• Journal of Broadcast Engineering
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• v.13 no.3
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• pp.328-338
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• 2008

Multi-view video coding (MVC) based on H.264/AVC encodes multiple views efficiently by using a prediction scheme that exploits inter-view correlation among multiple views. However, with the increase of the number of views and use of inter-view prediction among views, total encoding time will be increased in multiview video coding. In this paper, we propose a fast mode decision using both MB(Macroblock)-based region segmentation information corresponding to each view in multiple views and global disparity vector among views in order to reduce encoding time. The proposed method achieves on average 40% reduction of total encoding time with the objective video quality degradation of about 0.04 dB peak signal-to-noise ratio (PSNR) by using joint multi-view video model (JMVM) 4.0 that is the reference software of the multiview video coding standard.

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.911-921
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• 2020

Recently, MPEG-I (Immersive) has been exploring compression performance through standardization projects for immersive video. The MPEG Immersion Video (MIV) standard technology is intended to provide limited 6DoF based on depth map-based image rendering (DIBR). MIV is a model that processes the Basic View and the residual information into an Additional View, which is a collection of patches. Atlases have the unique characteristics depending on the kind of the view they are included, requiring consideration of the compression efficiency. In this paper, the performance comparison analysis of screen content coding tools such as intra block copy (IBC) is conducted, based on the pattern of various views and patches repetition. It is demonstrated that the proposed method improves coding performance around -15.74% BD-rate reduction in the MIV.

• Journal of Information Display
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• v.12 no.2
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• pp.77-83
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• 2011

The memory compression algorithm CCC-LCP (color-count-controlled local color palette) reduces flicker in the liquid crystal display (LCD) overdrive. Its compression ratio is 1:5.4 for 10-bit images, with a 33 dB PSNR peak signal-to-noise ratio and with five times flicker reduction compared with the block truncation coding. The authors' two-alternative forced choice subjective tests proposed two new soundness properties, the 'CMP harmlessness' and 'OD non-lost (or OD liveliness)', to clarify the functional interaction between the overdrive functionality OD and the compression functionality CMP. The tests verified that CCC-LCP is practically applicable (at a 1.2H viewing distance threshold) for 42" 37-ppi WXGA TVs.

• Journal of Broadcast Engineering
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• v.17 no.4
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• pp.565-571
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• 2012

During depth data acquisition and transmission, the quality of depth maps is usually degraded by physical noise and coding error. In this paper, a new joint bilateral filter based on the common distance transform is presented to enhance the low-quality depth map. The proposed method determines the amount of exploitable color data according to distance transform values of depth and color pixels. Consequently, the proposed filter minimizes noise in the depth map while suppressing visual artifacts of joint bilateral filtering. Experimental results show that our method outperforms other conventional methods in terms of noise reduction and visual artifact suppression.

• ETRI Journal
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• v.41 no.4
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• pp.415-425
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• 2019

Generative adversarial networks (GAN) have been successfully used in many image restoration tasks, including image denoising, super-resolution, and compression artifact reduction. By fully exploiting its characteristics, state-of-the-art image restoration techniques can be used to generate images with photorealistic details. However, there are many applications that require faithful rather than visually appealing image reconstruction, such as medical imaging, surveillance, and video coding. We found that previous GAN-training methods that used a loss function in the form of a weighted sum of fidelity and adversarial loss fails to reduce fidelity loss. This results in non-negligible degradation of the objective image quality, including peak signal-to-noise ratio. Our approach is to alternate between fidelity and adversarial loss in a way that the minimization of adversarial loss does not deteriorate the fidelity. Experimental results on compression-artifact reduction and super-resolution tasks show that the proposed method can perform faithful and photorealistic image restoration.

• Journal of Convergence for Information Technology
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• v.10 no.6
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• pp.41-48
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• 2020

The sensor nodes in underwater IoT networks have practical limitations in power supply. Thus, the reduction of power consumption is one of the most important issues in underwater environments. In this regard, AMC(Adaptive Modulation and Coding) techniques are used by using the relation between SNR and BER. However, according to our hands-on experience, we observed that the relation between SNR and BER is not that tight in underwater environments. Therefore, we propose a deep learning based MLP classification model to reflect multiple underwater channel parameters at the same time. It correctly predicts BER with a high accuracy of 85.2%. The proposed model can choose the best parameters to have the highest throughput. Simulation results show that the throughput can be enhanced by 4.4 times higher than the conventionally measured results.

• Journal of Korea Multimedia Society
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• v.10 no.12
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• pp.1551-1558
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• 2007

The standard of evaluating function of speech coder for mobile telecommunication can be shown in channel capacity, noise immunity, encryption, complexity and encoding delay largely. This study is an algorithm to reduce complexity applying to CDMA(Code Division Multiple Access) mobile telecommunication system, which has a benefit of keeping the existing advantage of telecommunication quality and low transmission rate. This paper has an objective to reduce the computing complexity by controlling the frequency band nonuniform during the changing process of LSP(Line Spectrum Pairs) parameters from LPC(Line Predictive Coding) coefficients used for EVRC(Enhanced Variable-Rate Coder, IS-127) speech coders. Its experimental result showed that when comparing the speech coder applied by the proposed algorithm with the existing EVRC speech coder, it's decreased by 45% at average. Also, the values of LSP parameters, Synthetic speech signal and Spectrogram test result were obtained same as the existing method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.06a
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• pp.272-273
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• 2019

VVC(Versatile Video Coding)는 YUV 입력 영상에 대하여 Luma 성분과 Chroma 성분에 대하여 각각 다른 최적의 방법으로 블록분할 후 해당 블록에 대해서 화면 내 예측 또는 화면 간 예측을 수행하고, 예측영상과 원본영상의 차이를 변환, 양자화하여 압축한다. 이 과정에서 복원영상에는 블록화 노이즈, 링잉 노이즈, 블러링 노이즈 발생한다. 본 논문에서는 인코더에서 원본영상과 복원영상의 잔차신호에 대한 MAE(Mean Absolute Error)를 추가정보로 전송하여 이 추가정보와 복원영상을 이용하여 Deep Learning 기반의 신경망 네트워크로 영상의 품질을 높이는 방법을 제안한다. 복원영상의 노이즈를 감소시키기 위하여 영상을 $32{\times}32$블록의 임의로 분할하고, DenseNet기반의 UNet 구조로 네트워크를 구성하였다.

• Journal of Broadcast Engineering
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• v.24 no.3
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• pp.420-429
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• 2019

Recently, researches using Convolutional Neural Network (CNN)-based approaches have been actively conducted to improve the reduced quality of compressed video using block-based video coding standards such as H.265/HEVC. This paper aims to summarize and analyze the network models in these quality enhancement studies. At first the detailed components of CNN for quality enhancement are overviewed and then we summarize prior studies in the image domain. Next, related studies are summarized in three aspects of network structure, dataset, and training methods, and present representative models implementation and experimental results for performance comparison.

• The KIPS Transactions:PartC
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• v.15C no.3
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• pp.149-156
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• 2008

JPEG2000 is the upcoming image coding standard that provides better compression rate and image quality compared with JPEG. Lazy-mode steganography guarantees the safe communication under the two information loss stages in JPEG2000. However, it causes the severe changes of the code-block noise variance sequence after embedding and that is detectable under the steganalysis using the Hilbert-Huang transform (HHT) based sequential analysis. In this paper, a JPEG2000 lazy-mode steganography method is presented. The code blocks which produce the sudden variation of the noise variance after embedding are estimated by calculating low precision code-block variance (LPV) and low precision code-block noise variance (LPNV). By avoiding those code-blocks from embedding, our algorithm preserves the sequence and makes stego images secure under the HHT-based steganalytic detection. In addition, it prevents a severe degradation of image quality by using JPEG2000 quality layer information. On various 2048 images, experiments are performed to show the effective reduction of the noise variation after message embedding and the stable performance against HHT-based steganalysis.