• Journal of Broadcast Engineering
• /
• v.16 no.2
• /
• pp.339-349
• /
• 2011

IBDI (Internal Bit Depth Increase) is able to significantly improve the coding efficiency of high definition video compression by increasing the bit depth (or precision) of internal arithmetic operation. However the scheme also increases required internal memory for storing decoded reference frames and this can be significant for higher definition of video contents. So, the reference frame memory compression method is proposed to reduce such internal memory requirement. The reference memory compression is performed on 4x4 block called the processing unit to compress the decoded image using the correlation of nearby pixel values. This method has successively reduced the reference frame memory while preserving the coding efficiency of IBDI. However, additional information of each processing unit has to be stored also in internal memory, the amount of additional information could be a limitation of the effectiveness of memory compression scheme. To relax this limitation of previous memory compression scheme, we propose a selective merging-based reference frame memory compression algorithm, dramatically reducing the amount of additional information. Simulation results show that the proposed algorithm provides much smaller overhead than that of the previous algorithm while keeping the coding efficiency of IBDI.

• Korean Journal of Remote Sensing
• /
• v.22 no.2
• /
• pp.123-129
• /
• 2006

High-resolution aerial color image offers great possibilities for geometric and semantic information for spatial data generation. However, shadow casts by buildings and trees in high-density urban areas obscure much of the information in the image giving rise to potentially inaccurate classification and inexact feature extraction. Though many researches have been implemented for solving shadow casts, few studies have been carried out about the extraction of features hindered by shadows from aerial color images in urban areas. This paper presents a asphalt road boundary extraction technique that combines information from aerial color image and LIDAR (LIght Detection And Ranging) data. The following steps have been performed to remove shadow effects and to extract road boundary from the image. First, the shadow regions of the aerial color image are precisely located using LEAR DSM (Digital Surface Model) and solar positions. Second, shadow regions assumed as road are corrected by shadow path reconstruction algorithms. After that, asphalt road boundary extraction is implemented by segmentation and edge detection. Finally, asphalt road boundary lines are extracted as vector data by vectorization technique. The experimental results showed that this approach was effective and great potential advantages.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.28 no.2
• /
• pp.247-254
• /
• 2010

In this paper, a new approach has been studied correcting the geometric distortion of SPOT 4 imagery. Two new equations were induced by the relationship between satellite and the Earth in the space. line-of-sight (LOS) vector adjustment model for SPOT 4 imagery was implemented in this study. This model is to adjust LOS vector under the assumption that the orbital information of satellite provided by receiving station is uncertain and this uncertainty makes a constant error over the image. This model is verified using SPOT 4 satellite image with high look angle and thirty five ground points, which include 10 GCPs(Ground Control Points) and 25 check points, measured by the GPS. In total thirty five points, the geometry of satellite image calculated by given satellite information(such as satellite position, velocity, attitude and look angles, etc) from SPOT 4 satellite image was distorted with a constant error. Through out the study, it was confirmed that the LOS vector adjustment model was able to be applied to SPOT4 satellite image. Using this model, RMSEs (Root Mean Square Errors) of twenty five check points taken by increasing the number of GCPs from two to ten were less than one pixel. As a result, LOS vector adjustment model could efficiently correct the geometry of SPOT4 images with only two GCPs. This method also is expected to get good results for the different satellite images that are similar to the geometry of SPOT images.

• Journal of Korean Society for Geospatial Information Science
• /
• v.18 no.2
• /
• pp.3-12
• /
• 2010

In previous studies, the digital measurement systems and analysis algorithms were developed by using the related techniques, such as the aerial photograph detection and high resolution satellite image process. However, these studies were limited in 2-dimensional geo-processing. Therefore, it is necessary to apply the 3-dimensional spatial information and coordinate system for higher accuracy in recognizing and locating of geo-features. The objective of this study was to develop a stochastic algorithm for the optimal sensor placement using the 3-dimensional spatial analysis method. The 3-dimensional information of the LiDAR was applied in the sensor field algorithm based on 2- and/or 3-dimensional gridded points. This study was conducted with three case studies using the optimal sensor placement algorithms; the first case was based on 2-dimensional space without obstacles(2D-non obstacles), the second case was based on 2-dimensional space with obstacles(2D-obstacles), and lastly, the third case was based on 3-dimensional space with obstacles(3D-obstacles). Finally, this study suggested the methodology for the optimal sensor placement - especially, for ground-settled sensors - using the LiDAR data, and it showed the possibility of algorithm application in the information collection using sensors.

• Korean Journal of Remote Sensing
• /
• v.36 no.5_4
• /
• pp.1179-1194
• /
• 2020

This study investigates the feasibility of three algorithms, K-Nearest Neighbors (K-NN), Random Forest (RF) and Neural Network (NN), for estimating the air temperature of an unobserved area where the weather station is not installed. The satellite image were obtained from Landsat-8 and MODIS Aqua/Terra acquired in 2019, and the meteorological ground weather data were from AWS/ASOS data of Korea Meteorological Administration and Korea Forest Service. In addition, in order to improve the estimation accuracy, a digital surface model, solar radiation, aspect and slope were used. The accuracy assessment of machine learning methods was performed by calculating the statistics of R² (determination coefficient) and Root Mean Square Error (RMSE) through 10-fold cross-validation and the estimated values were compared for each target area. As a result, the neural network algorithm showed the most stable result among the three algorithms with R² = 0.805 and RMSE = 0.508. The neural network algorithm was applied to each data set on Landsat imagery scene. It was possible to generate an mean air temperature map from June to September 2019 and confirmed that detailed air temperature information could be estimated. The result is expected to be utilized for national disaster safety management such as heat wave response policies and heat island mitigation research.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.7 no.1
• /
• pp.115-123
• /
• 2005

Quantitative understanding of spatial characteristics of the study site is a prerequisite to investigate water and carbon cycles in agricultural and forest ecosystems, particularly with complex, heterogeneous landscapes. The spatial characteristics of variables related with topography, vegetation and soil in Gwangneung forest watershed are quantified in this study. To characterize topography, information on elevation, slope and aspect extracted from DEM is analyzed. For vegetation and soil, a land-cover map classified from LANDSAT TM images is used. Four satellite images are selected to represent different seasons (30 June 1999, 4 September 2000, 23 September 2001 and 14 February 2002). As a flux index for CO₂ and water vapor, normalized difference vegetation index (NDVI) is calculated from satellite images for three different grid sizes: MODIS grid (7km x 7km), intensive observation grid (3km x 3km), and unit grid (1km x 1km). Then, these data are analyzed to quantify the spatial scale of heterogeneity based on semivariogram analysis. As expected, the scale of heterogeneity decreases as the grid size decreases and are sensitive to seasonal changes in vegetation. For the two unit grids where the two 40 m flux towers are located, the spatial scale of heterogeneity ranges from 200 to 1,000m, which correspond well to the climatology of the computed tower flux footprint.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.10a
• /
• pp.755-758
• /
• 2014

In this paper, we propose effective Deblocking Filter hardware architecture for High Efficiency Video Coding encoder. we propose Deblocking Filter hardware architecture with less processing time, filter ordering for low area design, effective memory architecture and four-pipeline for a high performance HEVC(High Efficiency Video Coding) encoder. Proposed filter ordering can be used to reduce delay according to preprocessing. It can be used for realtime single-port SRAM read and write. it can be used in parallel processing by using two filters. Using 10 memory is effective for solving the hazard caused by a single-port SRAM. Also the proposed filter can be used in low-voltage design by using clock gating architecture in 4-pipeline. The proposed Deblocking Filter encoder architecture is designed by Verilog HDL, and implemented by 100k logic gates in TSMC $0.18{\mu}m$ process. At 150MHz, the proposed Deblocking Filter encoder can support 4K Ultra HD video encoding at 30fps, and can be operated at a maximum speed of 200MHz.

• The KIPS Transactions:PartC
• /
• v.17C no.2
• /
• pp.197-204
• /
• 2010

An effective integration of web-based learning environment and mobile device technology is considered as a new challenge to the developers. The screen size, however, of the mobile device is too small, and its performance is too inferior. Due to the foregoing limit of mobile technology, displaying bulk data on the mobile screen, such as a cyber lecture accompanied with real-time image transmission on the web, raises a lot of problems. Users have difficulty in recognizing learning contents exactly by means of a mobile device, and continuous transmission of video stream with bulky information to the mobile device arouses a lot of load for the mobile system. Thus, an application which is developed to be applied in PC is improper to be used for the mobile device as it is, a player which is fitting for the mobile device should be developed. Accordingly, this paper suggests mobile presentation using transcoding techniques of the field concerned. To display continuous video frames of learning image, such as a cyber lecture or remote lecture, by means of a mobile device, the performance difference between high-resolution digital image and mobile device should be surmounted. As the transcoding techniques to settle the performance difference causes damage of image quality, high-quality image may be guaranteed by application of trial and error between transcoding and selected learning resources.

• Korean Journal of Remote Sensing
• /
• v.35 no.2
• /
• pp.343-346
• /
• 2019

This letter describes a development of satellite-based forest withering index for detection of fire burn area and its application to the Goseong-Sokcho and Gangneung-Donghae wildfires in April 4, 2019. Withered forest has very different spectral characteristics from healthy forest. In particular, a false color composite of R-NIR-G represents such difference very clearly. Using Sentinel-2 images with the forest withering index, we derived the area burned by the wildfires: approximately 701.16 ha for Goseong-Sokcho and approximately 710.60 ha for Gangneung-Donghae, although official record will be announced by the Korean government later.

• Korean Journal of Cognitive Science
• /
• v.33 no.2
• /
• pp.109-133
• /
• 2022

Episodic memory consists of a core event and the associated contexts. Although the role of the hippocampus and its neighboring regions in contextual representations during encoding has become increasingly evident, it remains unclear how these regions handle various context-specific information other than spatio-temporal contexts. Using high-resolution functional MRI, we explored the patterns of the medial temporal lobe (MTL) and cortical regions' involvement during the encoding of various types of contextual information (i.e., journalism principle 5W1H): "Who did it?," "Why did it happen?," "What happened?," "When did it happen?," "Where did it happen?," and "How did it happen?" Participants answered six different contextual questions while looking at simple experimental events consisting of two faces with one object on the screen. The MTL was divided to sub-regions by hierarchical clustering from resting-state data. General linear model analyses revealed a stronger activation of MTL sub-regions, the prefrontal lobe (PFC), and the inferior parietal lobule (IPL) during social (Who), behavioral (How), and intentional (Why) contextual processing when compared with spatio-temporal (Where/When) contextual processing. To further investigate the functional networks involved in contextual encoding dissociation, a multivariate pattern analysis was conducted with features selected as the task-based connectivity links between the hippocampal subfields and PFC/IPL. Each social, behavioral, and intentional contextual processing was individually and successfully classified from spatio-temporal contextual processing, respectively. Thus, specific contexts in episodic memory, namely social, behavior, and intention, involve distinct functional connectivity patterns that are distinct from those for spatio-temporal contextual memory.