• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.5
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• pp.1025-1030
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• 2005

With the increasing use of multimedia technologies, image compression requires higher performance as well as new functionality. Specially, in the specific area of still image encoding, a new standard, JPEG2000 was developed. This paper proposed Set Partitioning Sorting Algorithm that uses a method to optimized selection of threshold from feature of wavelet transform coefficients and to removes sign bit in LL area on JPEG2000. Experimental results show the proposed algorithm achieves more improved bit rate.

• Electronics and Telecommunications Trends
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• v.17 no.4 s.76
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• pp.65-74
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• 2002

멀티미디어 기술의 이용 증가와 더불어, 영상 압축은 새로운 기능과 보다 나은 성능을 필요로 한다. 정지영상 인코딩 분야에서 이 기술을 언급하기 위하여 JPEG2000이라 하는 새로운 표준이 최근 개발되고 있다. JPEG2000 기술은 기존의 표준기술보다 율왜곡, 영상성능 면에서 우수성을 제공할 뿐 아니라 또한 현재의 표준들이 언급할 수 없는 특징이나 기능들을 제공해준다. JPEG2000의 대표적 기술적인 특성으로는 무손실/손실 압축, 무손실 코딩에의 내포된 손실, 화소 정확성과 해상 등에 의한 진보적인 전달, 비트 에러 그리고 관심영역 부호화의 견고함 등이 있다. JPEG2000의 필요성이 강조되는 다양한 응용분야로는 인터넷, 컬러 팩스, 인쇄, 스캐닝, 디지털 사진, 원격 탐사, 이동식 적용, 의학 영상, 전자 도서관과 E-commerce 등이 있다.

• The KIPS Transactions:PartA
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• v.13A no.3 s.100
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• pp.185-190
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• 2006

JPEG 2000 is used as an alternative to solve the blocking artifact problem with the existing still image compression JPEG algorithm. However, it has shortcomings such as longer floating point computation time and more complexity in the procedure of enhancing the image compression rate and decompression rate. To compensate for these we implemented with hardware the JPEG 2000 algorithm's filter part which requires a lot of floating point computation. This DWT Filter[1] chip is designed on the basis of Daubechies 9/7 filter[6] and is composed of 3-stage pipeline system to optimize the performance and chip size. Our implemented Filter was 7 times faster than software based Filter in the floating point computation.

• Journal of Korea Multimedia Society
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• v.14 no.1
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• pp.94-102
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• 2011

To alleviate the size and bandwidth requirement in JPEG2000 system, a new Embedded Compression(EC) algorithm with minor image quality drop is proposed. For both random accessibility and low latency, very simple and efficient hadamard transform based compression algorithm is devised. We reduced LL intermediate memory and code-block memory to about half size and achieved significant memory bandwidth reductions(about 52~73%) through proposed multi-mode algorithms, without requiring any modification in JPEG2000 standard algorithm.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2642-2644
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• 2003

많은 양의 디지털 이미지들 중에서 원하는 이미지를 추출해 내기 위해서 JPEG이미지 파일 안에 들어있는 이미지에 대한 정보를 검색에 이용할 수 있을 것이다. Digital camera를 위해 고안된 확장된 JPEG파일 구조인 EXIF형식의 경우 이미지에 대한 정보를 많이 포함하고 있기 때문에 이를 검색에 활용할 수 있을 것이다. 하지만 이미지에 대한 정보는 여러 목적에 따라 다를 수 있기 때문에 모든 종류의 가능한 이미지 정보 포맷을 미리 지정해 두기에는 한계가 있다. 따라서 사용자가 이미지 정보의 특성 자체를 정의하여 첨가시킬 수 있다면 더 사용자의 용도에 맞는 이미지 검색이 가능하게 될 것이다. 이렇게 이미지에 대한 정보 목록 자체를 추가할 수 있도록 하기 위한 방안으로 새로운 JPEG 파일 포맷인 JPEG2000에 있는 XML BOX를 이용하였다. 사용자가 추가하고 싶은 이미지 정보를 직접 정의하여 입력하면 XML 생성기에 의해서 이에 따른 XML코드가 생성되고 JPEG2000의 XML BOX에 정보가 추가되어 이를 검색에 활용할 수 있도록 시스템을 설계하였다.

• The KIPS Transactions:PartC
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• v.15C no.3
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• pp.149-156
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• 2008

JPEG2000 is the upcoming image coding standard that provides better compression rate and image quality compared with JPEG. Lazy-mode steganography guarantees the safe communication under the two information loss stages in JPEG2000. However, it causes the severe changes of the code-block noise variance sequence after embedding and that is detectable under the steganalysis using the Hilbert-Huang transform (HHT) based sequential analysis. In this paper, a JPEG2000 lazy-mode steganography method is presented. The code blocks which produce the sudden variation of the noise variance after embedding are estimated by calculating low precision code-block variance (LPV) and low precision code-block noise variance (LPNV). By avoiding those code-blocks from embedding, our algorithm preserves the sequence and makes stego images secure under the HHT-based steganalytic detection. In addition, it prevents a severe degradation of image quality by using JPEG2000 quality layer information. On various 2048 images, experiments are performed to show the effective reduction of the noise variation after message embedding and the stable performance against HHT-based steganalysis.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.4
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• pp.321-328
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• 2008

In this paper, we propose the hardware architecture of two-dimensional discrete wavelet transform (2D DWT) and quantization for using JPEG2000. Color 2-D DWT processor is proposed that is to apply to JPEG 2000 Hard-wired Encoder. JPEG 2000 DWT processor uses the Daubechies' (9,7) bi-orthogonal filter, and we design by minimizing error of the DWT transformer by ${\pm}1$ LSB during compression and decompression. We designed the DWT filters that using by using shift and adder structure instead of multiplier structure which raise the hardware complexity. It is improve the operation speed of filters and reduce the hardware complexity. The proposed system is designed by the hardware description language Verilog-HDL and verified by Synopsys Design Analyzer using TSMC 0.25${\mu}m$ ASIC library.

• The Journal of the Korea Contents Association
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• v.6 no.10
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• pp.143-152
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• 2006

JPEG2000, the standard of still image compression based on wavelet, will be widely used. One of the greatest characteristics of JPEG2000 is to offer ROI(Region-Of-Interest) coding. This is to compress with high quality the region that the user wants better than the other region. JPEG2000 and ROI have different parameters, which are tile size and ROI size, wavelet filter type and ROI shape and its location, codeblock size and number of ROI, number of DWT decomposition level and ROI importance, and number of quality layer and low resolution sub-band importance. In this paper, we shows the correlation of the parameters and ROI coding methods through experiments. This helps an application select the parameters and the methods to meet the application.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.5C
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• pp.496-512
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• 2007

In this paper, we propose a novel JPEG-2000 compressed image retrieval system using feature vector extracted through partial entropy decoding. Main idea of the proposed method is to utilize the context information that is generated during entropy encoding/decoding. In the framework of JPEG-2000, the context of a current coefficient is determined depending on the pattern of the significance and/or the sign of its neighbors in three bit-plane coding passes and four coding modes. The contexts provide a model for estimating the probability of each symbol to be coded. And they can efficiently describe texture images which have different pattern because they represent the local property of images. In addition, our system can directly search the images in the JPEG-2000 compressed domain without full decompression. Therefore, our proposed scheme can accelerate the work of retrieving images. We create various distortion and similarity image databases using MIT VisTex texture images for simulation. we evaluate the proposed algorithm comparing with the previous ones. Through simulations, we demonstrate that our method achieves good performance in terms of the retrieval accuracy as well as the computational complexity.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.197-206
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• 2006

One of the most significant characteristics of JPEG2000. the emerging still image standards. is the ROI (Region of Interest) coding. JPEG2000 provides a number of ROI coding mechanisms and ROI parameters. To apply them to an application, it must select the applicable values. In this paper, we evaluate how the ROI coding mechanisms and the ROI parameters influencing JPEG2000 qualify affect the ROI quality and the whole image quality. The ROI coding mechanisms are Maxshift and Implicit. and the parameters are tile size and ROI size, codeblock size, number of DWT decomposition levels and ROI importance. The bigger the tile size, the better the quality. The bigger the ROI size, the ROI importance and the number of DWT decomposition levels, the worse the qualify. In code block $32{\times}32$ of Maxshift and Implicit, it has the best qualify.