19 research outputs found

    Fast algorithm for the 3-D DCT-II

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    Recently, many applications for three-dimensional (3-D) image and video compression have been proposed using 3-D discrete cosine transforms (3-D DCTs). Among different types of DCTs, the type-II DCT (DCT-II) is the most used. In order to use the 3-D DCTs in practical applications, fast 3-D algorithms are essential. Therefore, in this paper, the 3-D vector-radix decimation-in-frequency (3-D VR DIF) algorithm that calculates the 3-D DCT-II directly is introduced. The mathematical analysis and the implementation of the developed algorithm are presented, showing that this algorithm possesses a regular structure, can be implemented in-place for efficient use of memory, and is faster than the conventional row-column-frame (RCF) approach. Furthermore, an application of 3-D video compression-based 3-D DCT-II is implemented using the 3-D new algorithm. This has led to a substantial speed improvement for 3-D DCT-II-based compression systems and proved the validity of the developed algorithm

    Compression domain volume rendering for distributed environments

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    This paper describes a method for volume data compression and rendering which bases on wavelet splats. The underlying concept is especially designed for distributed and networked applications, where we assume a remote server to maintain large scale volume data sets, being inspected, browsed through and rendered interactively by a local client. Therefore, we encode the serverโ€˜s volume data using a newly designed wavelet based volume compression method. A local client can render the volumes immediately from the compression domain by using wavelet footprints, a method proposed earlier. In addition, our setup features full progression, where the rendered image is refined progressively as data comes in. Furthermore, framerate constraints are considered by controlling the quality of the image both locally and globally depending on the current network bandwidth or computational capabilities of the client. As a very important aspect of our setup, the client does not need to provide storage for the volume data and can be implemented in terms of a network application. The underlying framework enables to exploit all advantageous properties of the wavelet transform and forms a basis for both sophisticated lossy compression and rendering. Although coming along with simple illumination and constant exponential decay, the rendering method is especially suited for fast interactive inspection of large data sets and can be supported easily by graphics hardware

    JSCC-Cast: A Joint Source Channel Coding Video Encoding and Transmission System with Limited Digital Metadata

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    [Abstract] This work considers the design and practical implementation of JSCC-Cast, a comprehensive analog video encoding and transmission system requiring a reduced amount of digital metadata. Suitable applications for JSCC-Cast are multicast transmissions over time-varying channels and Internet of Things wireless connectivity of end devices having severe constraints on their computational capabilities. The proposed system exhibits a similar image quality compared to existing analog and hybrid encoding alternatives such as Softcast. Its design is based on the use of linear transforms that exploit the spatial and temporal redundancy and the analog encoding of the transformed coefficients with different protection levels depending on their relevance. JSCC-Cast is compared to Softcast, which is considered the benchmark for analog and hybrid video coding, and with an all-digital H.265-based encoder. The results show that, depending on the scenario and considering image quality metrics such as the structural similarity index measure, the peak signal-to-noise ratio, and the perceived quality of the video, JSCC-Cast exhibits a performance close to that of Softcast but with less metadata and not requiring a feedback channel in order to track channel variations. Moreover, in some circumstances, the JSCC-Cast obtains a perceived quality for the frames comparable to those displayed by the digital one.This work has been funded by the Xunta de Galicia (by grant ED431C 2020/15 and grant ED431G 2019/01 to support the Centro de Investigaciรณn de Galicia โ€œCITICโ€), the Agencia Estatal de Investigaciรณn of Spain (by grants RED2018-102668-T and PID2019-104958RB-C42), and ERDF funds of the EU (FEDER Galicia 2014โ€“2020 and AEI/FEDER Programs, UE)Xunta de Galicia; ED431C 2020/15Xunta de Galicia; ED431G 2019/0

    Feature-preserving Reduction and Visualization of Industrial CT data using GLCM texture analysis and Mass-spring Model Deformation

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2014. 8. ์‹ ์˜๊ธธ.๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” 3D ๋ณผ๋ฅจ ๋ฐ์ดํ„ฐ์—์„œ ์ค‘์š”ํ•œ ์˜์—ญ์„ ๋ณด์กดํ•˜๋ฉด์„œ ํฌ๊ธฐ๋ฅผ ์ค„์ด๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๋ณผ๋ฅจ ๋ฐ์ดํ„ฐ์—์„œ ์–ด๋Š ๋ถ€๋ถ„์ด ์ค‘์š”ํ•œ ์˜์—ญ์ธ์ง€๋ฅผ ๊ฒฐ์ •ํ•˜๊ธฐ ์œ„ํ•ด ์งˆ๊ฐ ๋ถ„์„ ๋ฐฉ๋ฒ• ์ค‘ ํ•˜๋‚˜์ธ GLCM ๊ท ์ผ๋„๋ฅผ ์ด์šฉํ•œ ์ค‘์š”๋„ ์ธก์ • ๋ชจ๋ธ์„ ์ œ์•ˆํ•˜๊ณ , ์ด๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ MSM ๊ธฐ๋ฐ˜์˜ ๋ณผ๋ฅจ ๋ณ€ํ˜•์„ ์ˆ˜ํ–‰ํ•œ๋‹ค. ์ค‘์š”๋„๊ฐ€ ๋ฐ˜์˜๋œ ๋ณผ๋ฅจ ๋ณ€ํ˜• ๊ณผ์ •์„ ํ†ตํ•ด, ์ค‘์š”ํ•œ ์˜์—ญ์€ ์ƒ๋Œ€์ ์œผ๋กœ ํฌ๊ธฐ๊ฐ€ ํ™•์žฅ๋˜๋Š” ๋ฐ˜๋ฉด, ๋œ ์ค‘์š”ํ•œ ์˜์—ญ์€ ์ค„์–ด๋“ค๊ฒŒ ๋œ๋‹ค. ์ด๋กœ ์ธํ•ด, ์ผ๋ฐ˜์ ์œผ๋กœ ์†์‹ค๋ฅ ์ด ๋†’์€ ๊ท ์ผ ๋‹ค์šด์ƒ˜ํ”Œ๋ง์„ ์ด์šฉํ•œ ์••์ถ• ํ›„์—๋„ ์ž‘์€ ํฌ๊ธฐ์˜ ์ค‘์š”ํ•œ ํŠน์ง•์ ๋“ค์ด ์†์‹ค๋˜์ง€ ์•Š๊ณ  ๋ณด์กด๋  ์ˆ˜ ์žˆ๋‹ค. ์‹ค์ธก ์‚ฐ์—… ์˜์ƒ ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•œ ์‹คํ—˜์„ ํ†ตํ•ด, ๊ทธ๋ƒฅ ๊ท ์ผ ๋‹ค์šด์ƒ˜ํ”Œ๋ง์„ ์ด์šฉํ•œ ์••์ถ• ๊ฒฐ๊ณผ์—์„œ๋Š” ์‚ฌ๋ผ์ง„ ์ž‘์€ ๊ธฐ๊ณต์ด๋‚˜ ์ˆ˜์ถ• ๊ท ์—ด ํ˜•ํƒœ์˜ ๊ฒฐํ•จ ์˜์—ญ์ด ์ œ์•ˆ ๋ฐฉ๋ฒ•์—์„œ๋Š” ๋ณด์กด๋˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด ๋ณ€ํ˜• ๋ณผ๋ฅจ์„ ์›๋ž˜ ํ˜•ํƒœ๋กœ ๊ฐ€์‹œํ™”ํ•˜๊ธฐ ์œ„ํ•ด์„  ์—ญ๋ณ€ํ˜• ๊ณผ์ •์„ ์ถ”๊ฐ€๋กœ ์ˆ˜ํ–‰ํ•ด์•ผ ํ•˜์ง€๋งŒ, ์ด ๊ณ„์‚ฐ์€ ๊ฐ€์‹œํ™” ๊ณผ์ •์— ๊ฐ„๋‹จํ•˜๊ฒŒ ์ถ”๊ฐ€ํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ๊ฒฐ๊ณผ๋ฅผ ์–ป๊ธฐ ์œ„ํ•œ ์†Œ์š”์‹œ๊ฐ„์— ์œ ์˜๋ฏธํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ์•Š๋Š”๋‹ค.Non-destructive testing is a method which examines the internal structures of industrial components such as various machine parts without dissecting them. Recently, 3D CT based analysis enables more accurate inspection than traditional X-ray based tests. However, manipulating volumetric data acquired by CT is still challenging due to its huge size of the volume data. This dissertation proposes a novel method that reduces the size of 3D volume data while preserving important features in the data. Our method quantifies the importance of features in the 3D data based on gray level co-occurrence matrix (GLCM) texture analysis and represents the volume data using a simple mass-spring model. According to the measured importance value, blocks containing important features expand while other blocks shrink. After deformation, small features are exaggerated on deformed volume space, and more likely to survive during the uniform volume reduction. Experimental results showed that our method well preserved the small features of the original volume data during the reduction without any artifact comparing with the previous methods. Although additional inverse deformation process was required for the rendering of the deformed volume data, the rendering speed of the deformed volume data was much faster than that of the original volume data.์ดˆ๋ก i ๋ชฉ์ฐจ iii ๊ทธ๋ฆผ ๋ชฉ์ฐจ vi ํ‘œ ๋ชฉ์ฐจ x 1์žฅ ์„œ๋ก  1 1.1 ๋ณผ๋ฅจ ๋ Œ๋”๋ง 1 1.2 ๋น„ํŒŒ๊ดด๊ฒ€์‚ฌ 2 1.3 ์—ฐ๊ตฌ ๋‚ด์šฉ 4 1.4 ๋…ผ๋ฌธ์˜ ๊ตฌ์„ฑ 6 2์žฅ ๊ด€๋ จ ์—ฐ๊ตฌ 7 2.1 ๋ณผ๋ฅจ ๋ Œ๋”๋ง ์•Œ๊ณ ๋ฆฌ์ฆ˜ 7 2.1.1 ๋ณผ๋ฅจ ๋ฐ์ดํ„ฐ์˜ ํŠน์„ฑ 7 2.1.2 ํ‘œ๋ฉด ์ถ”์ถœ ๊ธฐ๋ฒ• 8 2.1.3 ์ง์ ‘ ๋ณผ๋ฅจ ๋ Œ๋”๋ง 10 2.2 ์••์ถ• ๋ณผ๋ฅจ ๋ Œ๋”๋ง 17 2.2.1 ๋ฒกํ„ฐ ์–‘์žํ™” 18 2.2.2 ๋ณ€ํ™˜ ๋ถ€ํ˜ธํ™” 19 2.2.3 ๋‹ค์ค‘-ํ•ด์ƒ๋„ ๊ธฐ๋ฐ˜ ๊ธฐ๋ฒ• 23 2.2.4 ๋ณผ๋ฅจ ๋ณ€ํ˜• ๊ธฐ๋ฐ˜ ๋ฐฉ๋ฒ• 25 2.3 ์งˆ๋Ÿ‰-์Šคํ”„๋ง ๊ธฐ๋ฐ˜ ๋ณผ๋ฅจ ๋ณ€ํ˜• ๋ชจ๋ธ 27 2.4 ์‚ฐ์—…์šฉ CT ์˜์ƒ์˜ ์ค‘์š” ํŠน์ง•์  ์ธก๋Ÿ‰ ๋ฐฉ๋ฒ• 30 3์žฅ ์ค‘์š”๋„ ์ธก์ • ๊ธฐ๋ฒ• 32 3.1 ๋ช…์•”๋„ ๋™์‹œ๋ฐœ์ƒ ํ–‰๋ ฌ 32 3.2 GLCM ๊ท ์ผ๋„ ๊ธฐ๋ฐ˜ ์ค‘์š”๋„ ๋ชจ๋ธ 36 3.3 ๊ณต๊ธฐ ์˜์—ญ ์ œ๊ฑฐ 44 4์žฅ ๋ณผ๋ฅจ ๋ณ€ํ˜•, ์ถ•์†Œ ๋ฐ ๊ฐ€์‹œํ™” 47 4.1 ์งˆ๋Ÿ‰-์Šคํ”„๋ง ๋ชจ๋ธ ๊ธฐ๋ฐ˜ ๋ณผ๋ฅจ ๋ณ€ํ˜• 47 4.2 ๋ณผ๋ฅจ ์ถ•์†Œ 54 4.3 ์—ญ๋ณ€ํ˜• ๋ฐ ๋ Œ๋”๋ง 55 5์žฅ ์‹คํ—˜ ๋ฐ ๊ฒฐ๊ณผ 58 5.1 ํ™”์งˆ ํ‰๊ฐ€ 60 5.2 ์†๋„ ํ‰๊ฐ€ 65 5.3 ํŒŒ๋ผ๋ฏธํ„ฐ ์—ฐ๊ตฌ 69 6์žฅ ๊ฒฐ๋ก  74 6.1 ์š”์•ฝ 74 6.2 ํ–ฅํ›„ ์—ฐ๊ตฌ 75 ์ฐธ๊ณ ๋ฌธํ—Œ 77 Abstract 83Docto

    Natural ventilation design attributes application effect on, indoor natural ventilation performance of a double storey, single unit residential building

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    In establishing a good indoor thermal condition, air movement is one of the important parameter to be considered to provide indoor fresh air for occupants. Due to the public awareness on environment impact, people has been increasingly attentive to passive design in achieving good condition of indoor building ventilation. Throughout case studies, significant building attributes were found giving effect on building indoor natural ventilation performance. The studies were categorized under vernacular houses, contemporary houses with vernacular element and contemporary houses. The indoor air movement of every each spaces in the houses were compared with the outdoor air movement surrounding the houses to indicate the spaceโ€™s indoor natural ventilation performance. Analysis found the wind catcher element appears to be the most significant attribute to contribute most to indoor natural ventilation. Wide opening was also found to be significant especially those with louvers. Whereas it is also interesting to find indoor layout design is also significantly giving impact on the performance. The finding indicates that a good indoor natural ventilation is not only dictated by having proper openings at proper location of a building, but also on how the incoming air movement is managed throughout the interior spaces by proper layout. Understanding on the air pressure distribution caused by indoor windward and leeward side is important in directing the air flow to desired spaces in producing an overall good indoor natural ventilation performance
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