• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.870-878
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• 2019

In this paper, we propose an Attention-based BLSTM for predicting the video compression standard of a video. Recently, in NLP, many researches have been studied to predict the next word of sentences, classify and translate sentences by their semantics using the structure of RNN, and they were commercialized as chatbots, AI speakers and translator applications, etc. LSTM is designed to solve the gradient vanishing problem in RNN, and is used in NLP. The proposed algorithm makes video compression standard prediction possible by applying BLSTM and Attention algorithm which focuses on the most important word in a sentence to a bitstream of a video, not an sentence of a natural language.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.1_2
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• pp.137-146
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• 2006

Traditional live video streaming systems support the limited field of view (FOV) of image to the remote users. A server system based on the pan/tilt camera provides a user with wide view by changing the view direction of the camera mechanically. But, when many clients try to access to the server, this system can not offer their own view to every user simultaneously, and moreover it has the delay by camera motion. In order to offer wide views to several users, we propose new streaming system using the panorama image that has wide view. Our system is a kind of implementation of software pan/tilt camera. The server acquires panorama video and sends a part of the video to clients. Then, each client can control their own view. We need the effective way to reduce the average transmission data size and server burden to the compression because generally the full size of panorama video is too big to be served by the real-time streaming. To solve this problem, we propose an strip-based video compression and adaptive transmission of the compressed multiple strip videos. Experimental results show that our system can be adapted quickly to the change of view and the number of clients. Furthermore, proposed method effectively reduce the transmission data.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.4
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• pp.58-66
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• 2009

Recently the emerging demands of the light-video encoder promotes lots of research efforts on DVC (Distributed Video Coding). As an appropriate video compression method, DVC has been studied, and Wyner-Ziv (WZ) video compression is its one representative structure. The WZ encoder splits the image into two kinds of frames, one is key frame which is compressed by conventional intra coding, and the other is WZ frame which is encoded by WZ coding. The WZ decoder decodes the key frame first, and estimates the WZ frame using temporal correlation between key frames. Estimated WZ frame (Side Information) cannot be the same as the original WZ frame due to the absence of the WZ frame information at decoder. As a result, the difference between the estimated and original WZ frames are regarded as virtual channel noise. The WZ frame is reconstructed by removing noise in side information. Therefore precise noise estimation produces good performance gain in WZ video compression by improving error correcting capability by channel code. But noise cannot be estimated precisely at WZ decoder unless there is good WZ frame information, and generally it is estimated from the difference of corresponding key frames. Also the estimated noise is limited by comparing with frame level noise to reduce the uncertainty of the estimation method. However these methods cannot provide good noise estimation for every frame or each bit plane. In this paper, we propose a noise nodel selection method which chooses a better noise model for each bit plane after generating candidate noise models. Experimental result shows PSNR gain up to 0.8 dB.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.10
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• pp.1914-1919
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• 2006

Video transfer problem on mobile is transfer speed and controls. Compression technique is needed to transfer videos and H.263 codec is used for compression, effectively controls camera on remote places, increased the real time connecting users. In this paper, we could solve the problem that use existent RF, and could transfer the most suitable image and audio.

• Journal of information and communication convergence engineering
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• v.12 no.1
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• pp.53-59
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• 2014

Motion estimation is a core part of most video compression systems since it directly affects the output video quality and the encoding time. The full search (FS) technique gives the highest visual quality but has the problem of a significant computational load. To solve this problem, we present in this paper a modified median (MMED) operation and advanced search strategies for fast motion estimation. The proposed MMED operation includes a temporally co-located motion vector (MV) to select an appropriate initial candidate. Moreover, we introduce a search procedure that reduces the number of thresholds and simplifies the early termination conditions for the determination of a final MV. The experimental results show that the proposed approach achieves substantial speedup compared with the conventional methods including the motion vector field adaptive search technique (MVFAST) and predictive MVFAST (PMVFAST). The proposed algorithm also improves the PSNR values by increasing the correlation between the MVs, compared with the FS method.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.5
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• pp.605-614
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• 2016

We develop a novel SAD circuit for power-efficient H.264 encoding, namely a-SAD. Here, some highest-order MSB's are approximated to single MSB. Our theoretical estimations show that our proposed design simultaneously improves performance and power of SAD circuit, achieving good power efficiency. We decide that the optimal number of approximated MSB's is four under 8-bit YUV-420 format, the largest number not to affect video quality and compression-rate in our video experiments. In logic simulations, our a-SAD circuit shows at least 9.3% smaller critical-path delay compared to existing SAD circuits. We compare power dissipation under iso-throughput scenario, where our a-SAD circuit obtains at least 11.6% power saving compared to other designs. We perform same simulations under two- and three-stage pipelined architecture. Here, our a-SAD circuit delivers significant performance (by 13%) and power (by 17% and 15.8% for two and three stages respectively) improvements.

• Journal of Electrical Engineering and Technology
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• v.11 no.4
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• pp.1020-1026
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• 2016

Motion estimation (ME) algorithms supporting quarter-pixel accuracy have been recently introduced to retain detailed motion information for high quality of video in the state-of-the-art video compression standard of H.264/AVC. Conventional sub-pixel ME algorithms in the spatial domain are faced with a common problem of computational complexity because of embedded interpolation schemes. This paper proposes a low-complexity sub-pixel motion estimation algorithm in the transform domain utilizing shifting matrix. Simulations are performed to compare the performances of spatial-domain ME algorithms and transform-domain ME algorithms in terms of peak signal-to-noise ratio (PSNR) and the number of bits per frame. Simulation results confirm that the transform-domain approach not only improves the video quality and the compression efficiency, but also remarkably alleviates the computational complexity, compared to the spatial-domain approach.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.7
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• pp.495-502
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• 2013

Motion estimation (ME) that limits the performance of image quality and encoding speed has been developed to reduce temporal redundancy in video sequences and plays an important role in digital video compression. But it is computational demanding part of the encoder. Multi-view video is obtained by capturing one three-dimensional scene with many cameras at different positions. ME for Multi-view video requires high computational complexity. To reduce computational complexity and maintain the image quality, a fast motion estimation method is proposed in this paper. The proposed method uses a hierarchical search strategy. This strategy method consists of modified diamond search patten, multi gird diamond search pattern, and raster search pattern. These search patterns place search points symmetrically and evenly that can cover the overall search area not to fall into the local minimum or exploits the characteristics of the distribution of motion vectors to place the search points. Experiment results show that the speedup improvement of the proposed method over TZ search method (JMVC) can be up to 1.2 ~3 times faster while maintaining similar video quality and bit rates.

• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.743-754
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• 2019

System for three degrees of freedom plus (3DoF+) and 6DoF requires multi-view high resolution 360 video transmission to provide user viewport adaptive 360 video streaming. In this paper, we implement 3DoF+ 360 video compression system which removes the redundancy between multi-view videos and merges the residual into one video to provide high quality 360 video corresponding to an user's head movement efficiently. Implementations about 3D warping based redundancy removal method between 3DoF+ 360 videos and residual extraction and merger are explained in this paper. With the proposed system, 20.14% of BD-rate reduction in maximum is shown compared to traditional high-efficiency video coding (HEVC) based system.

• Journal of Broadcast Engineering
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• v.26 no.6
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• pp.738-747
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• 2021

Today, with the improvement of deep learning technology, computer vision areas such as image classification, object detection, object segmentation, and object tracking have shown remarkable improvements. Various applications such as intelligent surveillance, robots, Internet of Things, and autonomous vehicles in combination with deep learning technology are being applied to actual industries. Accordingly, the requirement of an efficient compression method for video data is necessary for machine consumption as well as for human consumption. In this paper, we propose an object-based compression of thermal infrared images for machine vision. The input image is divided into object and background parts based on the object detection results to achieve efficient image compression and high neural network performance. The separated images are encoded in different compression ratios. The experimental result shows that the proposed method has superior compression efficiency with a maximum BD-rate value of -19.83% to the whole image compression done with VVC.