• Journal of Broadcast Engineering
• /
• v.18 no.2
• /
• pp.271-282
• /
• 2013

Recently, ITU-T/VCEG and ISO/IEC MPEG have started a new joint standardization activity on video coding, called High Efficiency Video Coding (HEVC). This new standard gives significant improvement in terms of picture quality for high resolution video. The main challenge in this upcoming standard is the time complexity. In this paper we have focused on CU splitting algorithm. We have proposed a novel algorithm which can terminate the CU splitting process early based on the RD cost of the parent and current level and the motion vector value of the current CU. Experimental result shows that our proposed algorithm gives on average more than about 10% decrement in time over ECU [8] with on average 1.78% of BD loss on the original.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.43 no.4 s.310
• /
• pp.74-86
• /
• 2006

This paper proposes a robust and computationally efficient algorithm for automatic video object segmentation. For implementing the spatio-temporal segmentation, which aims for efficient combination of the motion segmentation and the color segmentation, an SOM-based hierarchical clustering method in which the segmentation process is regarded as clustering of feature vectors is employed. As results, problems of high computational complexity which required for obtaining exact segmentation results in conventional video object segmentation methods, and the performance degradation due to noise are significantly reduced. A measure of motion vector reliability which employs MRF-based MAP estimation scheme has been introduced to minimize the influence from the motion estimation error. In addition, a noise elimination scheme based on the motion reliability histogram and a clustering validity index for automatically identifying the number of objects in the scene have been applied. A cross projection method for effective object tracking and a dynamic memory to maintain temporal coherency have been introduced as well. A set of experiments has been conducted over several video sequences to evaluate the proposed algorithm, and the efficiency in terms of computational complexity, robustness from noise, and higher segmentation accuracy of the proposed algorithm have been proved.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.46 no.11
• /
• pp.66-76
• /
• 2009

The motion estimation in video encoding system occupies the biggest part. So, we require the motion estimator with efficient structure for real-time operation. And for motion estimator's implementation, it is desired to design hardware module of an exclusive use that perform the encoding process at high speed. This paper proposes motion estimation detection block(MED), 41 SADs(Sum of Absolute Difference) calculation block, minimum SAD calculation and motion vector generation block based on parallel processing. The parallel processing can reduce effectively the amount of the operation. The minimum SAD calculation and MED block uses the pre-computation technique for reducing switching activity of the input signal. It results in high-speed operation. The MED and 41 SADs calculation blocks are composed of adder tree which causes the problem of critical path. So, the structure of adder tree has changed the most commonly used ripple carry adder(RCA) with carry skip adder(CSA). It enables adder tree to operate at high speed. In addition, as we enabled to easily control key variables such as control signal of search range from the outside, the efficiency of hardware structure increased. Simulation and FPGA verification results show that the delay of MED block generating the critical path at the motion estimator is reduced about 19.89% than the conventional strukcture.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.15 no.2
• /
• pp.23-36
• /
• 2005

The main obstacle hindering the wide deployment of identity-based cryptosystem is that the entity responsible for creating the private key has too much power. As a result, private keys are no longer private. One obvious solution to this problem is to apply the threshold technique. However, this increases the authentication computation, and communication cost during the key issuing phase. In this paper, we propose a new effi ient model for issuing multiple private keys in identity-based encryption schemes based on the Weil pairing that also alleviates the key escrow problem. In our system, the private key of a user is divided into two components, KGK (Key Description Key) and KUD(Key Usage Desscriptor), which are issued separately by different parties. The KGK is issued in a threshold manner by KIC (Key Issuing Center), whereas the KW is issued by a single authority called KUM (Key Usage Manager). Changing KW results in a different private key. As a result, a user can efficiently obtain a new private key by interacting with KUM. We can also adapt Gentry's time-slot based private key revocation approach to our scheme more efficiently than others. We also show the security of the system and its efficiency by analyzing the existing systems.

• Journal of Broadcast Engineering
• /
• v.27 no.3
• /
• pp.318-331
• /
• 2022

With the recent development of hardware computing devices and software based frameworks, machine tasks using deep learning networks are expected to be utilized in various industrial fields and personal IoT devices. However, in order to overcome the limitations of high cost device for utilizing the deep learning network and that the user may not receive the results requested when only the machine task results are transmitted from the server, Collaborative Intelligence (CI) proposed the transmission of feature maps as a solution. In this paper, an efficient compression method for feature maps with vast data sizes to support the CI paradigm was analyzed and presented through experiments. This method increases redundancy by applying feature map reordering to improve compression efficiency in traditional video codecs, and proposes a feature map method that improves compression efficiency and maintains the performance of machine tasks by simultaneously utilizing image compression format and video compression format. As a result of the experiment, the proposed method shows 14.29% gain in BD-rate of BPP and mAP compared to the feature compression anchor of MPEG-VCM.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.39C no.10
• /
• pp.948-956
• /
• 2014

Today, Intelligent Vehicle Detection System seeks to reduce the negative factors, such as accidents over to get the traffic information of existing system. This paper proposes detection algorithm for the illegal U-turn vehicles which can cause critical accident among violations of road traffic laws. We predicted that if calculated optical flow vectors were shown on the illegal U-turn path, they would be cause of the illegal U-turn vehicles. To reduce the high computational complexity, we use the algorithm of pyramid Lucas-Kanade. This algorithm only track the key-points likely corners. Because of the high computational complexity, we detect center lane first through the color information and progressive probabilistic hough transform and apply to the around of center lane. And then we select vectors on illegal U-turn path and calculate reliability to check whether vectors is cause of the illegal U-turn vehicles or not. Finally, In order to evaluate the algorithm, we calculate process time of the type of algorithm and prove that proposed algorithm is efficiently.

• Journal of Korean Society of Transportation
• /
• v.28 no.3
• /
• pp.61-74
• /
• 2010

ITS (Intelligent transportation systems) collects real-time traffic data, and accumulates vest historical data. But tremendous historical data has not been managed and employed efficiently. With the introduction of data management systems like ADMS (Archived Data Management System), the potentiality of huge historical data dramatically surfs up. However, traffic data in any data management system includes missing values in nature, and one of major obstacles in applying these data has been the missing data because it makes an entire dataset useless every so often. For these reasons, imputation techniques take a key role in data management systems. To address these limitations, this paper presents a promising imputation technique which could be mounted in data management systems and robustly generates the estimations for missing values included in historical data. The developed model, based on NPR (Non-Parametric Regression) approach, employs various traffic data patterns in historical data and is designated for practical requirements such as the minimization of parameters, computational speed, the imputation of various types of missing data, and multiple imputation. The model was tested under the conditions of various missing data types. The results showed that the model outperforms reported existing approaches in the side of prediction accuracy, and meets the computational speed required to be mounted in traffic data management systems.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.3
• /
• pp.630-637
• /
• 2018

Harmony search (HS) is a recently developed metaheuristic optimization algorithm. HS is inspired by the process of musical improvisation and repeatedly searches for the optimal solution using three operations: random selection, memory recall (or harmony memory consideration), and pitch adjustment. HS has been applied by many researchers in various fields. The increasing complexity of real-world optimization problems has created enormous challenges for the current technique, and improved techniques of optimization algorithms and HS are required. We propose an improved clustering harmony search (ICHS) that uses a clustering technique to group solutions in harmony memory based on their objective function values. The proposed ICHS performs modified harmony memory consideration in which decision variables of solutions in a high-ranked cluster have higher probability of being selected than those in a low-ranked cluster. The ICHS is demonstrated in various optimization problems, including mathematical benchmark functions and water distribution system pipe design problems. The results show that the proposed ICHS outperforms other improved versions of HS.

• Journal of KIISE:Computer Systems and Theory
• /
• v.35 no.1
• /
• pp.18-29
• /
• 2008

NAND Flash memory has been growing popular storage device for the last years because of its low power consumption, fast access speed, shock resistance and light weight properties. However, it has the distinct characteristics such as erase-before-write architecture, asymmetric read/write/erase speed, and the limitation on the number of erasure per block. Due to these limitations, various Flash Translation Layers (FTLs) have been proposed to effectively use NAND flash memory. The systems that adopted the conventional FTL may result in severe performance degradation by the hot data which are frequently requested data for overwrite in the same logical address. In this paper, we propose a novel FTL algorithm called Adaptive Flash Translation Layer (AFTL) which uses sector mapping method for hot data and log-based block mapping method for cold data. Our system removes the redundant write operations and the erase operations by the separating hot data from cold data. Moreover, the read performance is enhanced according to sector translation that tends to use a few read operations. A series of experiments was organized to inspect the performance of the proposed method, and they show very impressive results.

• Journal of Broadcast Engineering
• /
• v.14 no.2
• /
• pp.238-252
• /
• 2009

Due to the rapid increase of available contents via the convergence of broadcasting and internet, the efficient access to personally preferred contents has become an important issue. In this paper, for recommendation scheme for TV programs using a collaborative filtering technique is studied. For recommendation of user preferred TV programs, our proposed recommendation scheme consists of offline and online computation. About offline computation, we propose reasoning implicitly each user's preference in TV programs in terms of program contents, genres and channels, and propose clustering users based on each user's preferences in terms of genres and channels by dynamic fuzzy clustering method. After an active user logs in, to recommend TV programs to the user with high accuracy, the online computation includes pulling similar users to an active user by similarity measure based on the standard preference list of active user and filtering-out of the watched TV programs of the similar users, which do not exist in EPG and ranking of the remaining TV programs by proposed rank model. Especially, in this paper, the BM (Best Match) algorithm is extended to make the recommended TV programs be ranked by taking into account user's preferences. The experimental results show that the proposed scheme with the extended BM model yields 62.1% of prediction accuracy in top five recommendations for the TV watching history of 2,441 people.