Yong-woo Lee

Hierarchical random linear network coding for multicast scalable video streaming

Network coding (NC) has been exploited to minimize delay, energy per bit and to maximize throughput over lossy networks. However, applying network coding for scalable video transmission over packet-lossy networks is still an active research topic because of the challenge in trading off between rank deficiency problem and scalability of the video bitstream. Scalable video coding promises providing flexible broadcasting to heterogeneous user equipments, but the rank deficiency problem in NC limits the scalable capability in video streaming since the time of waiting for full rank causes long delay. In this paper, we exploit the combination of hierarchical and random linear network coding for solving the above issue. The hierarchical network coding provides unequal erasure protection for scalable layers and addresses the process of decoding layer by layer according to importance. Otherwise, random linear network coding provides error-correcting capability and makes the scheme resilient to packet loss. Our proposed network coding scheme benefits from these two schemes for multicast scalable video streaming. The simulation results and performance analysis are provided to evaluate the efficiency in terms of averaged decoding probability and peak signal-to-noise ratio. The proposed scheme achieves same performance of random linear network coding in good channel and approximately hierarchical network coding scheme in bad channel.

Efficient data uploading based on network coding in LTE-Advanced heterogeneous networks

LTE-Advanced heterogeneous networks enable a uniform broadband experience to users flexibly anywhere in the network by using a mix of large and small cells — i.e., macro, pico, femto and relay stations. In this paper, we propose a novel network coding-based for mobile content uploading, where multiple user equipments upload their own content toward the eNodeB in LTE-Advanced relay networks. Network coding has been considered as a promising solution in next generation networks because of the significant improvement in the transmission rate and reliability. The network coding enables an intermediate node having the capability of encoding incoming packets rather than simply forwarding. However, the advantages come at the cost of high computational, storage costs and coding vector overhead. The two former drawbacks can be solved easily by the fast development of current smart users and relay with high capability on computation and storage. The last issue of coding vector overhead still remains as many packets are encoded together using a linear combination since each packet needs to carry a large size of the header to store the information of the coding vector. We propose random overlapped chunked code for enhancing the transmission rate and reliability under the constraint of coding vector overhead. Furthermore, the encoding and decoding processes can be operated with low complexity. The complete transmission consists of two phases: users upload the content to the relay; the relay performs the proposed random overlapped chunked code of different coming streams from users and forwards the network-coded packets to the eNodeB. For performance evaluation, we run various simulations along with analysis to show that our proposal outperforms current schemes in terms of decoding probability.

Fully automatic segmentation based on localizing active contour method

Cartilage segmentation is one of challenging issues because knee magnetic resonance (MR) images are consisted of thin sheet structure, intensity inhomogeneity, and low contrast between cartilage and muscle. In this paper, a fully automatic segmentation method for knee cartilage is proposed using spatial fuzzy c-mean clustering (SFCM) and morphological operators. The proposed method modifies the way to generate an approximate boundary of cartilage region, and combines it with localizing region-based active contour method, and overcomes limitations of previous methods. The performance of the proposed method is improved more than 10.8% by Dice similarity coefficient (DSC) in comparison with previous methods.

A comparison of transmission schemes for scalable video over MIMO relay networks

This paper investigates a comparison of transmission schemes for the scalable video over multiple-input multiple-output relay networks. We consider three time-division multiple-access cooperative schemes by using embedded space time codes with various degrees of broadcasting and receiving collision. In this paper, we assume that base station and relay station are equipped two antennas and mobile stations have only one antenna. The relay station operates decode-and-forward mode for cooperative transmission. The base station broadcasts scalable video bit stream by encoding embedded space time codes with the assistance of relay. Upon the amount of received information of each cooperative scheme, Mobile stations get the corresponding efficient decoding. With the help of relayed information, base layer retrieves better protection for ensuring the basic quality of scalable video. Visual quality is refined by enhancement layers carried in transmitted signal. We investigate the diversity performance of various schemes in term of bit error rate. Scalable video performance is also provided for comparing and evaluating the effectiveness of scalable video transmission schemes. Based on the need of users, its possible to select the suitable scheme for trading off between the protection of base and enhancement layers.

UEP Network Coding for SVC Streaming

In this paper, we propose an optimized unequal error protection UEP network coding for scalable video coding SVC. First, we introduce packet-level UEP network coding schemes which encode packets with different selection probabilities according to the priorities of video layers. Secondly, we explain how to prioritize the packet selection probability using distortion degree. The distortion degree accounts for the layers as well as group of pictures GOP. Finally, experiment results demonstrate the performance of our proposed UEP network coding compared to other existing network coding methods in terms of decoding probability and peak signal to noise ratio PSNR.

An efficient resource allocation scheme for scalable video multicast in LTE-advanced networks

We address the challenge of optimizing the radio resource allocation for scalable video multicast over LTE-Advanced. The advantage of multicast service is to utilize the available bandwidth efficiently in delivering the same content to multiple receivers. However, the instantaneous channel condition of each receiver in the multicast group varies independently. To guarantee all receivers experiencing similar performance becomes more challenging. Hence, we need an advanced radio resource management to guarantee the least quality of service levels. In this paper, we propose a novel heuristic strategy which aims to jointly optimize the frequency selectivity, adaptive modulation and coding, and random linear network coding scheme performed at Medium Access Layer. The key aspect is that our proposal enhances the reliability of video services by exploiting random linear network coding in LTE-Advanced. Moreover, the available bandwidth is utilized efficiently by taking the advantage from the frequency selectivity in subgroup formation. We do various simulations for performance evaluation. The results show that our proposed resource allocation outperforms the existing studies regarding resource load, spectral efficiency, recovery probability and the attainable video quality (i.e., peak signal-to-noise ratio).

Robust scalable video multicast using triangular network coding in LTE/LTE-Advanced

The recent research studies showed that inter-layered network coding is a promising approach to provide the unequal error protection for scalable video multicast under the channel heterogeneity. The selection of the optimal transmission distribution performed at eNB increases the system performance with the cost of time and computational complexities. In this paper, we propose an optimal transmission strategy for the scalable video multicast using triangular network coding at the application layer in LTE/LTE-Advanced networks. The proposed transmission strategy comprises of two optimization phases: space reduction and performance maximization. The first optimization reduces the number of searching steps in the dictionary of possible transmission distributions by using a proposed performance predictive algorithm. The following optimization not only maximizes the average number of successfully decoded layers among receivers but also maximizes the number of receivers decoding the video base layer successfully in the second phase. We evaluate the proposed transmission strategy through various simulations with the performance metrics regarding the average number of successfully decoded layers among receivers in a multicast group, throughput, and video quality measurement. The simulation results show that our proposed scheme outperforms other recent studies and adapts well with the variable streaming rates of the video under the extreme time constraints.

A weighted trilateral filter for improving virtual view synthesis quality in MVD based 3D video system

A Depth map containing depth information of an image is used for acquiring virtual view synthesis in Multi-view Video plus Depth (MVD) based 3D video system. Since the depth map quality has huge impact on the virtual view synthesis, it is important to acquire the high quality depth map. However it is difficult to obtain high quality depth map directly from the camera. There have been lots of researches to obtain the high quality depth map using pre/post processing from the lower quality depth map. However, it is unsatisfactory to denoise the edge area which has huge impact on synthesis image quality. Also, they did not reflect properly human visual perception characteristic that is sensitive to moving object. Therefore, we tackle these issues by proposing a filtering method which takes into account the importance of the edge area in virtual view synthesis image and the human visual perception characteristic. Our proposed method applies different weights on the edge of moving objects which results in quality enhancement of the virtual view synthesis image. It is shown that the proposed method has less error and better visual quality compared to existing methods.

Knee Cartilage Defect Assessment using Cartilage Thickness Atlas

Abstract: Osteoarthritis is the most common chronic joint disease in the world. With its progression, cartilage thick-ness tends to diminish, which causes severe pain to human being. One way to examine the stage of osteoarthritisis to measure the cartilage thickness. When it comes to inter-subject study, however, it is not easy task to comparecartilage thickness since every human being has different cartilage structure. In this paper, we propose a methodto assess cartilage defect using MRI inter-subject thickness comparison. First, we used manual segmentation methodto build accurate atlas images and each segmented image was labeled as articular surface and bone-cartilage interfacein order to measure the thickness. Secondly, each point in the bone-cartilage interface was assigned the measuredthickness so that the thickness does not change after registration. We used affine transformation and SyGN to getdeformation fields which were then applied to thickness images to have cartilage thickness atlas. In this way, it ispossible to investigate pixel-by-pixel thickness comparison. Lastly, the atlas images were made according to theirosteoarthritis grade which indicates the degree of its progression. The result atlas images were compared using theanalysis of variance in order to verify the validity of our method. The result shows that a significant difference isexisted among them with p < 0.001.Key words: osteoarthritis, Kellgren & Lawrence grade, cartilage thickness, magnetic resonance imaging, inter-sub-ject study, analysis of variance

Cartilage-defect assessment by measuring thickness of knee MRI: Data from the osteoarthritis initiative

In this paper, we present a knee cartilage assessment technique to differentiate those in different osteoarthritis grades. We construct atlas for each grade and each atlas represents an average size of cartilage thickness. We build the atlas using manual segmentation of patellar cartilage following groupwise registration. We assign thickness value at the voxels in bone-cartilage interface in ahead. The thickness atlas is used for t-test to see how our method is accurate. T-test result shows our method finds significant difference between osteoarthritis grade 0 and 4 with p less than 0.001.