• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.73-76
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• 2023

We introduce DCNN and DRAE appraoches for compression of medical videos, in order to decrease file size and storage requirements, there is an increasing need for medical video compression nowadays. Using a lossy compression technique, a higher compression ratio can be attained, but information will be lost and possible diagnostic mistakes may follow. The requirement to store medical video in lossless format results from this. The aim of utilizing a lossless compression tool is to maximize compression because the traditional lossless compression technique yields a poor compression ratio. The temporal and spatial redundancy seen in video sequences can be successfully utilized by the proposed DCNN and DRAE encoding. This paper describes the lossless encoding mode and shows how a compression ratio greater than 2 (2:1) can be achieved.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.825-828
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• 2022

In this paper, we propose an efficient image size selection method for video-based point cloud compression. The current MPEG video-based point cloud compression reference encoding process configures a threshold on the size of images while converting point cloud data into images. Because the converted image is compressed and restored by the legacy video codec, the size of the image is one of the main components in influencing the compression efficiency. If the image size can be made smaller than the image size determined by the threshold, compression efficiency can be improved. Here, we studied how to improve the compression efficiency by selecting the best-fit image size generated during video-based point cloud compression. Experimental results show that the proposed method can reduce the encoding time by 6 percent without loss of coding performance compared to the test model 15.0 version of video-based point cloud encoder.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.190-192
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• 2004

In this paper, we propose a low bit-rate embedded video compression scheme with 3-D block partition coding in the wavelet domain. The proposed video compression scheme includes multi-level 3-dimensional dyadic wavelet decomposition, raster scanning within each subband, formation of block, 3-D partitioning of block, and adaptive arithmetic entropy coding. Although the proposed video compression scheme is quit simple, it produces bit-stream with good features, including SNR scalability from the embedded nature. Experimental results demonstrate that the proposed video compression scheme is quit competitive to other good wavelet-based video coders in the literature.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.31-43
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• 2022

Traditional video compression has developed so far based on hybrid compression methods through motion prediction, residual coding, and quantization. With the rapid development of technology through artificial neural networks in recent years, research on image compression and video compression based on artificial neural networks is also progressing rapidly, showing competitiveness compared to the performance of traditional video compression codecs. In this paper, a new method capable of improving the performance of such an artificial neural network-based video compression model is presented. Basically, we take the rate-distortion optimization method using the auto-encoder and entropy model adopted by the existing learned video compression model and shifts some components of the latent information that are difficult for entropy model to estimate when transmitting compressed latent representation to the decoder side from the encoder side, and finally compensates the distortion of lost information. In this way, the existing neural network based video compression framework, MFVC (Motion Free Video Compression) is improved and the BDBR (Bjøntegaard Delta-Rate) calculated based on H.264 is nearly twice the amount of bits (-27%) of MFVC (-14%). The proposed method has the advantage of being widely applicable to neural network based image or video compression technologies, not only to MFVC, but also to models using latent information and entropy model.

• ETRI Journal
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• v.37 no.4
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• pp.743-751
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• 2015

Cloud gaming services are heavily dependent on the efficiency of real-time video streaming technology owing to the limited bandwidths of wire or wireless networks through which consecutive frame images are delivered to gamers. Video compression algorithms typically take advantage of similarities among video frame images or in a single video frame image. This paper presents a method for computing and extracting both graphics information and an object's boundary from consecutive frame images of a game application. The method will allow video compression algorithms to determine the positions and sizes of similar image blocks, which in turn, will help achieve better video compression ratios. The proposed method can be easily implemented using function call interception, a programmable graphics pipeline, and off-screen rendering. It is implemented using the most widely used Direct3D API and applied to a well-known sample application to verify its feasibility and analyze its performance. The proposed method computes various kinds of graphics information with minimal overhead.

• Journal of Broadcast Engineering
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• v.10 no.3
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• pp.392-400
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• 2005

Highly integrated MPEG-4@SP video compression engine, VideoCore, is proposed for mobile application. The primary components of video compression require the high memory bandwidth since they access the external memory frequently. They include motion estimation, motion compensation, quantization, discrete cosine transform, variable length coding, and so on. The motion estimation processor adopted in VideoCore utilizes the small-size local memories such that the video compression system accesses external memory as less frequently as possible. The entire video compression system is divided into two distinct sub-systems: the integer-unit motion estimation part and the others, and both operate concurrently in a pipelined architecture. Thus the VideoCore enables the real-time high-quality video compression with a relatively low operation frequency.

• Journal of Broadcast Engineering
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• v.27 no.7
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• pp.985-998
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• 2022

Even after the standardization of VVC completed in 2020, continuing efforts for developing even better video coding technology are still under way. In this context, the Joint Video Experts Group (JVET) is developing various video compression technologies under the name of enhanced compression beyond VVC capability which has reported BDBR gain of -6.75%, -14.05%, and -15.25% on its test model, ECM version 5.0, respectively in Y, Cb, and Cr channels by just having a few new or improved intra coding tools. The current activity has so far adopted 11 intra coding tools beyond VVC which can generate more sophisticated predictors, reduce signaling overhead, or combine various predictors. In this tutorial paper, we will review these techniques.

• Journal of Broadcast Engineering
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• v.22 no.1
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• pp.136-139
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• 2017

Recently, many companies and consumers has shown a lot of interest toward VR(Virtual Reality), so many VR devices such as HMD(Head mounted Display) and 360 degree VR camera are released on the market. Current encoded 360 degree VR video uses the codec which originally made for the conventional 2D video. Therefore, the compression efficiency isn't optimized because the en/decoder does not consider the characteristics of the 360 degree VR video. In this paper, we propose a method to improve the compression efficiency by using the reference frame which compensates for the distortions caused by characteristics the 360 degree VR video. Applying the proposed method we were able to increase the compression efficiency by providing better prediction.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.497-499
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• 2020

포인트 클라우드는 다수의 3D 포인터를 사용한 3D 데이터의 표현 방식 중 하나이며, 멀티미디어 획득 및 처리 기술의 발전에 따라 다양한 분야에서 주목하고 있는 기술이다. 특히 포인트 클라우드는 3D 데이터를 정밀하게 수집하고 표현할 수 있는 장점을 가진다. 하지만 포인트 클라우드는 방대한 양의 데이터를 가지고 있어 효율적인 압축이 필수적이다. 이에 따라 국제 표준화 단체인 Moving Picture Experts Group에서는 포인트 클라우드 데이터의 효율적인 압축을 위하여 Video based Point Cloud Compression(V-PCC)와 Geometry based Point Cloud Coding에 대한 표준을 제정하고 있다. 이 중 V-PCC는 기존 High Efficiency Video Coding(HEVC) 표준을 활용하여 포인트 클라우드를 압축하여 활용성이 높다는 장점이 있다. 본 논문에서는 V-PCC에 사용하는 HEVC 코덱을 2020년 7월 표준화 완료될 예정인 Versatile Video Coding으로 대체하여 V-PCC의 압축 성능을 더 개선할 수 있음을 보인다.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.1
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• pp.28-36
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• 2020

Due to the continued demand for high-definition video, video compression technology is steadily developing and the High Efficiency Video Coding standard was established in 2013. However, despite the development of this compression technology, it is very difficult to smoothly transmit VGA-level videos in Ultra-narrowband environments. In this paper, the target information preprocessing algorithm is presented for smooth transmission of target images moving in forest or open-terrain in Ultra-narrowband environment. In addition, for algorithm verification, the target information preprocessing algorithm was simulated and the simulated results were compared with the video compression result without the algorithm being applied.