• Journal of Broadcast Engineering
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• v.8 no.3
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• pp.268-277
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• 2003

In this paper, we propose a new video watermarking method exploiting the human visual system (HVS) to find effective locations. in the video frames which make the watermark robust and imperceptible simultaneously. In particular, we propose a new HVS-optimized weighting map for hiding the watermark by considering HVS in three different aspects : frequency, spatial, and motion masking effects. The global masking map is modeled by combining the frequency masking, the spatial masking, and the motion masking. In this paper, we use a watermark which is generated by the bitwise exclusive-OR operation between a logo image and a random sequence. The amount of watermarks is weighted by a control parameter. Furthermore, we embed the watermark in the uncompressed video sequence for the general watermarking method available to various coding schemes. Simulation results show that the watermark is imperceptible and the proposed method is good for watermark capacity. It is also demonstrated that the proposed method is robust against various attacks, such as MPEG coding, MPEG re-encoding, and frame attacks.

• Journal of Korean Society for Geospatial Information Science
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• v.16 no.2
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• pp.49-56
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• 2008

Image registration is one of the critical techniques of image mosaic which has many applications such as generating panoramas, video monitoring, image rendering and reconstruction, etc. The fundamental tasks of image registration are point features extraction and tracking which take much computation time. KLT(Kanade-Lucas-Tomasi) feature tracker has proposed for extracting and tracking features through image sequences. The aim of this study is to demonstrate the usage of effective and robust KLT feature detector and tracker for an image registration using the sequence image frames captured by UAV video camera. In result, by using iterative implementation of the KLT tracker, the features extracted from the first frame of image sequences could be successfully tracked through all frames. The process of feature tracking in the various frames with rotation, translation and small scaling could be improved by a careful choice of the process condition and KLT pyramid implementation.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.77-85
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• 2006

There is the spatial correlation of the video sequence between the motion vector of current blocks. In this paper, we propose the enhanced fast block matching algorithm using the spatial correlation of the video sequence and the center-biased properly of motion vectors. The proposed algorithm determines an exact motion vector using the predicted motion vector from the adjacent macro blocks of the current frame and the Cross-Hexagonal search pattern. From the of experimental results, we can see that our proposed algorithm outperforms both the prediction search algorithm (NNS) and the fast block matching algorithm (CHS) in terms of the search speed and the coded video's quality. Using our algorithm, we can improve the search speed by up to $0.1{\sim}38%$ and also diminish the PSNR (Peak Signal Noise Ratio) by at nst $0.05{\sim}2.5dB$, thereby improving the video qualify.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.45-53
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• 2009

The fast progress on multimedia data acquisition technologies has enabled collecting vast amount of videos in real time. Although the amount of information gathered from these videos could be high in terms of quantity and quality, the use of the collected data is very limited typically by human-centric monitoring systems. In this paper, we propose a framework for analyzing long traffic video using series of content-based analyses tools. Our framework suggests a method to integrate theses analyses tools to extract highly informative features specific to a traffic video analysis. Our analytical framework provides (1) re-sampling tools for efficient and precise analysis, (2) foreground extraction methods for unbiased traffic flow analysis, (3) frame property analyses tools using variety of frame characteristics including brightness, entropy, Harris corners, and variance of traffic flow, and (4) a visualization tool that summarizes the entire video sequence and automatically highlight a collection of frames based on some metrics defined by semi-automated or fully automated techniques. Based on the proposed framework, we developed an automated traffic flow analysis system, and in our experiments, we show results from two example traffic videos taken from different monitoring angles.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3239-3267
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• 2018

Image and video dehazing is a popular topic in the field of computer vision and digital image processing. A fast, optimized dehazing algorithm was recently proposed that enhances contrast and reduces flickering artifacts in a dehazed video sequence by minimizing a cost function that makes transmission values spatially and temporally coherent. However, its fixed-size block partitioning leads to block effects. The temporal cost function also suffers from the temporal non-coherence of newly appearing objects in a scene. Further, the weak edges in a hazy image are not addressed. Hence, a video dehazing algorithm based on well designed spectral clustering is proposed. To avoid block artifacts, the spectral clustering is customized to segment static scenes to ensure the same target has the same transmission value. Assuming that edge images dehazed with optimized transmission values have richer detail than before restoration, an edge intensity function is added to the spatial consistency cost model. Atmospheric light is estimated using a modified quadtree search. Different temporal transmission models are established for newly appearing objects, static backgrounds, and moving objects. The experimental results demonstrate that the new method provides higher dehazing quality and lower time complexity than the previous technique.

• Journal of Korea Multimedia Society
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• v.6 no.5
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• pp.797-804
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• 2003

Scene change detection algorithm, which is very important preprocessing technique for video indexing and retrieval and determines the performance of video database system, is being studied widely. In this paper, we propose a more effective abrupt scene change detection, which is robust to large motion, sudden change of light and successive abrupt shot transitions rapidly. And we also propose a new gradual scene change detection algorithm, which can detect dissolve, and fade in/out precisely. Furthermore, we also propose a representative frame extraction algorithm which performs content-based video summary by novel DCT DC image buffering technique and accumulative histogram intersection measure (AHIM).

• Journal of Communications and Networks
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• v.6 no.3
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• pp.280-286
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• 2004

The widespread use of the Internet and the maturing of digital video technology have led to an increase in various streaming media application. However, new classes of hosts such as mobile devices are gaining popularity, while the transmission became more heterogeneous. Due to the characteristics of mobile networks such as low speed, high error bit rate, etc., the applications over the wireless channel have different needs and limitations from desktop computers. An intermediary between two communicating endpoints to hide the heterogeneous network links is thought as one of the best approaches. In this paper, we adopted the concept of inter-packet gap and the sequence number between continuously received packets as the error discriminator, and designed an adaptive packet sizing mechanism to improve the network efficiency under varying channel conditions. Based on the proposed mechanism, the packetization scheme with error protection is proposed to scalable encoded video delivery. Finally, simulation results reveal that our proposed mechanism can react to the varying BER conditions with better network efficiency and gain the obvious improvement to video quality for stored MPEG-4 FGS video delivery.

• The Transactions of the Korea Information Processing Society
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• v.7 no.2S
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• pp.577-593
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• 2000

A distributed video-on-demand (VoD) system is one where collection of video data is located at dispersed sites across a computer network. In a single site environment, a local video server retrieves video data from its local storage device. However, in the setting of a distributed VoD system, when a customer requests a movie from the local server, the server may need to interact with other servers located across the network. In this paper, we present three types of presentation plans that a local server must construct in order to satisfy the customer request. Informally speaking, a presentation plan is a temporally synchronized detailed sequence of steps that the local server must perform for presenting the requested movie to the customer. This involves obtaining commitments from other video servers, obtaining commitments from the network service provider, as well as making commitments of local resources, within the limitations of available bandwidth, available buffer, and customer data consumption rates. Furthermore, for evaluating the goodness of a presentation plan, we introduce two measures of optimality for presentation plans: minimizing wait time for a customer, and minimizing access bandwidth is used. We develop algorithms to compute optimal presentation plans for all three types, and carry out extensive experiments to compare their performance. We have also mathematically proved certain results for the presentation plans that had previously been verified experimentally in the literature.

• Journal of KIISE:Software and Applications
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• v.33 no.10
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• pp.851-861
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• 2006

The paper proposes a novel unsupervised video object segmentation algorithm for image sequences with low depth-of-field (DOF), which is a popular photographic technique enabling to represent the intention of photographer by giving a clear focus only on an object-of-interest (OOI). The proposed algorithm largely consists of two modules. The first module automatically extracts OOIs from the first frame by separating sharply focused OOIs from other out-of-focused foreground or background objects. The second module tracks OOIs for the rest of the video sequence, aimed at running the system in real-time, or at least, semi-real-time. The experimental results indicate that the proposed algorithm provides an effective tool, which can be a basis of applications, such as video analysis for virtual reality, immersive video system, photo-realistic video scene generation and video indexing systems.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.4
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• pp.79-89
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• 2010

Recently, distributed video coding (DVC) has been actively studied for low complexity video encoder. The complexity of the encoder in DVC is much simpler than that of traditional video coding schemes such as H.264/AVC, but the complexity of the decoder in DVC increases. In this paper, we propose the Region-Of-Interest (ROI) based DVC with low decoding complexity. The proposed scheme uses the ROI, the region the motion of objects is quickly moving as the input of the Wyner-Ziv (WZ) encoder instead of the whole WZ frame. In this case, the complexity of encoder and decoder is reduced, and the bite rate decreases. Experimental results show that the proposed scheme obtain 0.95 dB as the maximum PSNR gain in Hall Monitor sequence and 1.87 dB in Salesman sequence. Moreover, the complexity of encoder and decoder in the proposed scheme is significantly reduced by 73.7% and 63.3% over the traditional DVC scheme, respectively. In addition, we employ the layered belief propagation (LBP) algorithm whose decoding convergence speed is 1.73 times faster than belief propagation algorithm as the Low-Density Parity-Check (LDPC) decoder for low decoding complexity.