5 research outputs found
Quality modeling for the Medium Grain Scalability option of H.264/SVC
Rate vs. quality is a crucial trade off not only for efficient video coding and transmission but also for adaptive transmission strategies in wireless networks and/or congestion-prone networks. Scalable coders are well suited to tackle the time-varying capacities of these environments. In this paper we propose a semi-analytical model suitable for the medium grain scalable option of the H.264 standard and discuss the parameters influencing its performance. Results show it can effectively be used to represent the expected rate for different quality layers and thus its applicability to algorithms for resource optimization
Analysis for Scalable Coding of Quality-Adjustable Sensor Data
학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2014. 2. 신현식.Machine-generated data such as sensor data now comprise major portion of available information. This thesis addresses two important problems: storing of massive sensor data collection and efficient sensing. We first propose a quality-adjustable sensor data archiving, which compresses entire collection of sensor data efficiently without compromising key features.
Considering the data aging aspect of sensor data, we make our archiving scheme capable of controlling data fidelity to exploit less frequent data access of user. This flexibility on quality adjustability leads to more efficient usage of storage space. In order to store data from various sensor types in cost-effective way, we study the optimal storage configuration strategy using analytical models that capture characteristics of our scheme. This strategy helps storing sensor data blocks with the optimal configurations that maximizes data fidelity of various sensor data under given storage space.
Next, we consider efficient sensing schemes and propose a quality-adjustable sensing scheme. We adopt compressive sensing (CS) that is well suited for resource-limited sensors because of its low computational complexity. We enhance quality adjustability intrinsic to CS with quantization and especially temporal downsampling. Our sensing architecture provides more rate-distortion operating points than previous schemes, which enables sensors to adapt data quality in more efficient way considering overall performance. Moreover, the proposed temporal downsampling improves coding efficiency that is a drawback of CS. At the same time, the downsampling further reduces computational complexity of sensing devices, along with sparse random matrix. As a result, our quality-adjustable sensing can deliver gains to a wide variety of resource-constrained sensing techniques.Abstract i
Contents iii
List of Figures vi
List of Tables x
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Spatio-Temporal Correlation in Sensor Data 3
1.3 Quality Adjustability of Sensor Data 7
1.4 Research Contributions 9
1.5 Thesis Organization 11
Chapter 2 Archiving of Sensor Data 12
2.1 Encoding Sensor Data Collection 12
2.1.1 Archiving Architecture 13
2.1.2 Data Conversion 16
2.2 Compression Ratio Comparison 20
2.3 Quality-Adjustable Archiving Model 25
2.3.1 Data Fidelity Model: Rate 25
2.3.2 Data Fidelity Model: Distortion 28
2.4 QP-Rate-Distortion Model 36
2.5 Optimal Rate Allocation 40
2.5.1 Rate Allocation Strategy 40
2.5.2 Optimal Storage Configuration 41
2.5.3 Experimental Results 44
Chapter 3 Scalable Management of Storage 46
3.1 Scalable Quality Management 46
3.1.1 Archiving Architecture 47
3.1.2 Compression Ratio Comparison 49
3.2 Enhancing Quality Adjustability 51
3.2.1 Data Fidelity Model: Rate 52
3.2.2 Data Fidelity Model: Distortion 55
3.3 Optimal Rate Allocation 59
3.3.1 Rate Allocation Strategy 60
3.3.2 Optimal Storage Configuration 63
3.3.3 Experimental Results 71
Chapter 4 Quality-Adjustable Sensing 73
4.1 Compressive Sensing 73
4.1.1 Compressive Sensing Problem 74
4.1.2 General Signal Recovery 76
4.1.3 Noisy Signal Recovery 76
4.2 Quality Adjustability in Sensing Environment 77
4.2.1 Quantization and Temporal Downsampling 79
4.2.2 Optimization with Error Model 85
4.3 Low-Complexity Sensing 88
4.3.1 Sparse Random Matrix 89
4.3.2 Resource Savings 92
Chapter 5 Conclusions 96
5.1 Summary 96
5.2 Future Research Directions 98
Bibliography 100
Abstract in Korean 109Docto
Cross-layer Optimization for Video Delivery over Wireless Networks
As video streaming is becoming the most popular application of Internet mo-
bile, the design and the optimization of video communications over wireless
networks is attracting increasingly attention from both academia and indus-
try. The main challenges are to enhance the quality of service support, and to
dynamically adapt the transmitted video streams to the network condition.
The cross-layer methods, i.e., the exchange of information among different
layers of the system, is one of the key concepts to be exploited to achieve this
goals. In this thesis we propose novel cross-layer optimization frameworks
for scalable video coding (SVC) delivery and for HTTP adaptive streaming
(HAS) application over the downlink and the uplink of Long Term Evolution
(LTE) wireless networks. They jointly address optimized content-aware rate
adaptation and radio resource allocation (RRA) with the aim of maximiz-
ing the sum of the achievable rates while minimizing the quality difference
among multiple videos. For multi-user SVC delivery over downlink wireless
systems, where IP/TV is the most representative application, we decompose
the optimization problem and we propose the novel iterative local approxi-
mation algorithm to derive the optimal solution, by also presenting optimal
algorithms to solve the resulting two sub-problems. For multiple SVC de-
livery over uplink wireless systems, where healt-care services are the most
attractive and challenging application, we propose joint video adaptation
and aggregation directly performed at the application layer of the transmit-
ting equipment, which exploits the guaranteed bit-rate (GBR) provided by
the low-complexity sub-optimal RRA solutions proposed. Finally, we pro-
pose a quality-fair adaptive streaming solution to deliver fair video quality
to HAS clients in a LTE cell by adaptively selecting the prescribed (GBR)
of each user according to the video content in addition to the channel condi-
tion. Extensive numerical evaluations show the significant enhancements of
the proposed strategies with respect to other state-of-the-art frameworks