION RATE-DISTORTION DEVELOPMENT FOR EMBEDDED BLOCK CODING WITH OPTIMAL TRUNCATION IN JPEG2000 IMAGERY." International Journal of Image and Graphics 11, no. 04 (October 2011): 611–27. http://dx.doi.org/10.1142/s0219467811004238.

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This paper presents a development of the post-compression rate-distortion optimization (PCRD-opt) algorithm in JPEG2000 used for optimal truncation (OT) rate control. The proposed treatment of PCRD-opt differs from the treatment given in the JPEG2000 standard and what is given in a number of publications. The proposed algorithm is implemented in a complete JPEG2000 compression engine as well as the algorithm published in the JPEG2000 standard. The proposed algorithm gives a substantial performance gain, outperforming the implementation given in the standard by 0.25–1 dB in PSNR on average, demonstrating an improvement from the method for PCRD-opt given in the standard. The proposed algorithm is also compared to the JPEG2000 reference implementation, JasPer, and the popular Kakadu JPEG2000 compressor. The proposed algorithm provides equivalent performance results to both JasPer and Kakadu, indicating that the proposed PCRD-opt treatment provides correct OT of JPEG2000 compressed imagery.

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2

Ren, Ke Qiang, and Hai Ying Jiang. "Application Research of Kakadu System Based on JPEG2000." Advanced Materials Research 403-408 (November 2011): 1933–36. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.1933.

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JPEG2000 is a new compressed standard of static image, Kakadu is a open source system which can be higher efficiency to realize the algorithm of JPEG2000. This article introduces the codec construction and the compressed stream structure in JPEG2000, analyzes the construction of Kakadu and image compression class in Kakadu, and then carries on application experiments based on JPEG2000 in Kakadu 2.2 platform. Experiments show that the compression of interest image region has higher transfer rate and lower memory compared with the original image, and JPEG2000 has higher compression rate and better visual quality compared with JPEG.

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3

OU, CHIEN-MIN, HUI-YA LI, WEN-JYI HWANG, and MEI-HWA LIU. "LAYERED IMAGE TRANSMISSION WITH QUALITY PRE-SPECIFIABLE JPEG2000." International Journal of Image and Graphics 08, no. 04 (October 2008): 629–41. http://dx.doi.org/10.1142/s0219467808003271.

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A novel JPEG2000-based algorithm, termed quality pre-specifiable layered JPEG2000 (QPSL-JPEG2000), for realizing a layered image transmission (LIT) system is presented in this paper. In the QPSL-JPEG2000 algorithm, the resolution and rate associated with each layer of the LIT system can be pre-specified. It encodes an image one layer at a time using the modified JPEG2000 technique. The encoding process at each layer only covers the subbands having resolution level lower than the designated resolution at that layer subject to the pre-specified incremental rate budget. The encoding results at the previous layers will be used in the current layer to accelerate the encoding process. Numerical results show that the QPSL-JPEG2000 algorithm outperforms its counterparts for constructing the rate and resolution pre-specified LIT systems.

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4

TSAI, TSUNG-HAN, and LIAN-TSUNG TSAI. "AN EFFICIENT DESIGN FOR MOTION-JPEG2000 SYSTEM IN REAL-TIME VIDEO ENCODING." Journal of Circuits, Systems and Computers 17, no. 04 (August 2008): 597–610. http://dx.doi.org/10.1142/s0218126608004563.

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Motion-JPEG2000 is derived from Part 1 of the JPEG2000 standard and provides high quality image compression in video coding system. This paper presents a real-time Motion-JPEG2000 encoding which is built on a fixed-point digital signal processor (DSP) as a single-chip implementation. The design addresses on the system-level design considerations. Among several modules in JPEG2000, Embedded Block Coding with Optimized Truncation (EBCOT) algorithm contains the highest computation complexity and also becomes the bottleneck of JPEG2000. Two speed-improved methods are proposed to significantly reduce the processing time of EBCOT. The fast algorithm is used by processing the three coding passes in sequential and is very suitable to the execution on programming-based processor. For Motion-JPEG2000 implementation, a real-time encoding system is built on the single-chip DSP processor without any extra hardware support. The optimization techniques are also applied to enhance the performance. Due to the contribution on algorithm and implementation issue, the complexity of Motion-JPEG2000 is largely reduced. Simulation results show that the proposed system can process 30 frames per second in CIF size (352 × 288 pixels) with 4:2:0 video sequences. For SDTV size resolution (760 × 480 pixels), the system can achieve near 20 frames per second.

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5

Zhang, Lu. "The Design of Static Image Compression System." Advanced Materials Research 1042 (October 2014): 150–53. http://dx.doi.org/10.4028/www.scientific.net/amr.1042.150.

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In this paper, a static image compression system based on DSP is introduced. The compression standard of this system is JPEG2000 that is the new image compression standard by ISO. First, the basic algorithm and key technologic of JPEG2000 are explained. Then, the realization method of the static image compression system based on JPEG2000 image coding is analyzed. Finally, the hardware of this system which is based on DSP is described. And by the verification of the system, very good static image compression effect can be get from this system.

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6

Dinu, Dragan, and Dragan Ivetic. "Architectures of DICOM based PACS for JPEG2000 medical image streaming." Computer Science and Information Systems 6, no. 1 (2009): 185–203. http://dx.doi.org/10.2298/csis0901185d.

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Delivering of medical image content to mobile/embedded devices with low storage and processing capabilities and low resolution displays is a challenging, but important task for achieving ubiquitous computing in modern hospital environments. The common approaches in industry and technical literature employ JPEG2000 compression and image streaming. Beside image compression, JPEG2000 facilitates image streaming. Different sized images are extracted from one codestream with minimal spatial distortion within decompressed image. We developed DICOM2000 syntax which enables JPEG2000 streaming over DICOM networks. DICOM2000 brings compression and streaming power of JPEG2000 to DICOM which is the standard for medical image interchange. It enables transmission of best quality medical images suited for client device processing and display capabilities. In this paper communication architectures of DICOM2000 based PACS are compared with the common architectures of DICOM based PACS. For the purpose of the paper, these communication architectures are categorized, named, and described. At the end, the advantages of the DICOM2000 based PACS over standard PACS are pointed up.

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7

LU, WEN, LIHUO HE, WENJIAN TANG, FEI GAO, and WEILONG HOU. "A NOVEL COMPRESSED IMAGES QUALITY METRIC." International Journal of Image and Graphics 11, no. 02 (April 2011): 281–92. http://dx.doi.org/10.1142/s021946781100410x.

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As the performance indicator of the image processing algorithms or systems, image quality assessment (IQA) has attracted the attention of many researchers. Aiming to the widely used compression standards, JPEG and JPEG2000, we propose a new no reference (NR) metric for compressed images to do IQA. This metric exploits the causes of distortion by JPEG and JPEG2000, employs the directional discrete cosine transform (DDCT) to obtain the detail and directional information of the images and incorporates with the visual perception to obtain the image quality index. Experimental results show that the proposed metric not only has outstanding performance on JPEG and JPEG2000 images, but also applicable to other types of artifacts.

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8

Marcellin, Michael W., and Ali Bilgin. "JPEG2000 for Digital Cinema." SMPTE Motion Imaging Journal 114, no. 5-6 (May 2005): 202–9. http://dx.doi.org/10.5594/j12291.

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9

Colyer, G. J., and R. A. Clark. "JPEG2000 and interactive applications." IEEE Transactions on Consumer Electronics 49, no. 4 (November 2003): 850–54. http://dx.doi.org/10.1109/tce.2003.1261165.

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10

Samet, Amine, Mohamed Ali Ben Ayed, Mourad Loulou, and Nouri Masmoudi. "Perceptual Evaluation of JPEG2000." European Transactions on Telecommunications 15, no. 2 (March 2004): 135–43. http://dx.doi.org/10.1002/ett.962.

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11

Wang, Wei Ling. "JPEG2000 Image Compression Method Based on GPGPU." Advanced Materials Research 756-759 (September 2013): 1314–19. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1314.

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In order to improve the compression speed of JPEG2000, the JPEG2000 compression standard is analysised and it concluded that the part data of the core algorithm that is DWT in JPEG2000 are independent from each other, so it is very suitable for parallel processing. CUDA (Compute Unified Device Architecture) is a latest software and hardware exploitation platform released by NVIDIA which is very suitable for large-scale data parallel computing.Using CUDA technology on general purpose graphic process unit (GPGPU) could speed up DWT algorithm parallelly and the program is optimized based on the characteristics of GPGPU storage space. The obtained experimental results show the DWT algorithm that is optimized by CUDA parallelly can improve the computing speed.

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12

JACOB, THEJU, K. R. RAO, and DO NYEON KIM. "IMAGE MIRRORING AND ROTATION IN THE WAVELET DOMAIN." International Journal of Wavelets, Multiresolution and Information Processing 08, no. 01 (January 2010): 61–69. http://dx.doi.org/10.1142/s0219691310003389.

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JPEG2000, the international standard for still image compression, uses wavelet transform. The filters used by JPEG2000 for transformation are the 9/7 Daubechies filter and the 5/3 Le Gall filter. In this paper, we present a method for image mirroring and rotation in the wavelet domain, by manipulating the transformation coefficients. Our technique requires no additional complexity and the perfect reconstruction property is preserved.

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13

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.

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14

Adamović, Marko. "KOMPRESIJA MULTISPEKTRALNIH SATELITSKIH SNIMAKA KORIŠĆENJEM HEVC STANDARDA." Zbornik radova Fakulteta tehničkih nauka u Novom Sadu 34, no. 03 (March 10, 2019): 594–97. http://dx.doi.org/10.24867/02be36adamovic.

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Sateliti za multispektralno snimanje Zemlje, kao što su američki Landsat 8 i evropski Sentinel-2, svakodnevno prave veliku količinu fotografija visoke rezolucije. Njihovo skladištenje zahteva veliki digitalni prostor, što uvećava cenu opreme. Landsat snima slike u nekompresovanom formatu, dok Sentinel koristi JPEG2000 kompresiju. Cilj ovog rada je da uporedi High Efficiency Video Coding (HEVC) i JPEG2000 standarde za kompresiju ovih fotografija i utvrdi da li bi HEVC bio bolje rešenje.

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15

Cao, Zhipeng, Zhenzhong Wei, and Guangjun Zhang. "A No-Reference Sharpness Metric Based on Structured Ringing for JPEG2000 Images." Advances in Optical Technologies 2014 (June 24, 2014): 1–13. http://dx.doi.org/10.1155/2014/295615.

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This work presents a no-reference image sharpness metric based on human blur perception for JPEG2000 compressed image. The metric mainly uses a ringing measure. And a blurring measure is used for compensation when the blur is so severe that ringing artifacts are concealed. We used the anisotropic diffusion for the preliminary ringing map and refined it by considering the property of ringing structure. The ringing detection of the proposed metric does not depend on edge detection, which is suitable for high degraded images. The characteristics of the ringing and blurring measures are analyzed and validated theoretically and experimentally. The performance of the proposed metric is tested and compared with that of some existing JPEG2000 sharpness metrics on three widely used databases. The experimental results show that the proposed metric is accurate and reliable in predicting the sharpness of JPEG2000 images.

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16

Campbell, Derrick S. "Scalable image registration with JPEG2000." Journal of Electronic Imaging 18, no. 1 (January 1, 2009): 013009. http://dx.doi.org/10.1117/1.3099706.

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17

Po-Chyi Su and C. C. J. Kuo. "Steganography in JPEG2000 compressed images." IEEE Transactions on Consumer Electronics 49, no. 4 (November 2003): 824–32. http://dx.doi.org/10.1109/tce.2003.1261161.

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18

Taubman, D. S., and M. W. Marcellin. "JPEG2000: standard for interactive imaging." Proceedings of the IEEE 90, no. 8 (August 2002): 1336–57. http://dx.doi.org/10.1109/jproc.2002.800725.

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19

Andra, K., C. Chakrabarti, and T. Acharya. "A high-performance JPEG2000 architecture." IEEE Transactions on Circuits and Systems for Video Technology 13, no. 3 (March 2003): 209–18. http://dx.doi.org/10.1109/tcsvt.2003.809834.

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20

Zhenyu Wu, A. Bilgin, and M. W. Marcellin. "Error Resilient Decoding of JPEG2000." IEEE Transactions on Circuits and Systems for Video Technology 17, no. 12 (December 2007): 1752–57. http://dx.doi.org/10.1109/tcsvt.2007.904534.

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21

Ebner, M., G. Tischler, and J. Albert. "Integrating Color Constancy Into JPEG2000." IEEE Transactions on Image Processing 16, no. 11 (November 2007): 2697–706. http://dx.doi.org/10.1109/tip.2007.908086.

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22

Han Oh, Ali Bilgin, and Michael W. Marcellin. "Visually Lossless Encoding for JPEG2000." IEEE Transactions on Image Processing 22, no. 1 (January 2013): 189–201. http://dx.doi.org/10.1109/tip.2012.2215616.

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23

Engel, Dominik, Thomas Stütz, and Andreas Uhl. "A survey on JPEG2000 encryption." Multimedia Systems 15, no. 4 (January 16, 2009): 243–70. http://dx.doi.org/10.1007/s00530-008-0150-0.

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24

Chen, Tzong-Jer, Sheng-Chieh Lin, You-Chen Lin, Ren-Gui Cheng, Li-Hui Lin, and Wei Wu. "JPEG2000 Still Image Coding Quality." Journal of Digital Imaging 26, no. 5 (April 16, 2013): 866–74. http://dx.doi.org/10.1007/s10278-013-9603-x.

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25

Tang, Ning, Jin Cai, and Yuan Li. "An Enhanced Resolution Three-Dimensional Transformation Method Based on Discrete Wavelet Transform." Applied Mechanics and Materials 159 (March 2012): 41–45. http://dx.doi.org/10.4028/www.scientific.net/amm.159.41.

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With the development of interactive multimedia technologies, image and video compression algorithms necessitated a number of better performance and functionality. Wavelet transform based embedded image coding method is the basis of JPEG2000. Lossy image compression algorithms sacrifice perfect image reconstruction in favor of decreased storage requirements. JPEG2000 algorithm has been developed based on the discrete wavelet transform (DWT) techniques, which have shown how the results achieved in different areas in information technology can be applied to enhance the performance.

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26

Fukuhara, Takahiro. "Motion JPEG2000 and MJ2 File Format." Journal of The Institute of Image Information and Television Engineers 65, no. 8 (2011): 1177–80. http://dx.doi.org/10.3169/itej.65.1177.

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27

Hayati, Anis Kamilah, and Haris Suka Dyatmika. "THE EFFECT OF JPEG2000 COMPRESSION ON REMOTE SENSING DATA OF DIFFERENT SPATIAL RESOLUTIONS." International Journal of Remote Sensing and Earth Sciences (IJReSES) 14, no. 2 (January 8, 2018): 111. http://dx.doi.org/10.30536/j.ijreses.2017.v14.a2724.

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The huge size of remote sensing data implies the information technology infrastructure to store, manage, deliver and process the data itself. To compensate these disadvantages, compressing technique is a possible solution. JPEG2000 compression provide lossless and lossy compression with scalability for lossy compression. As the ratio of lossy compression getshigher, the size of the file reduced but the information loss increased. This paper tries to investigate the JPEG2000 compression effect on remote sensing data of different spatial resolution. Three set of data (Landsat 8, SPOT 6 and Pleiades) processed with five different level of JPEG2000 compression. Each set of data then cropped at a certain area and analyzed using unsupervised classification. To estimate the accuracy, this paper utilized the Mean Square Error (MSE) and the Kappa coefficient agreement. The study shows that compressed scenes using lossless compression have no difference with uncompressed scenes. Furthermore, compressed scenes using lossy compression with the compression ratioless than 1:10 have no significant difference with uncompressed data with Kappa coefficient higher than 0.8.

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28

Persson, Hannes, Anna Brunstrom, and Tony Ottosson. "Utilizing Cross-Layer Information to Improve Performance in JPEG2000 Decoding." Advances in Multimedia 2007 (2007): 1–10. http://dx.doi.org/10.1155/2007/24758.

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We focus on wireless multimedia communication and investigate how cross-layer information can be used to improve performance at the application layer, using JPEG2000 as an example. The cross-layer information is in the form of soft information from the physical layer. The soft information, which is supplied by a soft decision demodulator, yields reliability measures for the received bits and is fed into two soft input iterative JPEG2000 image decoders. When errors are detected with the error detecting mechanisms in JPEG2000, the decoders utilize the soft information to point out likely transmission errors. Hence, the decoders can correct errors and increase the image quality without making time-consuming retransmissions. We believe that the proposed decoding method utilizing soft information is suitable for a general IP-based network and that it keeps the principles of a layered structure of the protocol stack intact. Further, experimental results with images transmitted over a simulated wireless channel show that a simple decoding algorithm that utilizes soft information can give high gains in image quality compared to the standard hard-decision decoding.

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29

Lu, Liang, Leng Zhang, and Ting Zhang. "JPEG2000-Based Optimization Algorithm for Effective Compression Display of Remote Sensing Images." Applied Mechanics and Materials 325-326 (June 2013): 1602–9. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1602.

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Taking the compression issue of remote sensing images as the study subject, this paper analyses the technical process for JPEG2000 compression and the image features of remote sensing images, natural images and figural images, and puts forwards an integrative optimization algorithm for effective compression display for remote sensing images based on common JPEG2000 compression frame. Thereinto, it includes high-frequency component filtering treatment, parallel processing of bit plane coding pass scanning and improved ROI coding algorithm, which give obvious clews to both overall consumed time for image compression and the gradual display effect in ROI region.

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30

Maini, Raman, and Suruchi Mehra. "A Review on JPEG2000 Image Compression." International Journal of Computer Applications 11, no. 9 (December 10, 2010): 43–47. http://dx.doi.org/10.5120/1607-2159.

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31

Chang, Zhiguo, and Jian Xu. "Format-Compliant Encryption of JPEG2000 Codestreams." International Journal of Wireless and Microwave Technologies 1, no. 3 (June 15, 2011): 54–61. http://dx.doi.org/10.5815/ijwmt.2011.03.09.

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32

Li, L., Z. S. Wang, and M. Guo. "Adaptive rate control for Motion JPEG2000." IET Image Processing 3, no. 2 (April 1, 2009): 75–87. http://dx.doi.org/10.1049/iet-ipr.2008.0053.

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33

Leibo Liu, Ning Chen, Hongying Meng, Li Zhang, Zhihua Wang, and Hongyi Chen. "A VLSI architecture of JPEG2000 encoder." IEEE Journal of Solid-State Circuits 39, no. 11 (November 2004): 2032–40. http://dx.doi.org/10.1109/jssc.2004.831492.

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34

Tsutsui, Hiroshi, Takahiko Masuzaki, Yoshiteru Hayashi, Yoshitaka Taki, Tomonori Izumi, Takao Onoye, and Yukihiro Nakamura. "Design Framework For JPEG2000 System Architecture." Intelligent Automation & Soft Computing 12, no. 3 (January 2006): 331–43. http://dx.doi.org/10.1080/10798587.2006.10642936.

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35

Varma, K., and A. Bell. "JPEG2000 - choices and tradeoffs for encoders." IEEE Signal Processing Magazine 21, no. 6 (November 2004): 70–75. http://dx.doi.org/10.1109/msp.2004.1359144.

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36

Bilgin, A., M. W. Marcellin, and M. I. Altbach. "Compression of electrocardiogram signals using JPEG2000." IEEE Transactions on Consumer Electronics 49, no. 4 (November 2003): 833–40. http://dx.doi.org/10.1109/tce.2003.1261162.

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37

Bing-Fei Wu and Chung-Fu Lin. "An efficient architecture for JPEG2000 coprocessor." IEEE Transactions on Consumer Electronics 50, no. 4 (November 2004): 1183–89. http://dx.doi.org/10.1109/tce.2004.1362517.

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38

Chebil, F., M. K. Bel Hadj Miled, A. Islam, and Kai Willner. "Compressed domain editing of JPEG2000 images." IEEE Transactions on Consumer Electronics 51, no. 2 (May 2005): 710–17. http://dx.doi.org/10.1109/tce.2005.1468023.

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39

Sanchez, V., and M. K. Mandal. "Efficient Channel Protection for JPEG2000 Bitstream." IEEE Transactions on Circuits and Systems for Video Technology 14, no. 4 (April 2004): 554–58. http://dx.doi.org/10.1109/tcsvt.2004.825573.

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40

Auli-Llinas, F., and J. Serra-Sagrista. "JPEG2000 Quality Scalability Without Quality Layers." IEEE Transactions on Circuits and Systems for Video Technology 18, no. 7 (July 2008): 923–36. http://dx.doi.org/10.1109/tcsvt.2008.920748.

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41

Minsoo Rhu and In-Cheol Park. "Optimization of Arithmetic Coding for JPEG2000." IEEE Transactions on Circuits and Systems for Video Technology 20, no. 3 (March 2010): 446–51. http://dx.doi.org/10.1109/tcsvt.2009.2031401.

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42

Long, M., H. M. Tai, and S. Yang. "Quantisation step selection schemes in JPEG2000." Electronics Letters 38, no. 12 (2002): 547. http://dx.doi.org/10.1049/el:20020375.

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43

HOU, J., X. FANG, H. YIN, and J. LI. "Constant Rate Control for Motion JPEG2000." IEICE Transactions on Information and Systems E89-D, no. 10 (October 1, 2006): 2690–92. http://dx.doi.org/10.1093/ietisy/e89-d.10.2690.

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44

Lalgudi, H. G., A. Bilgin, M. W. Marcellin, and M. S. Nadar. "Compression of Multidimensional Images Using JPEG2000." IEEE Signal Processing Letters 15 (2008): 393–96. http://dx.doi.org/10.1109/lsp.2008.922285.

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45

Acharya, Tinku. "VLSI Algorithms and architectures for JPEG2000." Ubiquity 2006, September (September 2006): 1–42. http://dx.doi.org/10.1145/1167867.1167107.

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46

Zhen Liu, L. J. Karam, and A. B. Watson. "JPEG2000 encoding with perceptual distortion control." IEEE Transactions on Image Processing 15, no. 7 (July 2006): 1763–78. http://dx.doi.org/10.1109/tip.2006.873460.

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47

Jicheng An and Zixing Cai. "Embedded Trellis Coded Quantization for JPEG2000." IEEE Transactions on Image Processing 17, no. 9 (September 2008): 1570–73. http://dx.doi.org/10.1109/tip.2008.2001157.

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48

Tan, D. M., C. S. Tan, and H. R. Wu. "Perceptual Color Image Coding With JPEG2000." IEEE Transactions on Image Processing 19, no. 2 (February 2010): 374–83. http://dx.doi.org/10.1109/tip.2009.2033625.

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49

Ling Li and Zhen-Song Wang. "Compression Quality Prediction Model for JPEG2000." IEEE Transactions on Image Processing 19, no. 2 (February 2010): 384–98. http://dx.doi.org/10.1109/tip.2009.2034706.

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50

Naman, A. T., and D. Taubman. "JPEG2000-Based Scalable Interactive Video (JSIV)." IEEE Transactions on Image Processing 20, no. 5 (May 2011): 1435–49. http://dx.doi.org/10.1109/tip.2010.2093905.

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