• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2015.11a
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• pp.149-151
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• 2015

본 논문에서는 4K UHD 입력 영상에 대해 각 프레임을 다중 슬라이스로 분할하고, 이를 병렬 처리를 통해 실시간으로 부호화를 수행하는 소프트웨어 기반 HEVC 인코더의 율 제어 방법에 관한 연구이다. 이를 위하여 HEVC 가 지원하는 슬라이스를 적용하여 프레임을 독립적인 다중 슬라이스로 분할하였고, 각각의 슬라이스에 대해서는 기존에 제안된 몇 가지의 율 제어 방법을 개선하여 적용하였다. 본 논문에서는 다중 슬라이스에 각각 독립적으로 적용한 율 제어 방법에 대한 실험 결과를 통해 제안된 율 제어 방법의 안정성을 확인하고, 향후 연구로서 각 슬라이스의 출력 비트율을 예측하고 다중 슬라이스에 효과적으로 할당하는 방법과 이를 통한 주관적 화질을 향상 하는 방법에 대해 논한다.

• ETRI Journal
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• v.33 no.5
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• pp.752-758
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• 2011

This paper presents a clock and data recovery circuit that supports dual data rates of 5.4 Gbps and 3.24 Gbps for DisplayPort v1.2 sink device. A quarter-rate linear phase detector (PD) is used in order to mitigate high speed circuit design effort. The proposed linear PD results in better jitter performance by increasing up and down pulse widths of the PD and removes dead-zone problem of charge pump circuit. A voltage-controlled oscillator is designed with a 'Mode' switching control for frequency selection. The measured RMS jitter of recovered clock signal is 2.92 ps, and the peak-to-peak jitter is 24.89 ps under $2^{31}-1$ bit-long pseudo-random bit sequence at the bitrate of 5.4 Gbps. The chip area is 1.0 mm${\times}$1.3 mm, and the power consumption is 117 mW from a 1.8 V supply using 0.18 ${\mu}m$ CMOS process.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.2
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• pp.218-226
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• 2001

The MPEG-2 Test Model 5 (TM5) algorithm is widely used for bit rate control. In TM5, however, the target number of bits and the number of actual coding bits for each picture do not match well. Therefore, buffer overflow and picture quality degradation may occur at the end of the GOP. In this paper, we propose a new bit rate control algorithm for matching the target and the actual coding bits based on accurate bit allocation. The key idea of the proposed algorithm is to determine quantization Parameters which enable us to generate the number of actual coding bits close to the target number of bits for each picture, while maintaining uniform picture quality and supporting real-time processing. The proposed algorithm exploits the relationship between the number of actual coding bits and the number of estimated bits of the previous macroblock.

• Journal of Korea Multimedia Society
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• v.12 no.7
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• pp.913-921
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• 2009

Quantization is an essential method for compression of video. The quantizer can adjust the bitrate and control the picture quality. Especially, the subjective picture quality can be improved if the concerned region within a video sequence has good picture quality. In this paper, firstly a classification method according to the subjective concerned region within the video sequence is suggested. Also we propose a method that assigns the quantization step-size differentially according to the concerned region within the video. Totally subjective picture quality can be increased by appling the quantization step-size as small value relatively for the concerned region compared with the other regions. We can find the result that the proposed method gives the improved picture quality by assigning differently quantization step-size and the best improvement can be brought when the difference between maximum and minimum values of the quantization step-size in a picture is from 4 to 8.

• Journal of KIISE
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• v.45 no.2
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• pp.175-186
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• 2018

Recently, HAS (HTTP Adaptive Streaming) has been the subject of much attention to improve the QoE (Quality of Experience). In a high latency network, HAS degrades the QoE due to the lost RTT cycle since it replies with a response of one segment to the request of one segment. The server-push based HAS schemes of downloading multiple segments in one request cause QoE degradation due to the buffer underflow. In this paper, we propose a VSSDS (Video Streaming Scheme based on Dynamic Server-push) scheme to improve the QoE in a high latency network. The proposed scheme adjust video quality by estimating available bandwidth and determine the number of segments to be downloaded for each segment request cycle. Through the simulation, the proposed scheme not only improves the average video bitrate but also alleviates the buffer underflow.

• Journal of Broadcast Engineering
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• v.16 no.1
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• pp.133-143
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• 2011

In this paper, a complexity-balancing algorithm is proposed for distributed video coding based on entropy coding. In order to reduce complexity of DVC-based decoders, the proposed method employs an entropy coder instead of channel coders and the complexity-balancing method is designed to improve RD performance with minimal computational complexity. The proposed method performs motion estimation in the decoder side and transmits the estimated motion vectors to the encoder. The proposed encoder can perform more accurate refinement using the transmitted motion vectors from the decoder. During the motion refinement, the optimal predicted motion vectors are decided by the received motion vector and the predicted motion vectors and complexity load of block is allocated by adjusting the search range based on the difference between the received motion vector and the predicted motion vectors. The computational complexity of the proposed encoder is decreased 11.9% compared to the H.264/AVC encoder and that of the proposed decoder are reduced 99% compared to the conventional DVC decoder.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.729-740
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• 2022

Netflix, Amazon Prime, and YouTube are the most popular and fastest-growing streaming services globally. It is a matter of great interest for the streaming service providers to preview their service infrastructure and streaming strategy in order to provide new streaming services. Hence, the first part of the paper presents a detailed survey of the Content Distribution Network (CDN) and cloud infrastructure of these service providers. To understand the streaming strategy of these service providers, the second part of the paper deduces a common quality-of-service (QoS) model based on rebuffering time, bitrate, progressive download ratio, and standard deviation of the On-Off cycle. This model is then used to analyze and compare the streaming behaviors of these services. This study concluded that the streaming behaviors of all these services are similar as they all use Dynamic Adaptive Streaming over HTTP (DASH) on top of TCP. However, the amount of data that they download in the buffering state and steady-state vary, resulting in different progressive download ratios, rebuffering levels, and bitrates. The characteristics of their On-Off cycle are also different resulting in different QoS. Hence a thorough adaptive bit rate (ABR) analysis is presented in this paper. The streaming behaviors of these services are tested on different access network bandwidths, ranging from 75 kbps to 30 Mbps. The survey results indicate that Netflix QoS and streaming behavior are significantly consistent followed by Amazon Prime and YouTube. Our approach can be used to compare and contrast the streaming services' strategies and finetune their ABR and flow control mechanisms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.4
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• pp.825-840
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• 2015

Adaptive bitrate (ABR) streaming technology has become an important and prevalent feature in many multimedia delivery systems, with content providers such as Netflix and Amazon using ABR streaming to increase bandwidth efficiency and provide the maximum user experience when channel conditions are not ideal. Where such systems could see improvement is in the delivery of live video with a closed loop cognitive control of video encoding. In this paper, we present streaming camera system which provides spatially and temporally adaptive video streams, learning the user's preferences in order to make intelligent scaling decisions. The system employs a hardware based H.264/AVC encoder for video compression. The encoding parameters can be configured by the user or by the cognitive system on behalf of the user when the bandwidth changes. A cognitive video client developed in this study learns the user's preferences(i.e. video size over frame rate) over time and intelligently adapts encoding parameters when the channel conditions change. It has been demonstrated that the cognitive decision system developed has the ability to control video bandwidth by altering the spatial and temporal resolution, as well as the ability to make scaling decisions.