• Journal of Broadcast Engineering
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• v.21 no.6
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• pp.957-964
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• 2016

Acquisition of reliable depth maps is a critical requirement in many applications such as 3D videos and free-viewpoint TV. Depth information can be obtained from the object directly using physical sensors, such as infrared ray (IR) sensors. Recently, Time-of-Flight (ToF) range camera including KINECT depth camera became popular alternatives for dense depth sensing. Although ToF cameras can capture depth information for object in real time, but are noisy and subject to low resolutions. Recently, filter-based depth up-sampling algorithms such as joint bilateral upsampling (JBU) and noise-aware filter for depth up-sampling (NAFDU) have been proposed to get high quality depth information. However, these methods often lead to texture copying in the upsampled depth map. To overcome this limitation, we formulate a convex optimization problem using higher order regularization for depth map upsampling. We decrease the texture copying problem of the upsampled depth map by using edge weighting term that chosen by the edge information. Experimental results have shown that our scheme produced more reliable depth maps compared with previous methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.2
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• pp.268-274
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• 2016

To find the location of a random noise source present in the three-dimensional space is required at least four microphones. Using four microphones distributed in a three-dimensional space, noise source imaging technique was applied and evaluated on their performance. To compensate resolution problem which comes from both the position of the sensor array is fixed and the sampling frequency is low, up-sampling technique and interpolation function were applied. Five different interpolation methods were applied such as zero-padding, zero-order hold, first-order hold, spline function, and random signal padding. The up-sampling rate were chosen by two, four, eight times, and counting up 16 times. As a result, it was possible to more accurately estimate the position of the noise source according to the higher of the up-sampling rate. It also found that the first-order hold and the spline function's performance were slightly falling relative to other methods.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2014.11a
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• pp.236-239
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• 2014

3차원 영상을 제작하기 위해서는 여러 시점의 색상 영상과 함께 깊이 정보를 필요로 한다. 하지만 깊이 정보를 얻을 때 사용하는 ToF 카메라는 해상도가 낮으며 적외선 신호의 주파수 문제 때문에 최대 3대까지 사용할 수 있다. 따라서 깊이 정보를 색상 영상과 함께 사용하기 위해서 깊이 정보의 업샘플링이 필수적이다. 업샘플링은 깊이 정보를 색상 카메라 위치로 3차원 워핑하고 결합형 양방향 필터(joint bilateral filter, JBF)를 사용하여 빈 영역을 채우는 방법으로 진행된다. 업샘플링은 오랜 시간이 소요되지만 그래픽스 프로세싱 유닛(graphics processing units, GPU)를 이용하여 빠르게 수행될 수 있다. 본 논문에서는 다중 GPU의 병렬 수행을 통하여 빠르게 다시점 깊이맵을 생성할 수 있는 방법을 제안한다. 다중 GPU 병렬 수행은 범용 목적 GPU(general purpose computing on GPU, GPGPU) 중의 하나인 CUDA를 이용하였으며, 본 논문에서 제안된 방법을 이용하여 3개의 GPU 사용한 실험 결과 초당 35 프레임의 다시점 깊이맵을 생성했다.

• Journal of KIISE:Information Networking
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• v.35 no.6
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• pp.549-556
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• 2008

To achieve flexible visual content adaption for multimedia communications, the ISO/IEC MPEG & ITU-T VCEG form the JVT to develop SVC amendment for the H.264/AVC standard. JVT uses inter-layer prediction as well as inter prediction and intra prediction that are provided in H.264/AVC to remove the redundancy among layers. The main goal consists of designing inter-layer prediction tools that enable the usage of as much as possible base layer information to improve the rate-distortion efficiency of the enhancement layer. But inter layer prediction causes the computational complexity to be increased. In this paper, we proposed an efficient residual prediction. In order to reduce the computational complexity while maintaining the high coding efficiency. The proposed residual prediction uses modified interpolation that is defined in H.264/AVC SVC.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.11
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• pp.918-926
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• 2012

In 3D video transmission, a depth map being used for depth image based rendering (DIBR) is generally compressed by reducing resolution for coding efficiency. Errors in resolution reduction are recovered by an appropriate up-sampling method after decoding. However, most previous works only focus on up-sampling techniques to reduce errors. In this paper, we propose a novel down-sampling technique of depth map that applies different down-sampling rates on moving objects and background in order to enhance human perceptual quality. Experimental results demonstrate that the proposed scheme provides both higher visual quality and peak signal-to-noise ratio (PSNR). Also, our method is compatible with other up-sampling techniques.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.6
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• pp.1175-1184
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• 2015

A depth map is an image which contains 3D distance information. Generally, it is difficult to acquire a high resolution (HD), noise-removed, good quality depth map directly from the camera. Therefore, many researches have been focused on acquisition of the high resolution and the good quality depth map by up-sampling and pre/post image processing of the low resolution depth map. However, many researches are lack of effective up-sampling for the edge region which has huge impact on image perceptual-quality. In this paper, we propose an up-sampling method, based on joint bilateral filter, which improves up-sampling of the edge region and visual quality of synthetic images by adopting different weights for the edge parts that is sensitive to human perception characteristics. The proposed method has gains in terms of PSNR and subjective video quality compared to previous researches.

• Journal of Broadcast Engineering
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• v.17 no.6
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• pp.933-944
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• 2012

In this paper, we propose a new method to convert a low-resolution depth map into its high-resolution one called distance transform-based bilateral upsampling. Since the proposed method controls the spatial domain weighting function based on distance transform values of the depth map, it increases the input depth map resolution while preserving edge sharpness. The proposed method is composed of three main steps: distance transform, spatial weighting control, and image interpolation. Experimental results show that our method outperforms the conventional bilateral upsampling in terms of the quality of output depth maps.

• Journal of Broadcast Engineering
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• v.19 no.1
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• pp.31-43
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• 2014

A depth map is an important component for stereoscopic image generation. Since the depth map acquired from a depth camera has a low resolution, upsamling a low-resolution depth map to a high-resolution one has been studied past decades. Upsampling methods are evaluated by objective evaluation tools such as PSNR, Sharpness Degree, Blur Metric. As well, the subjective quality is compared using virtual views generated by DIBR (depth image based rendering). However, works on the analysis of the relation between depth map upsampling and stereoscopic images are relatively few. In this paper, we investigate the relationship between subjective evaluation of stereoscopic images and objective performance of upsampling methods using cross correlation and linear regression. Experimental results demonstrate that the correlation of edge PSNR and visual fatigue is the highest and the blur metric has lowest correlation. Further, from the linear regression, we found relative weights of objective measurements. Further we introduce a formulae that can estimate 3D performance of conventional or new upsampling methods.

• Journal of Broadcast Engineering
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• v.25 no.2
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• pp.242-251
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• 2020

Recently, many deep convolutional neural networks for image super-resolution have been studied. Existing deep learning-based super-resolution algorithms are architecture that up-samples the resolution at the end of the network. The post-upsampling architecture has an inefficient structure at large scaling factor result of predicting a lot of information for mapping from low-resolution to high-resolution at once. In this paper, we propose a single image super-resolution using Channel Attention Residual Dense Block based on an iterative up-down sampling architecture. The proposed algorithm efficiently predicts the mapping relationship between low-resolution and high-resolution, and shows up to 0.14dB performance improvement and enhanced subjective image quality compared to the existing algorithm at large scaling factor result.

• Broadcasting and Media Magazine
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• v.18 no.4
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• pp.54-62
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• 2013