Algorithms & implementation of advanced video coding standards

Advanced video coding standards have become widely deployed coding techniques used in numerous products, such as broadcast, video conference, mobile television and blu-ray disc, etc. New compression techniques are gradually included in video coding standards so that a 50% compression rate reduction is achievable every five years. However, the trend also has brought many problems, such as, dramatically increased computational complexity, co-existing multiple standards and gradually increased development time. To solve the above problems, this thesis intends to investigate efficient algorithms for the latest video coding standard, H.264/AVC. Two aspects of H.264/AVC standard are inspected in this thesis: (1) Speeding up intra4x4 prediction with parallel architecture. (2) Applying an efficient rate control algorithm based on deviation measure to intra frame. Another aim of this thesis is to work on low-complexity algorithms for MPEG-2 to H.264/AVC transcoder. Three main mapping algorithms and a computational complexity reduction algorithm are focused by this thesis: motion vector mapping, block mapping, field-frame mapping and efficient modes ranking algorithms. Finally, a new video coding framework methodology to reduce development time is examined. This thesis explores the implementation of MPEG-4 simple profile with the RVC framework. A key technique of automatically generating variable length decoder table is solved in this thesis. Moreover, another important video coding standard, DV/DVCPRO, is further modeled by RVC framework. Consequently, besides the available MPEG-4 simple profile and China audio/video standard, a new member is therefore added into the RVC framework family. A part of the research work presented in this thesis is targeted algorithms and implementation of video coding standards. In the wide topic, three main problems are investigated. The results show that the methodologies presented in this thesis are efficient and encourage

Error resilience and concealment techniques for high-efficiency video coding

This thesis investigates the problem of robust coding and error concealment in High Efficiency Video Coding (HEVC). After a review of the current state of the art, a simulation study about error robustness, revealed that the HEVC has weak protection against network losses with significant impact on video quality degradation. Based on this evidence, the first contribution of this work is a new method to reduce the temporal dependencies between motion vectors, by improving the decoded video quality without compromising the compression efficiency. The second contribution of this thesis is a two-stage approach for reducing the mismatch of temporal predictions in case of video streams received with errors or lost data. At the encoding stage, the reference pictures are dynamically distributed based on a constrained Lagrangian rate-distortion optimization to reduce the number of predictions from a single reference. At the streaming stage, a prioritization algorithm, based on spatial dependencies, selects a reduced set of motion vectors to be transmitted, as side information, to reduce mismatched motion predictions at the decoder. The problem of error concealment-aware video coding is also investigated to enhance the overall error robustness. A new approach based on scalable coding and optimally error concealment selection is proposed, where the optimal error concealment modes are found by simulating transmission losses, followed by a saliency-weighted optimisation. Moreover, recovery residual information is encoded using a rate-controlled enhancement layer. Both are transmitted to the decoder to be used in case of data loss. Finally, an adaptive error resilience scheme is proposed to dynamically predict the video stream that achieves the highest decoded quality for a particular loss case. A neural network selects among the various video streams, encoded with different levels of compression efficiency and error protection, based on information from the video signal, the coded stream and the transmission network. Overall, the new robust video coding methods investigated in this thesis yield consistent quality gains in comparison with other existing methods and also the ones implemented in the HEVC reference software. Furthermore, the trade-off between coding efficiency and error robustness is also better in the proposed methods

Efficient algorithms for scalable video coding

A scalable video bitstream specifically designed for the needs of various client terminals, network conditions, and user demands is much desired in current and future video transmission and storage systems. The scalable extension of the H.264/AVC standard (SVC) has been developed to satisfy the new challenges posed by heterogeneous environments, as it permits a single video stream to be decoded fully or partially with variable quality, resolution, and frame rate in order to adapt to a specific application. This thesis presents novel improved algorithms for SVC, including: 1) a fast inter-frame and inter-layer coding mode selection algorithm based on motion activity; 2) a hierarchical fast mode selection algorithm; 3) a two-part Rate Distortion (RD) model targeting the properties of different prediction modes for the SVC rate control scheme; and 4) an optimised Mean Absolute Difference (MAD) prediction model. The proposed fast inter-frame and inter-layer mode selection algorithm is based on the empirical observation that a macroblock (MB) with slow movement is more likely to be best matched by one in the same resolution layer. However, for a macroblock with fast movement, motion estimation between layers is required. Simulation results show that the algorithm can reduce the encoding time by up to 40%, with negligible degradation in RD performance. The proposed hierarchical fast mode selection scheme comprises four levels and makes full use of inter-layer, temporal and spatial correlation aswell as the texture information of each macroblock. Overall, the new technique demonstrates the same coding performance in terms of picture quality and compression ratio as that of the SVC standard, yet produces a saving in encoding time of up to 84%. Compared with state-of-the-art SVC fast mode selection algorithms, the proposed algorithm achieves a superior computational time reduction under very similar RD performance conditions. The existing SVC rate distortion model cannot accurately represent the RD properties of the prediction modes, because it is influenced by the use of inter-layer prediction. A separate RD model for inter-layer prediction coding in the enhancement layer(s) is therefore introduced. Overall, the proposed algorithms improve the average PSNR by up to 0.34dB or produce an average saving in bit rate of up to 7.78%. Furthermore, the control accuracy is maintained to within 0.07% on average. As aMADprediction error always exists and cannot be avoided, an optimisedMADprediction model for the spatial enhancement layers is proposed that considers the MAD from previous temporal frames and previous spatial frames together, to achieve a more accurateMADprediction. Simulation results indicate that the proposedMADprediction model reduces the MAD prediction error by up to 79% compared with the JVT-W043 implementation

Variable Block Size Motion Compensation In The Redundant Wavelet Domain

Video is one of the most powerful forms of multimedia because of the extensive information it delivers. Video sequences are highly correlated both temporally and spatially, a fact which makes the compression of video possible. Modern video systems employ motion estimation and motion compensation (ME/MC) to de-correlate a video sequence temporally. ME/MC forms a prediction of the current frame using the frames which have been already encoded. Consequently, one needs to transmit the corresponding residual image instead of the original frame, as well as a set of motion vectors which describe the scene motion as observed at the encoder. The redundant wavelet transform (RDWT) provides several advantages over the conventional wavelet transform (DWT). The RDWT overcomes the shift invariant problem in DWT. Moreover, RDWT retains all the phase information of wavelet coefficients and provides multiple prediction possibilities for ME/MC in wavelet domain. The general idea of variable size block motion compensation (VSBMC) technique is to partition a frame in such a way that regions with uniform translational motions are divided into larger blocks while those containing complicated motions into smaller blocks, leading to an adaptive distribution of motion vectors (MV) across the frame. The research proposed new adaptive partitioning schemes and decision criteria in RDWT that utilize more effectively the motion content of a frame in terms of various block sizes. The research also proposed a selective subpixel accuracy algorithm for the motion vector using a multiband approach. The selective subpixel accuracy reduces the computations produced by the conventional subpixel algorithm while maintaining the same accuracy. In addition, the method of overlapped block motion compensation (OBMC) is used to reduce blocking artifacts. Finally, the research extends the applications of the proposed VSBMC to the 3D video sequences. The experimental results obtained here have shown that VSBMC in the RDWT domain can be a powerful tool for video compression

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

Investigating low-bitrate, low-complexity H.264 region of interest techniques in error-prone environments

The H.264/AVC video coding standard leverages advanced compression methods to provide a significant increase in performance over previous CODECs in terms of picture quality, bitrate, and flexibility. The specification itself provides several profiles and levels that allow customization through the use of various advanced features. In addition to these features, several new video coding techniques have been developed since the standard\u27s inception. One such technique known as Region of Interest (RoI) coding has been in existence since before H.264\u27s formalization, and several means of implementing RoI coding in H.264 have been proposed. Region of Interest coding operates under the assumption that one or more regions of a sequence have higher priority than the rest of the video. One goal of RoI coding is to provide a decrease in bitrate without significant loss of perceptual quality, and this is particularly applicable to low complexity environments, if the proper implementation is used. Furthermore, RoI coding may allow for enhanced error resilience in the selected regions if desired, making RoI suitable for both low-bitrate and error-prone scenarios. The goal of this thesis project was to examine H.264 Region of Interest coding as it applies to such scenarios. A modified version of the H.264 JM Reference Software was created in which all non-Baseline profile features were removed. Six low-complexity RoI coding techniques, three targeting rate control and three targeting error resilience, were selected for implementation. Error and distortion modeling tools were created to enhance the quality of experimental data. Results were gathered by varying a range of coding parameters including frame size, target bitrate, and macroblock error rates. Methods were then examined based on their rate-distortion curves, ability to achieve target bitrates accurately, and per-region distortions where applicable

Shuttle/TDRSS Ku-band downlink study

Assessing the adequacy of the baseline signal design approach, developing performance specifications for the return link hardware, and performing detailed design and parameter optimization tasks was accomplished by completing five specific study tasks. The results of these tasks show that the basic signal structure design is sound and that the goals can be met. Constraints placed on return link hardware by this structure allow reasonable specifications to be written so that no extreme technical risk areas in equipment design are foreseen. A third channel can be added to the PM mode without seriously degrading the other services. The feasibility of using only a PM mode was shown to exist, however, this will require use of some digital TV transmission techniques. Each task and its results are summarized