• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3165-3181
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• 2019

Spherical videos, which are also called 360-degree videos, have become increasingly popular due to the rapid development of virtual reality technology. However, the large amount of data in such videos is a huge challenge for existing transmission system. To use the existing encode framework, it should be converted into a 2D image plane by using a specific projection format, e.g. the equi-rectangular projection (ERP) format. The existing high-efficiency video coding standard (HEVC) can effectively compress video content, but its enormous computational complexity makes the time spent on compressing high-frame-rate and high-resolution 360-degree videos disproportionate to the benefits of compression. Focusing on the ERP format characteristics of 360-degree videos, this work develops a fast decision algorithm for predicting the coding unit depth interval and adaptive mode decision for intra prediction mode. The algorithm makes full use of the video characteristics of the ERP format by dealing with pole and equatorial areas separately. It sets different reference blocks and determination conditions according to the degree of stretching, which can reduce the coding time while ensuring the quality. Compared with the original reference software HM-16.16, the proposed algorithm can reduce time consumption by 39.3% in the all-intra configuration, and the BD-rate increases by only 0.84%.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.5
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• pp.323-326
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• 2016

High Efficiency Video Coding (HEVC) adopts intra transform skip mode, in which a residual block is directly quantized in the pixel domain without transforming the block into the frequency domain. Intra transform skip mode provides a significant coding gain for screen content. However, when intra-prediction errors are not transformed, the errors are often correlated along the intra-prediction direction. This paper introduces a residual differential pulse code modulation (DPCM) method for the intra-predicted and transform-skipped blocks to remove redundancy. The proposed method performs pixel-by-pixel residual prediction along the intra-prediction direction to reduce the dynamic range of intra-prediction errors. Experimental results show that the transform skip mode's Bjøntegaard delta rate (BD-rate) is improved by 12.8% for vertically intra-predicted blocks. Overall, the proposed method shows an average 1.2% reduction in BD-rate, relative to HEVC, with negligible computational complexity.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.234-236
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• 2020

Content-adaptive training and transmission of the model parameters of neural networks can boost up the SR performance with higher restoration fidelity. In this case, efficient transmission of neural network parameters are essentially needed. Thus, we propose a novel method of compressing the network model parameters based on the training of network model parameters in the sense that the residues of filter parameters and content loss are jointly minimized. So, the residues of filter parameters are only transmitted to receiver sides for different temporal portions of video under consideration. This is advantage for image restoration applications with receivers (user terminals) of low complexity. In this case, the user terminals are assumed to have a limited computation and storage resource.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.4160-4176
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• 2015

Data compression like image and video compression has come a long way since the introduction of Compressive Sensing (CS) which compresses sparse signals such as images, videos etc. to very few samples i.e. M < N measurements. At the receiver end, a robust and efficient recovery algorithm estimates the original image or video. Many prominent algorithms solve least squares problem (LSP) iteratively in order to reconstruct the signal hence consuming more processing time. In this paper non-iterative threshold based recovery algorithm (NITRA) is proposed for the recovery of images and videos without solving LSP, claiming reduced complexity and better reconstruction quality. The elapsed time for images and videos using NITRA is in ㎲ range which is 100 times less than other existing algorithms. The peak signal to noise ratio (PSNR) is above 30 dB, structural similarity (SSIM) and structural content (SC) are of 99%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.6
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• pp.2271-2288
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• 2015

The emerging high efficiency video coding (HEVC) standard adopts the quadtree-structured transform unit (TU) in the residual quadtree (RQT) coding. Each TU allows to be split into four equal sub-TUs recursively. The RQT coding is performed for all the possible transform depth levels to achieve the highest coding efficiency, but it requires a very high computational complexity for HEVC encoders. In order to reduce the computational complexity requested by the RQT coding, in this paper, we propose a fast TU size decision method incorporating an adaptive maximum transform depth determination (AMTD) algorithm and a full check skipping - early termination (FCS-ET) algorithm. Because the optimal transform depth level is highly content-dependent, it is not necessary to perform the RQT coding at all transform depth levels. By the AMTD algorithm, the maximum transform depth level is determined for current treeblock to skip those transform depth levels rarely used by its spatially adjacent treeblocks. Additionally, the FCS-ET algorithm is introduced to exploit the correlations of transform depth level between four sub-CUs generated by one coding unit (CU) quadtree partitioning. Experimental results demonstrate that the proposed overall algorithm significantly reduces on average 21% computational complexity while maintaining almost the same rate distortion (RD) performance as the HEVC test model reference software, HM 13.0.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.6
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• pp.736-745
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• 2002

In network delivery of compressed video, packets may be lost if the channel is unreliable like Internet. Such losses tend to of cur in burst like continuous bit-stream error. In this paper, we propose an effective error-concealment approach to which an error resilient video encoding approach is applied against burst errors and which reduces a complexity of error concealment at the decoder using data hiding. To improve the performance of error concealment, a temporal and spatial error resilient video encoding approach at encoder is developed to be robust against burst errors. For spatial area of error concealment, block shuffling scheme is introduced to isolate erroneous blocks caused by packet losses. For temporal area of error concealment, we embed parity bits in content data for motion vectors between intra frames or continuous inter frames and recovery loss packet with it at decoder after transmission While error concealment is performed on error blocks of video data at decoder, it is computationally costly to interpolate error video block using neighboring information. So, in this paper, a set of feature are extracted at the encoder and embedded imperceptibly into the original media. If some part of the media data is damaged during transmission, the embedded features can be extracted and used for recovery of lost data with bi-direction interpolation. The use of data hiding leads to reduced complexity at the decoder. Experimental results suggest that our approach can achieve a reasonable quality for packet loss up to 30% over a wide range of video materials.

• The Journal of Information Technology and Database
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• v.6 no.2
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• pp.19-28
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• 1999

Digital image and video libraries require new algorithms for the automated extraction and indexing of salient image features. Eigen values of an image provide one important cue for the discrimination of image content. In this paper we propose a new approach for automated content extraction that allows efficient database searching using eigen values. The algorithm automatically extracts eigen values from the image matrix represented by the covariance matrix for the image. We demonstrate that the eigen values representing shape information and the skewness of its distribution representing complexity provide good performance in image query response time while providing effective discriminability. We present the eigen value extraction and indexing techniques. We test the proposed algorithm of searching by eigen value and its skewness on a database of 100 images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.12
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• pp.2865-2874
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• 2013

Recently, most of movies top-ranked in the box office are screening in Stereoscopic 3D, and the world's leading electronics companies such as Samsung and LG are getting the hots for 3DTV sales. However, each person has different binocular disparity and different viewing distance, and thus he or she feels the severe visual fatigue and headaches if he or she is watching 3D content with the same binocular disparity, which is very different from things he or she feels in the real world. To solve this problem, this paper proposes and implement a 3D rendering system that correct the disparity of 3D content by reflecting binocular distance and viewing distance. Then, the computational complexity is analyzed. Optical-flow and Warping algorithms turn out to consume 732 seconds and 5.7 seconds per frame, respectively. Therefore, a dedicated chip-set for both blocks is strongly required for real-time HD 3D display.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.4 s.304
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• pp.133-142
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• 2005

In this paper, two digital video scrambling methods are proposed as simple means of the digital content protection techniques. One is inter block scrambling using motion vector, the other is intra block scrambling using intra prediction mode. The proposed inter block scrambling method distorts the original sequences by swapping horizontal and vertical components of motion vector. This method can be applied on most common video coding techniques such as MPEG-1, 2, 4, H.264, etc. The proposed intra block scrambling method distorts the original sequences by modifying intra prediction mode that is property of H.254 video coding technique. Both methods do not cause my bit rate increase after scrambling. Moreover, they have low complexity because they need only simple operation like XOR. Especially, the proposed intra block scrambling does not distort inter blocks directly. But inter blocks are distorted by error propagation effect as much as intra blocks. This paper introduces two new digital video scrambling method and verifies its effectiveness through simulation.

• Journal of the Korea Society of Computer and Information
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• v.18 no.11
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• pp.77-86
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• 2013

In this paper, we propose a fast video retargeting method which preserves the contents of a video and converts the image size. Since the conventional Seam Carving which is the well-known content-aware image retargeting technique uses the dynamic programming method, the repetitive update procedure of the accumulation energy is absolutely needed to obtain seam. The energy update procedure cannot avoid the processing time delay because of many operations by the image full-searching. By applying the proposed method, frames which have similar features in video are classified into a scene, and the first frame of a scene is resized by the modified Seam Carving where multiple seams are extracted from candidate seams to reduce the repetitive update procedure. After resizing the first frame of a scene, all continuous frames of the same scene are resized with reference to the seam information stored in the previous frame without the calculation of the accumulation energy. Therefore, although the fast processing is possible with reducing complexity and without analyzing all frames of scene, the quality of an image can be analogously maintained with an existing method. The experimental results show that the proposed method can preserve the contents of an image and can be practically applied to retarget the image on real time.